JournalTOCs

IEEE Transactions on Wireless Communications
Journal Prestige (SJR): 1.246

Citation Impact (citeScore): 6
Number of Followers: 26

Hybrid journal (It can contain Open Access articles)
ISSN (Print) 1536-1276
Published by IEEE

[228 journals]

IEEE Transactions on Wireless Communications Publication Information
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  Pages: C2 - C2
  Abstract: null
  PubDate: FRI, 10 NOV 2023 09:17:33 -04
  Issue No: Vol. 22, No. 11 (2023)
IEEE Transactions on Wireless Communications Society Information
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  Pages: C3 - C3
  Abstract: null
  PubDate: FRI, 10 NOV 2023 09:17:33 -04
  Issue No: Vol. 22, No. 11 (2023)
Optimized Precoding for MU-MIMO With Fronthaul Quantization
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  Authors: Yasaman Khorsandmanesh;Emil Björnson;Joakim Jaldén;
  Pages: 7102 - 7115
  Abstract: One of the first widespread uses of multi-user multiple-input multiple-output (MU-MIMO) is in 5G networks, where each base station has an advanced antenna system (AAS) that is connected to the baseband unit (BBU) with a capacity-constrained fronthaul. In the AAS configuration, multiple passive antenna elements and radio units are integrated into a single box. This paper considers precoded downlink transmission over a single-cell MU-MIMO system. We study optimized linear precoding for AAS with a limited-capacity fronthaul, which requires the precoding matrix to be quantized. We propose a new precoding design that is aware of the fronthaul quantization and minimizes the mean-squared error at the receiver side. We compute the precoding matrix using a sphere decoding (SD) approach. We also propose a heuristic low-complexity approach to quantized precoding. This heuristic is computationally efficient enough for massive MIMO systems. The numerical results show that our proposed precoding significantly outperforms quantization-unaware precoding and other previous approaches in terms of the sum rate. The performance loss for our heuristic method compared to quantization-aware precoding is insignificant considering the complexity reduction, which makes the heuristic method feasible for real-time applications. We consider both perfect and imperfect channel state information (CSI).
  PubDate: THU, 02 MAR 2023 10:03:53 -04
  Issue No: Vol. 22, No. 11 (2023)
Channel Prediction With Time-Varying Doppler Spectrum in High-Mobility
Scenarios: A Polynomial Fourier Transform Based Approach and Field
Measurements
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  Authors: Xianling Wang;Yi Shi;Wenbo Xin;Tianci Wang;Guangli Yang;Zhiyuan Jiang;
  Pages: 7116 - 7129
  Abstract: Beamforming for multi-antenna wireless communication systems has been widely studied and applied in practice. However, its performance in high mobility scenarios deteriorates dramatically due to severe channel aging. This issue stimulates the research of channel prediction in high mobility scenarios. This paper studies the general high-mobility Doppler domain wireless channel characterization and reveals that: 1) the Doppler frequency of the cluster corresponding to the near scatterers varies approximately linearly over short periods; 2) the linear-fitting characteristics of autoregressive model leads to poor channel prediction performance; 3) the parameter estimation algorithms of the chirp model which encompasses the characteristics are particularly complex. Given the shortcomings of existing works, this paper adopts the short-time Fourier transform to pre-estimate the parameter range and leverages the polynomial Fourier transform to obtain the initial values of the parameters. The downhill simplex algorithm is used to search for the fine-grained values of the parameters. In addition, the orthogonal matching pursuit algorithm is utilized to reconstruct the sparse signal. Evaluation results under the COST2100 channel model and a channel measurement campaign indicate that the proposed scheme can enhance the channel prediction accuracy or reduce the computing overhead compared to the existing matrix completion and polynomial iteration method.
  PubDate: THU, 02 MAR 2023 10:03:53 -04
  Issue No: Vol. 22, No. 11 (2023)
A Hybrid Differential Detection Scheme for the Ultra-Wideband
Orientational Beamforming System
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  Authors: Jiangyan Han;Boon Poh Ng;Meng Hwa Er;
  Pages: 7130 - 7143
  Abstract: Differential space-time coding methods have been investigated for ultra-wideband communication systems to avoid channel estimation. However, their performance in the line-of-sight (LOS) environment is worse than the recently proposed orientational beamforming (OBF) system under low signal-to-noise ratios (SNRs). On the other hand, the OBF system cannot work well in the non-LOS (NLOS) environment. To address these issues, a hybrid differential detection (HDD) scheme is proposed in this paper, which combines the OBF system with a proposed differential OBF (DOBF) system. First, the DOBF system with a fully differential detection scheme is proposed, whose performance is almost the same as the differential space-time block coding method. However, its detection complexity only linearly increases with the number of transmitting antennas. Then, the HDD scheme is proposed, with its decision statistic being a combination of a modified OBF decision statistic and the DOBF decision statistic. In the NLOS environment, the HDD scheme is reduced to the DOBF system. In the LOS environment, a combination coefficient is obtained by mapping an SNR-related factor through a sigmoid function. The optimal parameters for the sigmoid function are determined through simulations, with which the proposed HDD scheme can achieve overall better performance than the OBF and DOBF systems.
  PubDate: WED, 01 MAR 2023 10:04:08 -04
  Issue No: Vol. 22, No. 11 (2023)
Content-Aware Transmission in UAV-Assisted Multicast Communication
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  Authors: Mingze Zhang;Yifeng Xiong;Soon Xin Ng;Mohammed El-Hajjar;
  Pages: 7144 - 7157
  Abstract: To alleviate the explosive growth of data traffic caused by the increased use of smart devices, new transmission techniques are needed to increase the utilization of limited bandwidth resources and for providing high transmission rates in future wireless networks. On the other hand, due to their flexibility and autonomy, unmanned aerial vehicles (UAVs) are considered as a potential candidate to support ubiquitous connectivity and operate as flying base stations, where the deployment of UAVs can affect the quality of experience (QoE) of users. Hence, in this paper, we employ UAVs as aerial base stations to transmit data to ground users (GUs) via air to ground (A2G) communication links, where we show how content awareness can help improve the data rate. Specifically, we design two content-sharing (CS) data transmission schemes to improve the average data rate of the GUs. Additionally, two UAV deployment strategies, namely the fixed-point deployment scheme and traverse-search deployment scheme, are proposed based on the proposed CS transmission schemes. The simulation results demonstrate that our proposed data transmission schemes combined with their proposed deployment schemes outperform the traditional transmission scheme by 26 bits/s/Hz and 51 bits/s/Hz, respectively.
  PubDate: WED, 01 MAR 2023 10:04:08 -04
  Issue No: Vol. 22, No. 11 (2023)
Edge-Cloud Offloading: Knapsack Potential Game in 5G Multi-Access Edge
Computing
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  Authors: Cheng-Ying Hsieh;Yi Ren;Jyh-Cheng Chen;
  Pages: 7158 - 7171
  Abstract: In 5G, multi-access edge computing enables the applications to be offloaded to near-end edge servers for faster response. According to the 3GPP standards, users in 5G are separated into many types, e.g., vehicles, AR/VR, IoT devices, etc. Specifically, the high-priority traffic can preempt edge resources to guarantee the service quality. However, even if a traffic is transmitted with low priority, its latency requirement in 5G is much lower than that in 4G. Too strict latency requirement and priority-based service make resource configuration difficult on the edge side. Therefore, we propose the edge-cloud offloading mechanism, in which each edge server can offload tasks to back-end cloud server to ensure service quality of both high- and low-priority traffic. In this paper, we establish a priority-based queuing system to model the edge-cloud offloading behaviors. Based on the formulation of our system model, we propose Knapsack Potential Game (KPG) to derive an optimal offloading ratio for each edge server to balance the cost-effectiveness of the overall system. We demonstrate that KPG has low computational complexity and outperforms two baseline algorithms. The results indicate that KPG’s performance is optimal and provides a theoretical guideline to operators while designing their edge-cloud offloading strategies without large-scale implementation.
  PubDate: WED, 01 MAR 2023 10:04:08 -04
  Issue No: Vol. 22, No. 11 (2023)
Trading Off Delay and Energy Saving Through Advanced Sleep Modes in 5G
RANs
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  Authors: Daniela Renga;Zunera Umar;Michela Meo;
  Pages: 7172 - 7184
  Abstract: While designed for being energy efficient, the deployment of 5G networks will further increase Radio Access Networks (RANs) energy consumption with the twofold effect to raise sustainability issues and increase operational costs for Mobile Network Operators (MNOs). However, the energy waste occurring during low traffic periods can be mitigated through Advanced Sleep Modes (ASMs) that make the BSs enter into progressively deeper and less consuming sleep modes. Deep sleep modes, unfortunately, have longer reactivation times, and may jeopardize service quality. In this paper, focusing on 5G latency requirements in low traffic periods, we propose a framework to dynamically adapt the ASM configuration settings to the actual traffic load so as to meet a desired constraint on the average BS reactivation delay.
  PubDate: WED, 01 MAR 2023 10:04:08 -04
  Issue No: Vol. 22, No. 11 (2023)
Energy Consumption Minimization in Secure Multi-Antenna UAV-Assisted MEC
Networks With Channel Uncertainty
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  Authors: Weihao Mao;Ke Xiong;Yang Lu;Pingyi Fan;Zhiguo Ding;
  Pages: 7185 - 7200
  Abstract: This paper investigates the robust and secure task transmission and computation scheme in multi-antenna unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) networks, where the UAV is dual-function, i.e., aerial MEC and aerial relay. The channel uncertainty is considered during information offloading and downloading. An energy consumption minimization problem is formulated under some constraints including users’ quality of service and information security requirements and the UAV’s trajectory’s causality, by jointly optimizing the CPU frequency, the offloading time, the beamforming vectors, the artificial noise and the trajectory of the UAV, as well as the CPU frequency, the offloading time and the transmit power of each user. To solve the non-convex problem, a reformulated problem is first derived by a series of convex reformation methods, i.e., semi-definite relaxation, S-Procedure and first-order approximation, and then, solved by a proposed successive convex approximation (SCA)-based algorithm. The convergence performance and computational complexity of the proposed algorithm are analyzed. Numerical results demonstrate that the proposed scheme outperforms existing benchmark schemes. Besides, the proposed SCA-based algorithm is superior to traditional alternative optimization-based algorithm.
  PubDate: THU, 02 MAR 2023 10:03:53 -04
  Issue No: Vol. 22, No. 11 (2023)
Uncertainty Injection: A Deep Learning Method for Robust Optimization
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  Authors: Wei Cui;Wei Yu;
  Pages: 7201 - 7213
  Abstract: This paper proposes a paradigm of uncertainty injection for training deep learning model to solve robust optimization problems. The majority of existing studies on deep learning focus on the model learning capability, while assuming the quality and accuracy of the inputs data can be guaranteed. However, in realistic applications of deep learning for solving optimization problems, the accuracy of inputs, which are the problem parameters in this case, plays a large role. This is because, in many situations, it is often costly or sometime impossible to obtain the problem parameters accurately, and correspondingly, it is highly desirable to develop learning algorithms that can account for the uncertainties in the input and produce solutions that are robust against these uncertainties. This paper presents a novel uncertainty injection scheme for training machine learning models that are capable of implicitly accounting for the uncertainties and producing statistically robust solutions. We further identify the wireless communications as an application field where uncertainties are prevalent in problem parameters such as the channel coefficients. We show the effectiveness of the proposed training scheme in two applications: the robust power loading for multiuser multiple-input-multiple-output (MIMO) downlink transmissions; and the robust power control for device-to-device (D2D) networks.
  PubDate: WED, 15 MAR 2023 10:01:49 -04
  Issue No: Vol. 22, No. 11 (2023)
The Extremal GDoF Gain of Optimal Versus Binary Power Control in K User
Interference Networks is Θ (√K)
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  Authors: Yao-Chia Chan;Pouya Pezeshkpour;Chunhua Geng;Syed A. Jafar;
  Pages: 7214 - 7226
  Abstract: Using ideas from Generalized Degrees of Freedom (GDoF) analyses and extremal network theory, this work studies the extremal gain of optimal power control over binary (on/off) power control, especially in large interference networks, in search of new theoretical insights. Whereas numerical studies have already established that in most practical settings binary power control is close to optimal, the extremal analysis shows not only that there exist settings where the gain from optimal power control can be quite significant, but also bounds the extremal values of such gains from a GDoF perspective. As its main contribution, this work explicitly characterizes the extremal GDoF gain of optimal over binary power control as $\Theta (\sqrt {K})$ for all $K$ . In particular, the extremal gain is bounded between $\lfloor \sqrt {K}\rfloor $ and $2.5\sqrt {K}$ for every $K$ . For $K=2,3,4,5,6$ users, the precise extremal gain is found to be 1, 3/2, 2, 9/4 and 41/16, respectively. Networks shown to achieve the extremal gain may be interpreted as multi-tier heterogeneous networks. It is worthwhile to note that because of their focus on asymptotic analysis, the sharp characterizations of extremal gains are valuable primarily from a theoretical perspective, and not as contradictions to the conventional wisdom that binary power control is generally close to optimal in practical, non-asymptotic settings.
  PubDate: THU, 02 MAR 2023 10:03:53 -04
  Issue No: Vol. 22, No. 11 (2023)
Secrecy-Capacity Bounds for Visible Light Communications With
Signal-Dependent Noise
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  Authors: Jin-Yuan Wang;Peng-Fei Yu;Xian-Tao Fu;Jun-Bo Wang;Min Lin;Julian Cheng;Mohamed-Slim Alouini;
  Pages: 7227 - 7242
  Abstract: In physical-layer security, secrecy capacity is an important performance metric. This work aims to determine the secrecy capacity for an indoor visible light communication system consisting of a transmitter, a legitimate receiver and an eavesdropping receiver. In such a system, both signal-independent noise and signal-dependent noise are considered. Under nonnegativity and average optical intensity constraints, lower and upper bounds on secrecy capacity are derived by the variational method, the dual expression of the secrecy capacity, and the concept of “the optimal input distribution that escapes to infinity”. By an asymptotic analysis at large optical intensity, there is a small gap between the asymptotic upper and lower bounds. Then, by adding a peak optical intensity constraint, we further analyze the exact and asymptotic secrecy-capacity bounds. For practical considerations, the effects of imperfect channel state information, multi-photodiode eavesdropper, and artificial noise on secrecy performance are also discussed. Finally, the derived secrecy-capacity bounds are verified by numerical results.
  PubDate: FRI, 03 MAR 2023 10:01:47 -04
  Issue No: Vol. 22, No. 11 (2023)
Cooperative Beamforming for RIS-Aided Cell-Free Massive MIMO Networks
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  Authors: Xinying Ma;Deyou Zhang;Ming Xiao;Chongwen Huang;Zhi Chen;
  Pages: 7243 - 7258
  Abstract: The combination of cell-free massive multiple-input multiple-output (CF-mMIMO) and reconfigurable intelligent surface (RIS) is envisioned as a promising paradigm to improve network capacity and enhance coverage capability. However, to reap full benefits of RIS-aided CF-mMIMO, the main challenge is to efficiently design cooperative beamforming (CBF) at base stations (BSs), RISs, and users. Firstly, we investigate the fractional programing to convert the weighted sum-rate (WSR) maximization problem into a tractable optimization problem. Then, the alternating optimization framework is employed to decompose the transformed problem into a sequence of subproblems, i.e., hybrid BF (HBF) at BSs, passive BF at RISs, and combining at users. In particular, the alternating direction method of multipliers algorithm is utilized to solve the HBF subproblem at BSs. Concretely, the analog BF design with unit-modulus constraints is solved by the manifold optimization (MO) while we obtain a closed-form solution to the digital BF design that is essentially a convex least-square problem. Additionally, the passive BF at RISs and the analog combining at users are designed by primal-dual subgradient and MO methods. Moreover, considering heavy communication costs in conventional CF-mMIMO systems, we propose a partially-connected CF-mMIMO (P-CF-mMIMO) framework to decrease the number of connections among BSs and users. To better compromise WSR performance and network costs, we formulate the BS selection problem in the P-CF-mMIMO system as a binary integer quadratic programming (BIQP) problem, and develop a relaxed linear approximation algorithm to handle this BIQP problem. Finally, numerical results demonstrate superiorities of our proposed algorithms over baseline counterparts.
  PubDate: FRI, 03 MAR 2023 10:01:47 -04
  Issue No: Vol. 22, No. 11 (2023)
Base Station Dataset-Assisted Broadband Over-the-Air Aggregation for
Communication-Efficient Federated Learning
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  Authors: Jun-Pyo Hong;Sangjun Park;Wan Choi;
  Pages: 7259 - 7272
  Abstract: This paper proposes an over-the-air aggregation framework for federated learning (FL) in broadband wireless networks where not only edge devices but also a base station (BS) has its own local dataset. The proposed framework leverages the BS dataset to improve communication efficiency of FL by reducing the number of channel uses required for the model convergence as well as avoiding the signaling overhead incurred by power scale coordination among edge devices. We analyze the convergence to a stationary point without convexity assumption on the objective function. The analysis result reveals that the utilization of BS dataset improves the convergence rate and the update distortion caused by the limited power budget is a crucial factor hindering the model convergence. To facilitate the convergence, we develop an optimized power control method by solving the distortion minimization problem without assumptions on power scale coordination and global CSI at BS. Our simulation results validate that BS dataset is beneficial to reducing the number of channel uses for the model convergence and the developed power control method outperforms the conventional method in terms of both convergence rate and converged test accuracy. Furthermore, we identify some scenarios where the compression of local update can be helpful to reduce communication resources for model training.
  PubDate: FRI, 03 MAR 2023 10:02:01 -04
  Issue No: Vol. 22, No. 11 (2023)
Toward Protecting 5G Sidelink Scheduling in C-V2X Against Intelligent DoS
Attacks
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  Authors: Geoff Twardokus;Hanif Rahbari;
  Pages: 7273 - 7286
  Abstract: 5G Cellular Vehicle-to-Everything (5G C-V2X) is emerging as the globally dominant connected vehicle technology. One critical application of 5G C-V2X is the direct exchange of safety-critical messages between vehicles to prevent crashes and correspondingly reduce roadway injuries and fatalities. While current C-V2X security protocols concern only message payloads, we expose vulnerabilities in the physical-layer attributes and decentralized MAC-layer scheduling algorithm of 5G C-V2X by developing two stealthy denial-of-service (DoS) attacks to exploit them. These low-duty-cycle attacks dramatically degrade C-V2X availability, increasing the likelihood of prolonged travel times and even vehicle crashes. We further develop detection and mitigation techniques for each attack, in part by exploiting new C-V2X features of 3GPP Rel-17. We experimentally evaluate our attacks and countermeasures in a hardware testbed composed of USRPs and state-of-the-art C-V2X kits as well as through extensive network and roadway simulations, showing that within seconds of initiation our attacks can reduce a target’s packet delivery ratio by 90% or that of the C-V2X channel to under 25%. We further evaluate our machine-learning detection and low-cost mitigation techniques, showing the latter completely thwart one attack and reduce the impact of the other by 80%, providing insight towards developing a more robust 5G C-V2X.
  PubDate: FRI, 03 MAR 2023 10:02:01 -04
  Issue No: Vol. 22, No. 11 (2023)
Community Detection and Attention-Weighted Federated Learning Based
Proactive Edge Caching for D2D-Assisted Wireless Networks
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  Authors: Dongyang Li;Haixia Zhang;Tiantian Li;Hui Ding;Dongfeng Yuan;
  Pages: 7287 - 7303
  Abstract: This work investigates proactive edge caching for D2D-assisted wireless networks, where user equipments (UEs) can be selected as caching nodes to assist content delivery. The objective of this work is to achieve a trade-off between the cost for providing caching services and the content transmission latency. Doing so, there are two challenges: 1) Which UEs can be selected as caching nodes; 2) How to place contents on these selected UEs without user’s privacy disclosure. To address these, a novel community detection and attention-weighted federated learning based proactive edge caching (CAFLPC) strategy is proposed. In the strategy, we first group UEs into different communities based on both the mobility and social properties of UEs, and then select important users (IUs) as caching nodes for each community by considering the social importance of UEs. To determine how to place the popular contents in these selected IUs, an attention-weighted federated learning (AWFL) based content popularity prediction framework is proposed. It integrates the attention-weighted federated learning with Bidirectional Long Short Term Memory Network (AWFL_BiLSTM) to achieve a higher content popularity prediction accuracy while protecting user’s privacy. Considering the imbalance of UEs’ active levels and local computing capacities, an attention-weighted aggregation mechanism is proposed to improve the training efficiency and prediction accuracy. Simulations results show that the proposed CAFLPC strategy outperforms the compared existing caching strategies at about 2.2%-35.1% in terms of the transmission latency reduced by per unit cost.
  PubDate: MON, 06 MAR 2023 10:03:00 -04
  Issue No: Vol. 22, No. 11 (2023)
System-Level Analysis of Energy and Performance Trade-Offs in mmWave 5G NR
Systems
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  Authors: Darya Ostrikova;Vitalii Aleksandrovich Beschastnyi;Dmitri Moltchanov;Yuliya Gaidamaka;Yevgeni Koucheryavy;Konstantin E. Samouylov;
  Pages: 7304 - 7318
  Abstract: Energy efficiency and service reliability are two critical requirements for 5G New Radio cellular access. To address the latter, 3GPP has proposed multiconnectivity operation allowing user equipment (UE) to maintain active links to more than a single base station. However, the use of this technique compromises the energy efficiency of UE. In this paper, we develop a mathematical model capturing key energy and performance indicators as a function of system and environmental conditions. Then, we apply it to investigate the trade-offs between user performance and energy efficiency as well as the effect of scaling of discontinuous reception (DRX) timers. For a considered set of system parameters, our results reveal that for low micromobility speed $\leq 0.1^{\circ}$ /s and blockers density, $\leq 0.1$ bl./ $\text{m}^{2}$ two simultaneously supported links with minimal DRX timers lead to optimal performance. For higher blockers density more than two links are needed to optimize energy efficiency while for high micromobility speed multiconnectivity does not allow to improve energy efficiency at all. Thus, the optimal degree of multiconnectivity and DRX timer scaling coefficients depend on the environmental characteristics including both micromobility speed and density of blockers and need to be dynamically updated during UE operation.
  PubDate: TUE, 07 MAR 2023 10:18:34 -04
  Issue No: Vol. 22, No. 11 (2023)
Multi-Domain Resource Multiplexing Based Secure Transmission for
Satellite-Assisted IoT: AO-SCA Approach
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  Authors: Zhisheng Yin;Nan Cheng;Yilong Hui;Wei Wang;Lian Zhao;Khalid Aldubaikhy;Abdullah Alqasir;
  Pages: 7319 - 7330
  Abstract: Due to the wireless broadcasting and broad coverage in satellite-supported Internet of things (IoT) networks, the IoT nodes are susceptible to eavesdropping threats. Considering the distance difference between satellite and nearby destinations is negligible, the main and wiretapping channels between satellite and IoT node are similar, it poses great challenges to reach physical layer security in satellite-assisted IoT networks. In this paper, to guarantee secure transmissions for satellite-assisted IoT downlink communications, the multi-domain resource multiplexing based secure approach is proposed. Particularly, the self-induced co-channel interference between adjacent nodes is leveraged to increase the difference of signal transmission quality over both main and wiretapping channels. By comprehensively optimizing multi-domain resources, i.e., frequency, power, and spatial domains, secure transmissions from satellite to IoT nodes are reached. Specifically, the problem to maximize the sum secrecy rate of IoT nodes is formulated with a constraint of common communication rate of IoT nodes. To solve this non-convex problem, an alternating optimization (AO) algorithm with two inner successive convex approximation (SCA) algorithms are executed to solve the power allocation, spectral multiplexing, and precoding. In addition, simulation results are carried out to evaluate the secrecy rate performance and verify the efficiency of our proposed approach.
  PubDate: MON, 06 MAR 2023 10:03:00 -04
  Issue No: Vol. 22, No. 11 (2023)
Resource Optimization for Task Offloading With Real-Time Location
Prediction in Pedestrian-Vehicle Interaction Scenarios
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  Authors: Dawen Zheng;Lusheng Wang;Caihong Kai;Min Peng;
  Pages: 7331 - 7344
  Abstract: With the development of autonomous driving, task offloading of Internet of vehicles has become a hot research issue. In pedestrian-vehicle interaction scenarios, characteristics of tasks are constantly changing due to the influence of pedestrians and road conditions. Real-time offloading optimization and signaling are time-consuming, which may not meet the low delay requirement of task offloading. Therefore, this paper proposes a location prediction-based resource optimization scheme for task offloading in these scenarios. Firstly, the locations of pedestrians are predicted by the social force model based on their movement rules, and the locations of vehicles are predicted by the car-following model on the basis of ensuring pedestrian safety. The characteristics of tasks are obtained based on the predicted locations of vehicles. Then a neural network trained beforehand based on deep Q-learning is used to obtain a task offloading strategy. Since the tasks are obtained by prediction in advance, this strategy decision can be processed before vehicles arriving the predicted locations, which saves the time consumption of optimization and signaling. Besides, simulation results show that the proposed scheme still guarantees an acceptably low task offloading delay compared with the other methods, especially in congested areas.
  PubDate: MON, 06 MAR 2023 10:03:00 -04
  Issue No: Vol. 22, No. 11 (2023)
On Integrated Sensing and Communication Waveforms With Tunable PAPR
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  Authors: Ahmad Bazzi;Marwa Chafii;
  Pages: 7345 - 7360
  Abstract: We present a novel approach to the problem of dual-functional radar and communication (DFRC) waveform design with adjustable peak-to-average power ratio (PAPR), while minimizing the multi-user communication interference and maintaining a similarity constraint towards a radar chirp signal. The approach is applicable to generic radar chirp signals and for different constellation sizes. We formulate the waveform design problem as a non convex optimization problem. As a solution, we adopt the alternating direction method of multipliers (ADMM), hence iterating towards a stable waveform for both radar and communication purposes. Additionally, we prove convergence of the proposed method and analyze its computational complexity. Moreover, we offer an extended version of the method to cope with imperfect channel state information (CSI). Finally, we demonstrate its superior performance through simulations, in comparison to state-of-the-art radar-communication waveform designs.
  PubDate: MON, 06 MAR 2023 10:03:00 -04
  Issue No: Vol. 22, No. 11 (2023)
DFT-Spread Orthogonal Time Frequency Space System With Superimposed Pilots
for Terahertz Integrated Sensing and Communication
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  Authors: Yongzhi Wu;Chong Han;Zhi Chen;
  Pages: 7361 - 7376
  Abstract: Terahertz (THz) integrated sensing and communication (ISAC) is a promising interdisciplinary technology that realizes simultaneously transmitting Terabit-per-second (Tbps) and millimeter-level accurate environment or human activity sensing. However, both communication performance and sensing accuracy are influenced by the Doppler effects, which are especially severe in the THz band. Moreover, peak-to-average power ratio (PAPR) degrades the THz power amplifier (PA) efficiency. In this paper, a discrete Fourier transform spread orthogonal time frequency space (DFT-s-OTFS) system with superimposed pilots is proposed to improve the robustness to Doppler effects and reduce PAPR for THz ISAC. Then, a two-phase sensing parameter estimation algorithm is developed to integrate sensing functionality into the DFT-s-OTFS waveform. Meanwhile, a low-complexity iterative channel estimation and data detection method with a conjugate gradient based equalizer is proposed to recover the data symbols of DFT-s-OTFS. The proposed DFT-s-OTFS waveform can improve the PA efficiency by 10% on average compared to OTFS. Simulation results demonstrate that the proposed two-phase sensing estimation algorithm for THz DFT-s-OTFS systems is able to realize millimeter-level range estimation accuracy and decimeter-per-second-level velocity estimation accuracy. Moreover, the effectiveness of the iterative method for data detection aided by superimposed pilots in DFT-s-OTFS systems is validated by the simulations and the bit error rate performance is not degraded by the Doppler effects.
  PubDate: MON, 06 MAR 2023 10:03:00 -04
  Issue No: Vol. 22, No. 11 (2023)
Bias Reduced Semidefinite Relaxation Method for 3-D Moving Object
Localization Using AOA
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  Authors: Gang Wang;Peng Xiang;K. C. Ho;
  Pages: 7377 - 7392
  Abstract: This paper addresses the localization of a constant velocity moving object in 3-D using angle-of-arrival (AOA) measurements. Compared with the maximum-likelihood estimator, pseudo-linear approach for AOA localization has a large amount of bias resulting from the model transformation to simplify the solution finding. This work aims at reducing the bias and developing bias-reduced semidefinite relaxation (SDR) methods for estimating the initial position and velocity of the object, in a batch or sequential manner. This is accomplished by first formulating a bias reduced constrained weighted least squares (BR-CWLS) problem from the transformed measurements, through introducing an auxiliary variable and adding a quadratic constraint. Such an intractable non-convex problem is tackled next by applying the SDR technique and relaxing it into a convex semidefinite program (SDP), which is shown to be capable of reaching the solution of the original BR-CWLS problem. For sequential estimation, we formulate a different BR-CWLS problem and utilize SDR for obtaining a sequential estimation method that updates the initial position and velocity estimates at each time step. We conduct the mean square error (MSE) and bias analyses for both estimation methods to assess their expected performance. Simulation results verify the ability of bias reduction and the good performance of the proposed methods.
  PubDate: MON, 06 MAR 2023 10:03:00 -04
  Issue No: Vol. 22, No. 11 (2023)
Viewing Channel as Sequence Rather Than Image: A 2-D Seq2Seq Approach for
Efficient MIMO-OFDM CSI Feedback
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  Authors: Zirui Chen;Zhaoyang Zhang;Zhuoran Xiao;Zhaohui Yang;Kai-Kit Wong;
  Pages: 7393 - 7407
  Abstract: In this paper, we aim to design an effective learning-based channel state information (CSI) feedback scheme for the multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems from a physics-inspired perspective. We first argue that the CSI matrix of a MIMO-OFDM system is physically closer to a two-dimensional (2-D) sequence rather than an image due to its apparent unsmoothness, non-scalability, and translational variance within both the spatial and frequency domains. On this basis, we introduce a 2-D long short-term memory (LSTM) neural network to represent the CSI and propose a 2-D sequence-to-sequence (Seq2Seq) model for CSI compression and reconstruction. Specifically, one two-layer 2-D LSTM is used for CSI feature extraction, and the other is used for CSI representation and reconstruction. The proposed scheme can not only fully utilize the unique 2-D characteristics of CSI but also preserve the index information and unsmooth features of the CSI matrix compared with current convolutional neural network (CNN) based schemes. We show that the computational complexity of the proposed scheme is linear in the number of transmit antennas and subcarriers. Its key performances, like reconstruction accuracy, convergence speed, generalization ability after short-term training, and robustness to lossy feedback, are comprehensively compared with existing popular convolutional networks. Experimental results show that our scheme can bring up to nearly 7 dB gain in reconstruction accuracy under the same overhead and reduce feedback overhead by up to 75% under the same accuracy compared with the conventional CNN-based approaches.
  PubDate: MON, 06 MAR 2023 10:03:00 -04
  Issue No: Vol. 22, No. 11 (2023)
Vehicular Connectivity on Complex Trajectories: Roadway-Geometry Aware
ISAC Beam-Tracking
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  Authors: Xiao Meng;Fan Liu;Christos Masouros;Weijie Yuan;Qixun Zhang;Zhiyong Feng;
  Pages: 7408 - 7423
  Abstract: In this paper, we propose sensing-assisted beamforming designs for vehicles on arbitrarily shaped roads by relying on integrated sensing and communication (ISAC) signalling. Specifically, we aim to address the limitations of conventional ISAC beam-tracking schemes that do not apply to complex road geometries. To improve the tracking accuracy and communication quality of service (QoS) in vehicle to infrastructure (V2I) networks, it is essential to model the complicated roadway geometry. To that end, we impose the curvilinear coordinate system (CCS) in an interacting multiple model extended Kalman filter (IMM-EKF) framework. By doing so, both the position and the motion of the vehicle on a complicated road can be explicitly modeled and precisely tracked attributing to the benefits from the CCS. Furthermore, an optimization problem is formulated to maximize the array gain by dynamically adjusting the array size and thereby controlling the beamwidth, which takes the performance loss caused by beam misalignment into account. Numerical simulations demonstrate that the roadway geometry-aware ISAC beamforming approach outperforms the communication-only-based and ISAC kinematic-only-based technique in tracking performance. Moreover, the effectiveness of the dynamic beamwidth design is also verified by our numerical results.
  PubDate: MON, 06 MAR 2023 10:03:00 -04
  Issue No: Vol. 22, No. 11 (2023)
Analysis of Coded Slotted ALOHA With Energy Harvesting Nodes for Perfect
and Imperfect Packet Recovery Scenarios
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  Authors: Javad Haghighat;Tolga M. Duman;
  Pages: 7424 - 7437
  Abstract: We analyze the performance of Coded Slotted ALOHA (CSA) protocols in scenarios where users are equipped with limited batteries that are recharged through Energy Harvesting (EH). First, we assume a Perfect Packet Recovery Scenario (PPRS) for which the received packets are decoded with no errors when there is no interference. We introduce Battery Outage Probability (BOP) as an extra performance metric; and, we derive the optimal EH-CSA transmission policies, which offer the maximum attainable traffic load while maintaining an asymptotically negligible Packet Loss Ratio (PLR), under specific rate and BOP constraints. We extend our study to Imperfect Packet Recovery Scenario (IPRS) where impairments at the physical layer, including channel estimation and channel decoding errors, will distort messages being passed through the iterative Successive Interference Cancellation (SIC) process. The distorted messages being passed through the SIC process potentially lead to error propagation. In order to track the error propagation process, we define the concept of Accumulated Noise plus Interference Power (ANIP), and analytically track the evolution of its probability distribution. We employ our results to evaluate the bit error rates for different transmission policies for the case of IPRS. We also demonstrate the advantages of the optimal transmission policies through numerical examples for both PPRS and IPRS. Our results show that the optimal EH-CSA policies outperform the policies optimized for standard CSA without EH considerations, and the schemes that are optimal for PPRS are not necessarily optimal for the IPRS case. Furthermore, the EH-CSA optimal policies strictly outperform standard CRDSA when the system is required to support higher traffic loads.
  PubDate: MON, 06 MAR 2023 10:03:00 -04
  Issue No: Vol. 22, No. 11 (2023)
Performance Analysis of RIS-Assisted Large-Scale Wireless Networks Using
Stochastic Geometry
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  Authors: Tianxiong Wang;Gaojie Chen;Mihai-Alin Badiu;Justin P. Coon;
  Pages: 7438 - 7451
  Abstract: In this paper, we investigate the performance of a reconfigurable intelligent surface (RIS) assisted large-scale network by characterizing the coverage probability and the average achievable rate using stochastic geometry. Considering the spatial correlation between transmitters (TXs) and RISs, their locations are jointly modelled by a Gauss-Poisson process (GPP). Two association strategies, i.e., nearest association and fixed association, are both discussed. For the RIS-aided transmission, the signal power distribution with a direct link is approximated by a gamma random variable using a moment matching method, and the Laplace transform of the aggregate interference power is derived in closed form. Based on these expressions, we analyze the channel hardening effect in the RIS-assisted transmission, the coverage probability, and the average achievable rate of the typical user. We derive the coverage probability expressions for the fixed association strategy and the nearest association strategy in an interference-limited scenario in closed form. Numerical results are provided to validate the analysis and illustrate the effectiveness of RIS-assisted transmission with passive beamforming in improving the system performance. Furthermore, it is also unveiled that the system performance is independent of the density of TXs with the nearest association strategy in the interference-limited scenario.
  PubDate: WED, 08 MAR 2023 10:02:48 -04
  Issue No: Vol. 22, No. 11 (2023)
Height-Fixed UAV Enabled Energy-Efficient Data Collection in RIS-Aided
Wireless Sensor Networks
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  Authors: Jianghui Liu;Hongtao Zhang;
  Pages: 7452 - 7463
  Abstract: Wireless sensor nodes (SNs) are usually energy-limited, a low energy efficiency of SN may be caused by the occlusion of transmission link in the unmanned aerial vehicle (UAV)-enabled data collection. Reconfigurable intelligent surfaces (RISs) provide potential communication opportunities by reflecting and enhancing the signal, thus increasing SNs’ energy efficiency. This paper investigates energy-efficient data collection aided by $N$ RIS elements, where a height-fixed and flight time-constrained UAV is considered as the collector, a tractable air-to-ground channel model is established considering the cascaded RIS channel and time-varying trajectory of UAV, and maximum energy consumption of SNs is minimized. Specifically, coupling two intractable fourth-order moment variables, the air-to-ground channel model is processed into an easily computable expression through the Gamma function. Furthermore, based on the proposed two RIS deployment schemes, maximum energy consumption of SNs minimization problem is formulated as a non-convex mixed-integer nonlinear programming (NMNP) problem with optimization of UAV trajectory and SNs wake-up schedules. Additionally, through the slack technique and Taylor expansion, the NMNP problem is decoupled as linear programming and quadratically constrained quadratic programming, and an effective algorithm is designed to obtain the sub-optimal solution. Simulations prove the validity of our algorithm and RIS deployment insights are given for SNs energy saving.
  PubDate: MON, 17 APR 2023 10:06:02 -04
  Issue No: Vol. 22, No. 11 (2023)
Beamforming Vector Design and Device Selection in Over-the-Air Federated
Learning
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  Authors: Minsik Kim;A. Lee Swindlehurst;Daeyoung Park;
  Pages: 7464 - 7477
  Abstract: In this paper, we consider a beamforming vector design and device selection problem in over-the-air computation (AirComp) for federated learning. Since the learning performance improves as more devices participate in the federated learning aggregation, we formulate a beamforming vector optimization problem that maximizes the number of selected devices under a given target aggregation mean-squared error. This AirComp uplink beamforming problem with device selection is shown to have the same form as the downlink multicast beamforming problem with user selection, which establishes the AirComp-multicasting duality. We design a low-complexity algorithm based on the projected subgradient method that is orders of magnitude faster than conventional semidefinite relaxation-based algorithms and faster than local model training on the devices, which makes it possible to implement the proposed wireless federated learning in real time. Numerical results show that the proposed algorithm provides significant multiple antenna beamforming gains and achieves the performance of the ideal federated learning system with no aggregation errors.
  PubDate: WED, 08 MAR 2023 10:02:48 -04
  Issue No: Vol. 22, No. 11 (2023)
Joint Transceiving and Reflecting Design for Intelligent Reflecting
Surface Aided Wireless Power Transfer
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  Authors: Qingdong Yue;Jie Hu;Kun Yang;Qin Yu;
  Pages: 7478 - 7491
  Abstract: In an intelligent reflecting surface (IRS) aided wireless power transfer (WPT) system, a practical architecture of an energy receiver (ER) is proposed, which includes multiple receive antennas, an analog energy combiner, a power splitter and multiple energy harvesters. In order to maximise the output direct-current (DC) power, the transmit beamformer of the transmitter, the passive beamformer of the IRS, the energy combiner, and the power splitter of the ER are jointly optimised. The optimisation problem is equivalently divided into two sub-problems, which independently maximises the input RF power and the output DC power of the energy harvesters, respectively. A successive linear approximation (SLA) based algorithm with a low complexity is proposed to maximise the input RF power to the energy harvesters, which converges to a Karush-Kuhn-Tucker (KKT) point. We also propose an improved greedy randomized adaptive search procedure (I-GRASP) based algorithm having better performance to maximise the input RF power. Furthermore, the optimal power splitter for maximising the output DC power of the energy harvesters is derived in closed-form. The numerical results are provided to verify the performance advantage of the IRS-aided WPT and to demonstrate that conceiving the optimised energy combiner achieves better WPT performance than the deterministic counterpart.
  PubDate: WED, 08 MAR 2023 10:02:48 -04
  Issue No: Vol. 22, No. 11 (2023)
Outage Analysis of UAV-Aided Networks With Underlaid Ambient Backscatter
Communications
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  Authors: Xu Jiang;Min Sheng;Nan Zhao;Junyu Liu;Dusit Niyato;F. Richard Yu;
  Pages: 7492 - 7505
  Abstract: Ambient backscatter communication is an energy efficient technique for massive Internet of Things (IoT). Combining with flexibly deployed unmanned aerial vehicles (UAVs), the UAV-aided ambient backscatter communication can establish wireless links for isolated IoT nodes efficiently. In this paper, we investigate the outage performance of the UAV-aided air-ground network with underlaid ambient backscatter communications, where the emitted signals from the air-ground link are leveraged as radio frequency (RF) carrier for ambient backscattering. The air-ground channel is modeled as a probabilistic line-of-sight (LoS) channel with Nakagami- $m$ fading. Then, the ground communication is modeled as a non-line-of-sight (NLoS) channel with Rayleigh fading. For the downlink, we derive the expressions of the outage probabilities of the backscatter link and the air-ground link. In addition, the asymptotic cases of infinite transmit power and infinite fading parameter are analyzed. For the uplink, the outage probabilities of the backscatter link and air-ground are analyzed, with the cases of infinite transmit power and fading parameter discussed. Simulation results show that the analytical results match well with the Monte Carlo results, which verifies the effectiveness of the proposed scheme.
  PubDate: THU, 09 MAR 2023 10:02:01 -04
  Issue No: Vol. 22, No. 11 (2023)
Robust Transmission Design for RIS-Assisted Secure Multiuser Communication
Systems in the Presence of Hardware Impairments
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  Authors: Zhangjie Peng;Ruisong Weng;Cunhua Pan;Gui Zhou;Marco Di Renzo;A. Lee Swindlehurst;
  Pages: 7506 - 7521
  Abstract: This paper investigates reconfigurable intelligent surface (RIS)-assisted secure multiuser communication systems in the presence of hardware impairments (HIs) at the RIS and the transceivers. We jointly optimize the beamforming vectors at the base station (BS) and the phase shifts of the reflecting elements at the RIS so as to maximize the weighted minimum approximate ergodic secrecy rate (WMAESR), subject to the transmission power constraints at the BS and unit-modulus constraints at the RIS. To solve the formulated optimization problem, we first decouple it into two tractable subproblems and then use the block coordinate descent (BCD) method to alternately optimize the subproblems. Two different methods are proposed to solve the two obtained subproblems. The first method transforms each subproblem into a second order cone programming (SOCP) problem by invoking the penalty convex–concave procedure (CCP) method and the closed-form fractional programming (FP) criterion, and then directly solves them by using CVX. The second method leverages the minorization-maximization (MM) algorithm. Specifically, we first derive a concave approximation function, which is a lower bound of the original objective function, and then the two subproblems are transformed into two simple surrogate problems that admit closed-form solutions. Simulation results verify the performance gains of the proposed robust transmission methods over existing non-robust designs. In addition, the MM algorithm is shown to have much lower complexity than the SOCP-based algorithm.
  PubDate: THU, 09 MAR 2023 10:02:01 -04
  Issue No: Vol. 22, No. 11 (2023)
Energy Efficient Beamforming for Millimeter-Wave Massive MIMO Systems
Under User-Wise Asymmetric Uplink–Downlink Traffic
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  Authors: Ke Xu;Fu-Chun Zheng;Hongguang Xu;Xu Zhu;Xiaogang Xiong;
  Pages: 7522 - 7536
  Abstract: In this paper, a beamforming scheme that aims to support user-wise asymmetric uplink-downlink (UL-DL) traffic in a more energy-efficient manner is proposed for time-division duplex (TDD) millimeter-wave massive multiple-input-multiple-output (MIMO) systems. Assuming that proper data links have been established during the initial access stage for DL or UL traffic, we consider the beamforming problem for UL or DL whose payload traffic is much lighter than the other link. Such asymmetric traffic allows part of the massive MIMO array to be deactivated in order to achieve a higher energy efficiency (EE) while still meeting the spectrum efficiency (SE) requirements for UL or DL. To deal with such a problem, we propose the corresponding phase shifter (PS) deactivation strategies based on different SE constraints for individual mobile stations or users independently, which select the PSs to be deactivated accordingly with low computational complexity. We then propose a novel codebook design method, which relies on the iteratively updated average main beam gain, in order to pursue a flatter beam under asymmetric DL-UL traffic that interacts with the corresponding PS deactivation strategy. The proposed codebook not only offers flexible beam width and flat main beam gain, but also can generate some good candidate codewords to account for the need of beam refinements, after some of the PSs have been deactivated. Simulation results demonstrated the superiority of the proposed energy efficient PS deactivation approach and the corresponding codebook design.
  PubDate: FRI, 10 MAR 2023 10:02:14 -04
  Issue No: Vol. 22, No. 11 (2023)
Channel Estimation for LEO Satellite Massive MIMO OFDM Communications
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  Authors: Ke-Xin Li;Xiqi Gao;Xiang-Gen Xia;
  Pages: 7537 - 7550
  Abstract: In this paper, we investigate the massive multiple-input multiple-output orthogonal frequency division multiplexing channel estimation for low-earth-orbit satellite communication systems. First, we use the angle-delay domain channel to characterize the space-frequency domain channel. Then, we show that the asymptotic minimum mean square error (MMSE) of the channel estimation can be minimized if the array response vectors of the user terminals (UTs) that use the same pilot are orthogonal. Inspired by this, we design an efficient graph-based pilot allocation strategy to enhance the channel estimation performance. In addition, we devise a novel two-stage channel estimation (TSCE) approach, in which the received signals at the satellite are manipulated with per-subcarrier space domain processing followed by per-user frequency domain processing. Moreover, the space domain processing of each UT is shown to be identical for all the subcarriers, and an asymptotically optimal vector for the per-subcarrier space domain linear processing is derived. The frequency domain processing can be efficiently implemented by means of the fast Toeplitz system solver. Simulation results show that the proposed TSCE approach can achieve a near performance to the MMSE estimation with much lower complexity.
  PubDate: FRI, 10 MAR 2023 10:02:14 -04
  Issue No: Vol. 22, No. 11 (2023)
Graph Neural Networks for Distributed Power Allocation in Wireless
Networks: Aggregation Over-the-Air
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  Authors: Yifan Gu;Changyang She;Zhi Quan;Chen Qiu;Xiaodong Xu;
  Pages: 7551 - 7564
  Abstract: Distributed power allocation is important for interference-limited wireless networks with dense transceiver pairs. In this paper, we aim to design low signaling overhead distributed power allocation schemes by using graph neural networks (GNNs), which are scalable to the number of wireless links. We first apply the message passing neural network (MPNN), a unified framework of GNN, to solve the problem. We show that the signaling overhead grows quadratically as the network size increases. Inspired from the over-the-air computation (AirComp), we then propose an Air-MPNN framework, where the messages from neighboring nodes are represented by the transmit power of pilots and can be aggregated efficiently by evaluating the total interference power. The signaling overhead of Air-MPNN grows linearly as the network size increases, and we prove that Air-MPNN is permutation invariant. To further reduce the signaling overhead, we propose the Air message passing recurrent neural network (Air-MPRNN), where each node utilizes the graph embedding and local state in the previous frame to update the graph embedding in the current frame. Since existing communication systems send a pilot during each frame, Air-MPRNN can be integrated into the existing standards by adjusting pilot power. Simulation results validate the scalability of the proposed frameworks, and show that they outperform the existing power allocation algorithms in terms of sum-rate for various system parameters.
  PubDate: MON, 13 MAR 2023 10:05:05 -04
  Issue No: Vol. 22, No. 11 (2023)
Physical Layer Security for Frequency Diverse Array-Based Dual-Hop Spatial
Modulation
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  Authors: Jiangwei Jian;Wen-Qin Wang;Abdul Basit;Bang Huang;
  Pages: 7565 - 7579
  Abstract: In this paper, we design the physical layer security of a dual-hop spatial modulation system provided the relay node transmits its own private information. Meanwhile, the quadrature virtual channel spatial modulation (QVCSM) is proposed to enhance the physical layer security in the second hop. To achieve system security in both angle and range dimensions, the frequency diverse array (FDA) is used in the relay node with a decode-and-forward (DF) protocol. Moreover, the baseband directional modulation (DM) and artificial noise (AN) are jointly utilized to suppress the received signal at the passive eavesdropper. Additionally, the Ergodic secrecy rate of the proposed system is analyzed, whereas the closed-form expressions for tight upper bounds on the bit error rates (BERs) of the source and relay are derived for both the destination and eavesdropper. The simulation results show that the proposed FDA relay design outperforms the traditional phase array (PA) relay system in terms of the system secrecy rate. In contrast to the quadrature spatial modulation (QSM) method, the proposed designs achieve an improved suppression performance of the eavesdropper.
  PubDate: MON, 13 MAR 2023 10:05:05 -04
  Issue No: Vol. 22, No. 11 (2023)
Matching-Aided-Learning Resource Allocation for Dynamic Offloading in
mmWave MEC System
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  Authors: Zhongling Zhao;Jia Shi;Zan Li;Jiangbo Si;Pei Xiao;Rahim Tafazolli;
  Pages: 7580 - 7591
  Abstract: With exploiting massive spectrum resources, millimeter wave (mmWave) communications significantly improve the offloading capability for future mobile edge computing (MEC) techniques, which however is constrained by blockage problem in dynamic environments. In this paper, we study the resource allocation problem for the conceived mmWave MEC system with dynamic offloading process, in which the UEs are characterized by being mobile and having the imperfect knowledge of the offloading tasks coming. By introducing the multi-objective Markov decision process (MOMDP), the resource allocation problem is modeled by simultaneously minimizing the delay and energy consumption, where jointly considering the multi-beam assignment (mBA) and beamwidth and power optimization (BPO). To tackle this problem, we innovatively propose a matching-aided-learning (MaL) resource allocation scheme, with the aid of a learnable weight based attention mechanism (LW-AM) for adapting the dynamic offloading process. In particular, our MaL scheme includes many-to-one matching (M2O-M) based mBA algorithm and deep deterministic policy gradient (DDPG) based BPO algorithm, which are executed iteratively and converge with relatively low number of iterations. The simulation results show the practical value of the proposed MaL, which can approach the performance of benchmark scheme with perfect knowledge of offloading tasks.
  PubDate: TUE, 04 APR 2023 10:04:28 -04
  Issue No: Vol. 22, No. 11 (2023)
Using Loaded N-Port Structures to Achieve the Continuous-Space
Electromagnetic Channel Capacity Bound
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  Authors: Zixiang Han;Shanpu Shen;Yujie Zhang;Shiwen Tang;Chi-Yuk Chiu;Ross Murch;
  Pages: 7592 - 7605
  Abstract: A method for achieving the continuous-space electromagnetic channel capacity bound using loaded $N$ -port structures is described. It is relevant for the design of compact multiple-input multiple-output (MIMO) antennas that can achieve channel capacity bounds when constrained by size. The method is not restricted to a specific antenna configuration and a closed-form expression for the channel capacity limits are provided with various constraints. Furthermore, using loaded $N$ -port structures to represent arbitrary antenna geometries, an efficient optimization approach is proposed for finding the optimal MIMO antenna design that achieves the channel capacity bounds. Simulation results of the channel capacity bounds achieved using our MIMO antenna design with one square wavelength size are provided. These show that at least 18 ports can be supported in one square wavelength and achieve the continuous-space electromagnetic channel capacity bound. The results demonstrate that our method can link continuous-space electromagnetic channel capacity bounds to MIMO antenna design.
  PubDate: MON, 13 MAR 2023 10:05:05 -04
  Issue No: Vol. 22, No. 11 (2023)
RIS-Aided Multiuser MIMO-OFDM With Linear Precoding and Iterative
Detection: Analysis and Optimization
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  Authors: Mingyang Yue;Lei Liu;Xiaojun Yuan;
  Pages: 7606 - 7619
  Abstract: In this paper, we consider a reconfigurable intelligent surface (RIS) aided uplink multiuser multi-input multi-output (MIMO) orthogonal frequency division multiplexing (OFDM) system, where the receiver is assumed to conduct low-complexity iterative detection. We aim to minimize the total transmit power by jointly designing the precoder of the transmitter and the passive beamforming of the RIS. This problem can be tackled from the perspective of information theory. But this information-theoretic approach may involve prohibitively high complexity since the number of rate constraints that specify the capacity region of the uplink multiuser channel is exponential in the number of users. To avoid this difficulty, we formulate the design problem of the iterative receiver under the constraints of a maximal iteration number and target bit error rates of users. To tackle this challenging problem, we propose a groupwise successive interference cancellation (SIC) optimization approach, where the signals of users are decoded and canceled in a group-by-group manner. We present a heuristic user grouping strategy, and resort to the alternating optimization technique to iteratively solve the precoding and passive beamforming sub-problems. Specifically, for the precoding sub-problem, we employ fractional programming to convert it to a convex problem; for the passive beamforming sub-problem, we adopt successive convex approximation to deal with the unit-modulus constraints of the RIS. We show that the proposed groupwise SIC approach has significant advantages in both performance and computational complexity, as compared with the counterpart approaches.
  PubDate: MON, 13 MAR 2023 10:05:05 -04
  Issue No: Vol. 22, No. 11 (2023)
Parabolic Wavefront Model for Line-of-Sight MIMO Channels
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  Authors: Heedong Do;Namyoon Lee;Angel Lozano;
  Pages: 7620 - 7634
  Abstract: Motivated by the widespread adoption of the parabolic wavefront model for line-of-sight (LOS) multiple-input multiple-output (MIMO) communication, this paper presents a comprehensive analysis of this model’s validity and simple conditions that ensure its applicability. Then, with the model’s scope clearly delineated, the paper expounds a number of properties of the channel that results from applying it. Connections are drawn among these properties under the umbrella of a Fourier interpretation, and their significance to LOS MIMO communication is substantiated.
  PubDate: TUE, 14 MAR 2023 10:02:59 -04
  Issue No: Vol. 22, No. 11 (2023)
Data Synchronization in Vehicular Digital Twin Network: A Game Theoretic
Approach
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  Authors: Jinkai Zheng;Tom H. Luan;Yao Zhang;Rui Li;Yilong Hui;Longxiang Gao;Mianxiong Dong;
  Pages: 7635 - 7647
  Abstract: A fundamental issue of the vehicular digital twin (DT) is efficiently synchronizing the data between the DT and the vehicular user (VUE). In this paper, we consider the heterogeneous vehicular networks (HetVNets) in which a VUE can connect to the network through different networks. The HetVNets can improve the efficiency of communication by providing seamless connections. However, the uneven distribution of VUEs and the dynamics of HetVNets make the environment more complex. Therefore, we propose the network selection algorithm for data synchronization between VUEs and DTs in the HetVNets, where the behaviour between the VUEs is considered as a competition for wireless resources. A learning-based prediction model residing in the DT is developed where the DT can predict the waiting time of each relay and transmit the predicted results to the VUE for decision-making. We model the network selection problem as a potential game considering both the transmission time and the waiting time obtained from the prediction model and prove the existence of Nash equilibrium (NE). We analyze the performance of the proposed algorithm, and simulation results show that our approach can effectively find the optimal strategy while achieving a fast convergence speed and high-level performance compared to the baselines.
  PubDate: TUE, 14 MAR 2023 10:02:59 -04
  Issue No: Vol. 22, No. 11 (2023)
Intelligent Trajectory Design for RIS-NOMA Aided Multi-Robot
Communications
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  Authors: Xinyu Gao;Xidong Mu;Wenqiang Yi;Yuanwei Liu;
  Pages: 7648 - 7662
  Abstract: A novel reconfigurable intelligent surface-aided multi-robot network is proposed, where multiple mobile robots are served by an access point (AP) through non-orthogonal multiple access (NOMA). The goal is to maximize the sum-rate of whole trajectories for the multi-robot system by jointly optimizing trajectories and NOMA decoding orders of robots, phase-shift coefficients of the RIS, and the power allocation of the AP, subject to predicted initial and final positions of robots and the quality of service (QoS) of each robot. To tackle this problem, an integrated machine learning (ML) scheme is proposed, which combines long short-term memory (LSTM)-autoregressive integrated moving average (ARIMA) model and dueling double deep Q-network ( $\text{D}^{3}$ QN) algorithm. For initial and final position prediction for robots, the LSTM-ARIMA is able to overcome the problem of gradient vanishment of non-stationary and non-linear sequences of data. For jointly determining the phase shift matrix and robots’ trajectories, $\text{D}^{3}$ QN is invoked for solving the problem of action value overestimation. Based on the proposed scheme, each robot holds an optimal trajectory based on the maximum sum-rate of a whole trajectory, which reveals that robots pursue long-term benefits for whole trajectory design. Numerical results demonstrated that: 1) LSTM-ARIMA model provides high accuracy predicting model; 2) The proposed $\text{D}^{3}$ QN algorithm can achieve fast average convergence; and 3) RIS-NOMA networks have superior network performance compared to RIS-aided orthogonal counterparts.
  PubDate: TUE, 14 MAR 2023 10:02:59 -04
  Issue No: Vol. 22, No. 11 (2023)
RML22: Realistic Dataset Generation for Wireless Modulation Classification
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  Authors: Venkatesh Sathyanarayanan;Peter Gerstoft;Aly El Gamal;
  Pages: 7663 - 7675
  Abstract: Application of Deep learning (DL) to modulation classification has shown significant performance improvements. The focus has been model centric, where newer architectures are attempted on benchmark dataset RADIOML.2016.10A (RML16). RML16 is a high impact effort that laid the foundation for generating a synthetic dataset for applying DL models to wireless problems. This encouraged development of newer architectures to RML16. We use a data centric DL approach where focus moves from model architectures to data quality. RML16 has shortcomings such as errors and ad-hoc choices of parameters. We build upon RML16 and provide realistic and correct methodology of generating dataset. A new benchmark dataset RML22 is generated. Going forward, we envision researchers to improve model quality on RML22. We attempt to improve data quality by studying the impact of information sources. Further, the choices of artifacts and signal model parameterization are analyzed carefully. The Python source code used to generate RML22 is shared to enable researchers to further improve dataset quality.
  PubDate: TUE, 14 MAR 2023 10:02:59 -04
  Issue No: Vol. 22, No. 11 (2023)
Location Information Assisted Beamforming Design for Reconfigurable
Intelligent Surface Aided Communication Systems
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  Authors: Zhe Xing;Rui Wang;Xiaojun Yuan;Jun Wu;
  Pages: 7676 - 7695
  Abstract: The large overhead arising from conventional channel estimations in reconfigurable intelligent surface (RIS) aided millimeter-wave communication systems, may offset the performance gain brought by the RIS. To tackle this issue, we propose a location information assisted beamforming design without the requirement of the channel training process. First, we establish the geometrical relationship between the channel model and the user location, and mathematically derive an approximate channel state information (CSI) error bound based on the user location error region. Then, for combating the negative impact of the location error on the communication performance, we formulate a worst-case robust beamforming optimization problem to optimize the beamformer at the base station (BS) and the phase-shift matrix at the RIS. To solve this non-convex problem, we develop a novel relaxed alternating optimization process (RAOP) by utilizing various optimization tools, such as the Lagrange multiplier, the matrix inversion lemma, the semidefinite relaxation (SDR), as well as the branch-and-bound (BnB). Additionally, we prove sufficient conditions for the SDR to yield rank-one solutions, and modify the BnB to acquire the phase-shift solution under an arbitrary constraint of possible phase-shift values. Finally, we analyse the convergence and complexity of the proposed RAOP, and carry out simulations for performance evaluations. Compared to the conventional non-robust beamforming, our method performs better and shows strong robustness against the location-error-related CSI uncertainty. Compared to the robust beamforming based on the S-procedure and penalty convex-concave procedure (CCP), our method with BnB shows the advantages of being able to converge faster and handle arbitrary phase-shift argument sets.
  PubDate: TUE, 14 MAR 2023 10:02:59 -04
  Issue No: Vol. 22, No. 11 (2023)
Rate-Splitting Multiple Access for Quantized Multiuser MIMO Communications
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  Authors: Seokjun Park;Jinseok Choi;Jeonghun Park;Wonjae Shin;Bruno Clerckx;
  Pages: 7696 - 7711
  Abstract: This paper investigates the sum spectral efficiency maximization problem in downlink multiuser multiple-input multiple-output systems with low-resolution quantizers at an access point (AP) and users. We consider rate-splitting multiple access (RSMA) to enhance spectral efficiency by offering opportunities to boost achievable degree-of-freedom. Optimizing RSMA precoders, however, is highly challenging due to the minimum rate constraint when determining the common rate. The quantization errors coupled with the precoders make the problem more complicated. In this paper, we develop a novel RSMA precoding algorithm incorporating quantization errors for maximizing the sum spectral efficiency. To this end, we first obtain an approximate spectral efficiency in a smooth function. Subsequently, we derive the first-order optimality condition in the form of the nonlinear eigenvalue problem (NEP). We propose a computationally efficient algorithm to find the principal eigenvector of the NEP as a sub-optimal solution. We also extend the weighted minimum mean square error-based RSMA precoding to the considered quantization system. Simulation results validate the proposed methods. The key benefit of using RSMA over spatial division multiple access (SDMA) comes from the ability of the common stream to balance between the channel gain and quantization error in multiuser MIMO systems with different quantization resolutions.
  PubDate: WED, 15 MAR 2023 10:01:49 -04
  Issue No: Vol. 22, No. 11 (2023)
Dual-Mode Index Modulation for Non-Orthogonal Frequency Division
Multiplexing
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  Authors: Muhammad Sajid Sarwar;I. Nyoman Apraz Ramatryana;Muneeb Ahmad;Soo Young Shin;
  Pages: 7712 - 7726
  Abstract: This paper presents dual-mode index modulation for spectral efficient frequency division multiplexing (SEFDM-DM). SEFDM is a non-orthogonal multicarrier technique created by compressing the subcarrier spacing of classical orthogonal frequency-division multiplexing (OFDM). SEFDM provides high spectrum efficiency (SE) at the expense of increased inter-carrier interference (ICI). SEFDM with index modulation (SEFDM-IM) has two information-bearing units, that is, a subcarrier activation pattern and modulated symbols, which reduce ICI at the expense of an SE deficit owing to inactive subcarriers. The proposed SEFDM-DM uses all available subcarriers while retaining the diversity gain of index modulation (IM). It enhances SE by transmitting distinct constellation modes through a subcarrier index selection mechanism. This study also presents SEFDM-DM with coordinate interleaving (SEFDM-CDM), which introduces space-time block codes with coordinate interleaving to enhance transmission diversity by sending real and imaginary parts of the constellation symbols over different subcarriers. Analytical and simulation results corroborate the benefits of the suggested work in terms of SE and bit error rate.
  PubDate: WED, 15 MAR 2023 10:01:49 -04
  Issue No: Vol. 22, No. 11 (2023)
Intelligent Traffic Steering in Beyond 5G Open RAN Based on LSTM Traffic
Prediction
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  Authors: Fatemeh Kavehmadavani;Van-Dinh Nguyen;Thang X. Vu;Symeon Chatzinotas;
  Pages: 7727 - 7742
  Abstract: Open radio access network (ORAN) Alliance offers a disaggregated RAN functionality built using open interface specifications between blocks. To efficiently support various competing services, namely enhanced mobile broadband (eMBB) and ultra-reliable and low-latency (uRLLC), the ORAN Alliance has introduced a standard approach toward more virtualized, open, and intelligent networks. To realize the benefits of ORAN in optimizing resource utilization, this paper studies an intelligent traffic steering (TS) scheme within the proposed disaggregated ORAN architecture. For this purpose, we propose a joint intelligent traffic prediction, flow-split distribution, dynamic user association, and radio resource management (JIFDR) framework in the presence of unknown dynamic traffic demands. To adapt to dynamic environments on different time scales, we decompose the formulated optimization problem into two long-term and short-term subproblems, where the optimality of the latter is strongly dependent on the optimal dynamic traffic demand. We then apply a long-short-term memory (LSTM) model to effectively solve the long-term subproblem, aiming to predict dynamic traffic demands, RAN slicing, and flow-split decisions. The resulting non-convex short-term subproblem is converted to a more computationally tractable form by exploiting successive convex approximations. Finally, simulation results are provided to demonstrate the effectiveness of the proposed algorithms compared to several well-known benchmark schemes.
  PubDate: WED, 15 MAR 2023 10:01:49 -04
  Issue No: Vol. 22, No. 11 (2023)
Intelligent Cloud-Edge Collaborations Assisted Energy-Efficient Power
Control in Heterogeneous Networks
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  Authors: Lin Zhang;Jianhao Peng;Jiabao Zheng;Ming Xiao;
  Pages: 7743 - 7755
  Abstract: We consider a typical heterogeneous network (HetNet), which consists of a macro base station (BS) and multiple small BSs sharing the same spectrum band. Since the spectrum sharing among different BS-user links may cause severe mutual interference and degrades the global energy efficiency (GEE), it is important to optimize the transmit power of each BS and enhance the GEE. Conventional methods first collect the global instantaneous channel state information (CSI) and then optimize the transmit power in a centralized manner. Nevertheless, it is demanding to obtain the global instantaneous CSI in practical situations and the centralized optimization may easily overwhelm the coherence time of wireless channels. To tackle these issues, we leverage the strong computing capability of the (cloud) core network and the fast configuration capability of (edge) BSs and propose an intelligent cloud-edge collaboration framework. By properly designing the cloud-edge collaboration, we develop a deep reinforcement learning (DRL) based energy efficient power control algorithm. With the proposed algorithm, each BS can configure its transmit power independently and enhance the GEE. Simulation results reveal that, in both static-user and mobile-user scenarios, the proposed algorithm can provide comparable GEE performance with the conventional methods while requiring a much lower time complexity.
  PubDate: THU, 16 MAR 2023 10:02:19 -04
  Issue No: Vol. 22, No. 11 (2023)
Antenna Selection for Reconfigurable Intelligent Surfaces: A
Transceiver-Agnostic Passive Beamforming Configuration
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  Authors: Chao Xu;Jiancheng An;Tong Bai;Shinya Sugiura;Robert G. Maunder;Lie-Liang Yang;Marco Di Renzo;Lajos Hanzo;
  Pages: 7756 - 7774
  Abstract: Reconfigurable intelligent surface (RIS) is capable of improving the wireless system performance by steering the reflected signal in the desired direction. One of the major challenges is that both the transceiver and RIS have to be jointly optimized, where the optimization problems have to be reformulated for different system models and scenarios. To circumvent this challenge, new low-complexity antenna selection (AS) algorithms for transceiver-agnostic RIS configuration are proposed. Given a multiple-input multiple-output (MIMO) channel, the proposed RIS-AS opts for accurately aligning the RIS both with the transmit antenna (TA) and receive antenna (RA) for the sake of maximizing the MIMO channel’s overall output power. The proposed RIS-AS only has to configure the RIS alone, i.e. without iterations with the transceiver optimization. As a result, the proposed RIS-AS has the compelling benefit that they are generically applicable, regardless of the specific transceiver architecture. Our simulation results confirm that the proposed RIS-AS is capable of supporting any MIMO configuration, regardless of their closed/open-loop, single-/ full-RF and multiplexing-/diversity-oriented setups.
  PubDate: THU, 16 MAR 2023 10:02:30 -04
  Issue No: Vol. 22, No. 11 (2023)
Non-Line-of-Sight Full-Duplex Ultraviolet Communications Under
Self-Interference
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  Authors: Zhifeng Wang;Renzhi Yuan;Mugen Peng;
  Pages: 7775 - 7788
  Abstract: The full-duplex optical communications can be achieved simply by separating the transmitting link and the receiving link in the space. However, it is challenging to achieve the non-line-of-sight (NLOS) full-duplex ultraviolet (UV) communication due to the serious self-interference caused by the strong multiple scattering effects of UV signals. To explore the capacity of NLOS full-duplex UV communications, in this paper, we first quantify the self-interference using an analytical channel impulse response function. Based on the quantified self-interference, we then derive the error rate and the corresponding achievable information rate (AIR) for on- off keying modulation, 4-digital-pulse-interval modulation and 4-pulse-position modulation. We further propose a self-interference cancelling (SIC) method to mitigate the impacts of self-interferences. Simulation results show that the proposed SIC method can significantly improve the error rate and AIR performances. Besides, we find that the NLOS full-duplex UV communication will gradually lose its advantage over the NLOS half-duplex UV communication as either the communication distance or the elevation angle increases. However, using the proposed SIC method, the NLOS full-duplex UV communication can hold its advantage in a wide range of system geometries.
  PubDate: MON, 13 MAR 2023 10:05:05 -04
  Issue No: Vol. 22, No. 11 (2023)
IRS-Aided Joint Spatial Division and Multiplexing for mmWave Multiuser
MISO Systems
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  Authors: Zijian Chen;Ming-Min Zhao;Min Li;Ming Lei;Min-Jian Zhao;
  Pages: 7789 - 7804
  Abstract: Intelligent reflecting surface (IRS)-aided millimeter wave (mmWave) communication systems have gained considerable attention recently. However, the benefits brought by IRS require the instantaneous channel state information (I-CSI) of the cascaded base station (BS)-IRS and IRS-user channel which is difficult to obtain in practice, especially for the multiuser scenario. To address this issue, in this paper, we combine two-timescale beamforming and multi-IRS aided joint spatial division and multiplexing (JSDM) in a mmWave multiuser system. Specifically, all the users are first divided into different groups and each group is associated with an IRS. Then, we propose a novel two-stage grouping-based randomized beamforming (TS-GRB) scheme, where the analog beamformer is designed based on the statistical CSI (S-CSI) in the first stage, and the short-term digital beamformer at the BS and long-term passive beam pattern control policy at the IRSs are jointly optimized in the second stage with both S-CSI and dimension-reduced effective I-CSI. In particular, in the first stage, the analog beamformer is designed to reduce the inter-group interference (IGI) and effective channel dimension, while in the second stage, a two-timescale randomized joint beamforming (TRJB) algorithm is proposed to maximize the proportional fairness utility (PFU). We show that through two-timescale beamforming, JSDM and proper problem reformulation, the pilot overhead of our TS-GRB scheme is significantly lower than existing schemes. Finally, simulation results are presented to illustrate the effectiveness of the proposed TS-GRB scheme.
  PubDate: THU, 16 MAR 2023 10:02:19 -04
  Issue No: Vol. 22, No. 11 (2023)
Outage Analysis of IRS-Assisted RF Powered Networks for Energy-Constrained
IoT Devices
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  Authors: Danish Mehmood Mughal;Daniyal Munir;Min Young Chung;
  Pages: 7805 - 7818
  Abstract: This paper studies the performance of an intelligent reflecting surface (IRS)-assisted radio frequency (RF)-powered wireless network. In the considered network, a multi-antenna base station (BS) communicates with its users (UEs) through downlink transmission and energy-constrained internet-of-things (IoT) devices, which are randomly deployed around a cluster head (CH), utilize the resources of BS for energy harvesting and information transmission. IRS, deployed randomly around CH, reflects the RF signals transmitted by BS to the IoT devices, providing additional energy to harvest. For the considered network, average distances between BS, CH, IRS, and an IoT device have been computed, statistically. Furthermore, the average harvested energy at an IoT device is computed. Instead of considering a fixed transmit power (FTP) of an IoT device, we consider that its transmit power varies randomly depending on the stored energy. Using the Markov chain model, we obtain transmission probability and state-dependent transmit power (SDTP). Analytical expressions for outage probability and throughput of an IoT device are obtained. To manifest the effectiveness of derived expressions, numerical results are compared with Monte-Carlo simulation results. Our results reveal that the proposed SDTP scheme has a performance gain over the FTP scheme, especially in scenarios where harvested energy is in abundance.
  PubDate: THU, 16 MAR 2023 10:02:19 -04
  Issue No: Vol. 22, No. 11 (2023)
Opportunistic Fluid Antenna Multiple Access
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  Authors: Kai-Kit Wong;Kin-Fai Tong;Yu Chen;Yangyang Zhang;Chan-Byoung Chae;
  Pages: 7819 - 7833
  Abstract: Multiple access can be realized by utilizing the spatial moments of deep fades, using fluid antennas. The interference immunity for fluid antenna multiple access (FAMA), nevertheless, comes with the requirement of a large number of ports at each user. To alleviate this, we study the synergy between opportunistic scheduling and FAMA. A large pool of users permits selection of favourable users for FAMA and decreases the outage probability at each selected user. Our objective is to characterize the benefits of opportunistic scheduling in FAMA. In particular, we derive the multiplexing gain of the opportunistic FAMA network in closed form and upper bound the required number of users in the pool to achieve a given multiplexing gain. Also, we find a lower bound on the required outage probability at each user for achieving a given network multiplexing gain, from which the advantage of opportunistic scheduling is illustrated. In addition, we investigate the rate of increase of the multiplexing gain with respect to the number of users in the pool, and derive a tight approximation to the multiplexing gain, expressed in closed form. As a key result of our analysis, we obtain an operating condition on the product of the number of users in the pool and the number of ports at each fluid antenna that ensures a high multiplexing gain. Numerical results demonstrate clear benefits of opportunistic scheduling in FAMA networks, and corroborate our analytical results.
  PubDate: TUE, 21 MAR 2023 10:04:17 -04
  Issue No: Vol. 22, No. 11 (2023)
Detecting Abrupt Change in Channel Covariance Matrix for MIMO
Communication
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  Authors: Runnan Liu;Liang Liu;Dazhi He;Wenjun Zhang;Erik G. Larsson;
  Pages: 7834 - 7847
  Abstract: The acquisition of the channel covariance matrix is of paramount importance to many strategies in multiple-input-multiple-output (MIMO) communications, such as the minimum mean-square error (MMSE) channel estimation. Therefore, plenty of efficient channel covariance matrix estimation schemes have been proposed in the literature. However, an abrupt change in the channel covariance matrix may happen occasionally in practice due to the change in the scattering environment and the user location. Our paper aims to adopt the classic change detection theory to detect the change in the channel covariance matrix as accurately and quickly as possible such that the new covariance matrix can be re-estimated in time. Specifically, this paper first considers the technique of on-line change detection (also known as quickest/sequential change detection), where we need to detect whether a change in the channel covariance matrix occurs at each channel coherence time interval. Next, because the complexity of detecting the change in a high-dimension covariance matrix at each coherence time interval is too high, we devise a low-complexity off-line strategy in massive MIMO systems, where change detection is merely performed at the last channel coherence time interval of a given time period. Numerical results show that our proposed on-line and off-line schemes can detect the channel covariance change with a small delay and a low false alarm rate. Therefore, our paper theoretically and numerically verifies the feasibility of detecting the channel covariance change accurately and quickly in practice.
  PubDate: MON, 20 MAR 2023 10:11:10 -04
  Issue No: Vol. 22, No. 11 (2023)
PRONTO: Preamble Overhead Reduction With Neural Networks for Coarse
Synchronization
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  Authors: Nasim Soltani;Debashri Roy;Kaushik Chowdhury;
  Pages: 7848 - 7861
  Abstract: In IEEE 802.11 WiFi-based waveforms, the receiver performs coarse time and frequency synchronization using the first field of the preamble known as the legacy short training field (L-STF). The L-STF occupies upto 40% of the preamble length and takes upto $32 \mu \text{s}$ of airtime. With the goal of reducing communication overhead, we propose a modified waveform, where the preamble length is reduced by eliminating the L-STF. To decode this modified waveform, we propose a neural network (NN)-based scheme called PRONTO that performs coarse time and frequency estimations using other preamble fields, specifically the legacy long training field (L-LTF). Our contributions are threefold: (i) We present PRONTO featuring customized convolutional neural networks (CNNs) for packet detection and coarse carrier frequency offset (CFO) estimation, along with data augmentation steps for robust training. (ii) We propose a generalized decision flow that makes PRONTO compatible with legacy waveforms that include the standard L-STF. (iii) We validate the outcomes on an over-the-air WiFi dataset from a testbed of software defined radios (SDRs). Our evaluations show that PRONTO can perform packet detection with 100% accuracy, and coarse CFO estimation with errors as small as 3%. We demonstrate that PRONTO provides upto 40% preamble length reduction with no bit error rate (BER) degradation. We further show that PRONTO is able to achieve the same performance in new environments without the need to re-train the CNNs. Finally, we experimentally show the speedup achieved by PRONTO through GPU parallelization over the corresponding CPU-only implementations.
  PubDate: MON, 20 MAR 2023 10:11:10 -04
  Issue No: Vol. 22, No. 11 (2023)
A Lightweight Authentication and Key Exchange Protocol With Anonymity for
IoT
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  Authors: Daojing He;Yanchang Cai;Shanshan Zhu;Ziming Zhao;Sammy Chan;Mohsen Guizani;
  Pages: 7862 - 7872
  Abstract: The number of IoT devices is growing rapidly, and the interaction between devices and servers is also more frequent. However, IoT devices are often at the edge of the network, which leads their communications with the server to be completely exposed, making it more vulnerable to attacks. Moreover, IoT devices have limited energy and computational resources. Therefore, we propose in this paper a lightweight authentication and key exchange protocol with anonymity for IoT devices. The proposed scheme supports mutual authentication between IoT devices and the server. We verify the security of the protocol through formal and informal analyses. Finally, we compare security and performance with other protocols, which shows that our protocol has the advantages of being lightweight and secure.
  PubDate: FRI, 24 MAR 2023 10:03:06 -04
  Issue No: Vol. 22, No. 11 (2023)
Multi-IRS-Aided Millimeter-Wave Multi-User MISO Systems for Power
Minimization Using Generalized Benders Decomposition
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  Authors: Huan Huang;Ying Zhang;Hongliang Zhang;Zixin Zhao;Chongfu Zhang;Zhu Han;
  Pages: 7873 - 7886
  Abstract: Difficulties in controlling IRSs to form the optimized passive beamforming have rarely been considered in intelligent reflecting surface (IRS)-aided systems, which are summarized as follows: 1) sending the optimized passive precoding vectors to the IRS controller incurs significant control overheads; 2) implementing the optimized passive precoding needs to set massive modes in the IRS control circuit. To address these issues, we investigate codebook-based passive beamforming for multi-IRS-aided millimeter-wave (mmWave) multi-user multiple-input single-output (MU-MISO) systems, where the control overheads are reduced to several scalars and the number of modes set in the IRS control circuit is reduced to that of codewords. Moreover, we formulate a joint passive and active precoding problem in the multi-IRS-aided mmWave MU-MISO system as a mixed-integer nonlinear programming (MINLP) problem, and then develop a generalized Benders decomposition (GBD)-based joint passive and active precoding algorithm. The proposed algorithm offers near-optimal performance ( $\ge99.9$ %) with significantly-reduced computational complexity. Simulation results show that the proposed algorithm achieves energy savings of up to 50% and 95%, compared to the benchmark by the maximum ratio transmission and that without IRSs, respectively. In addition, the energy savings increase with the number of reflecting elements packed on each IRS as well as that of codewords.
  PubDate: MON, 20 MAR 2023 10:11:10 -04
  Issue No: Vol. 22, No. 11 (2023)
Cross-Frame Resource Allocation With Context-Aware QoE Estimation for
360° Video Streaming in Wireless Virtual Reality
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  Authors: Cheng-Yeh Chen;Hung-Yun Hsieh;
  Pages: 7887 - 7901
  Abstract: Wireless virtual reality (VR), aiming to provide an untethered immersive experience through 360° videos, could be facilitated by viewport-guided streaming with the help of viewport prediction. Although many recent viewport predictors can output a series of predictions over upcoming frames, most existing work on video streaming does not fully utilize the capability of these predictors. In this paper, we investigate the problem of 360° video streaming by incorporating the complete series of viewport predictions for maximizing the quality of experience (QoE) through cross-frame resource allocation. To address the problem of viewport prediction errors that could result in erroneous estimation of QoE contribution of tiles in upcoming frames, we develop a novel approach based on contextual multi-armed bandit (CMAB) to “learn” online the viewing behavior of the user and the capability of the predictor such that resource can be preferentially allocated to tiles with significant QoE contribution. Further, to address the problem of transmission failures during wireless streaming, we formulate a constrained Markov decision process (CMDP) and apply model predictive control (MPC) to account for resource competition among reactive and proactive transmissions as well as retransmissions of tiles. The performance of the proposed streaming system is evaluated using a real-world VR dataset, state-of-the-art viewport predictors, and realistic mmWave channel models. An improvement of 5.68% in QoE and a reduction of 23.0% in resource waste are achieved across various videos, users, and predictors. Simulation results substantiate that the context-aware QoE learned by the proposed CMAB effectively addresses prediction errors for tiles with different temporal and spatial contexts, and the proposed CMDP can achieve the desired performance even under high viewport prediction error and channel error.
  PubDate: MON, 20 MAR 2023 10:11:10 -04
  Issue No: Vol. 22, No. 11 (2023)
Wireless-Enabled Asynchronous Federated Fourier Neural Network for
Turbulence Prediction in Urban Air Mobility (UAM)
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  Authors: Tengchan Zeng;Omid Semiari;Walid Saad;Mehdi Bennis;
  Pages: 7902 - 7916
  Abstract: To meet the growing mobility needs in intra-city transportation, the concept of urban air mobility (UAM) has been proposed in which vertical takeoff and landing (VTOL) aircraft are used to provide a ride-hailing service. In UAM, aircraft can operate in designated air spaces known as corridors, that link the aerodromes, thus avoiding the use of complex routing strategies such as those of modern-day helicopters and alleviating the burden on the ground transportation system. For safety, a UAM aircraft must use air-to-ground communications to report flight plan, off-nominal events, and real-time movement to ground base stations (GBSs). A reliable communication network between GBSs and aircraft enables UAM to adequately utilize the airspace and create a fast, efficient, and safe transportation system. In this paper, to characterize the wireless connectivity performance for UAM, a suitable spatial model is proposed. For the considered setup, assuming that any given aircraft communicates with the closest GBS, the distribution of the distance between an arbitrarily selected GBS and its associated aircraft and the Laplace transform of the interference experienced by the GBS are derived. Using these results, the signal-to-interference ratio (SIR)-based connectivity probability is determined to capture the connectivity performance of the UAM aircraft-to-ground communication network. Then, leveraging these connectivity results, a wireless-enabled asynchronous federated learning (AFL) framework that uses a Fourier neural network is proposed to tackle the challenging problem of turbulence prediction during UAM operations. For this AFL scheme, a staleness-aware global aggregation scheme is introduced to expedite the convergence to the optimal turbulence prediction model used by UAM aircraft. Simulation results validate the theoretical derivations for the UAM wireless connectivity. The results also demonstrate that the proposed AFL framework converges to the optimal turbulence prediction model faster than the synchronous federated learning baselines and a staleness-free AFL approach. Furthermore, the results characterize the performance of wireless connectivity and convergence of the aircraft’s turbulence model under different parameter settings, offering useful UAM design guidelines.
  PubDate: MON, 20 MAR 2023 10:11:10 -04
  Issue No: Vol. 22, No. 11 (2023)
Inductive Matrix Completion and Root-MUSIC-Based Channel Estimation for
Intelligent Reflecting Surface (IRS)-Aided Hybrid MIMO Systems
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  Authors: Khawaja Fahad Masood;Jun Tong;Jiangtao Xi;Jinhong Yuan;Yanguang Yu;
  Pages: 7917 - 7931
  Abstract: This paper studies the estimation of cascaded channels in passive intelligent reflective surface (IRS)-aided multiple-input multiple-output (MIMO) systems employing hybrid precoders and combiners. We propose a low-complexity solution that estimates the channel parameters progressively. The angles of departure (AoDs) and angles of arrival (AoAs) at the transmitter and receiver, respectively, are first estimated using inductive matrix completion (IMC) followed by root-MUSIC-based super-resolution spectrum estimation. Forward-backward spatial smoothing (FBSS) is applied to address the coherence issue. Using the estimated AoAs and AoDs, the training precoders and combiners are then optimized and the angle differences between the AoAs and AoDs at the IRS are estimated using the least squares (LS) method followed by FBSS and the root-MUSIC algorithm. Finally, the composite path gains of the cascaded channel are estimated using on-grid sparse recovery with a small-size dictionary. The simulation results suggest that the proposed estimator can achieve improved channel parameter estimation performance with lower complexity as compared to several recently reported alternatives, thanks to the exploitation of the knowledge of the array responses and low-rankness of the channel using low-complexity algorithms at all the stages.
  PubDate: MON, 20 MAR 2023 10:11:10 -04
  Issue No: Vol. 22, No. 11 (2023)
Joint Optimization of Caching Placement and Power Allocation in
Virtualized Satellite-Terrestrial Network
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  Authors: Haijun Zhang;Jiali Xu;Xiangnan Liu;Keping Long;Victor C. M. Leung;
  Pages: 7932 - 7943
  Abstract: With the rapid development of mobile services and applications, the transmitting of massive data makes low-cost communication a challenge. Edge-based wireless communication technology is developed to be a promising approach to satisfy the communication requirements. Edge caching technology is one of effective methods to reduce the overhead of communication system and the pressure of backhauls. In this paper, the joint optimization problem of caching placement and power allocation in virtualized low earth orbit (LEO) satellite-terrestrial networks is proposed, which is based on cooperative caching, by considering cache size limits and power constraints. The optimization problem is solved using an algorithm inspired by the courtship movements and random flights of mayflies. Simulation results show the effectiveness of the proposed scheme in improving system performance and reducing power consumption.
  PubDate: MON, 20 MAR 2023 10:11:10 -04
  Issue No: Vol. 22, No. 11 (2023)
LuMaMi28: Real-Time Millimeter-Wave Multi-User MIMO Systems With Antenna
Selection
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  Authors: Minkeun Chung;Liang Liu;Andreas Johansson;Sara Willhammar;Martin Nilsson;Zhinong Ying;Olof Zander;Kamal Samanta;Chris Clifton;Toshiyuki Koimori;Shinya Morita;Satoshi Taniguchi;Fredrik Tufvesson;Ove Edfors;
  Pages: 7944 - 7960
  Abstract: This paper presents LuMaMi28, a real-time 28 GHz multi-user (MU) multiple-input multiple-output (MIMO) testbed. In this testbed, the base station has 16 transceiver chains with a fully-digital beamforming architecture (with different pre-coding algorithms) and simultaneously supports multiple user equipments (UEs) with spatial multiplexing. The UEs are equipped with a beam-switchable antenna array for real-time antenna selection where the one with the highest channel magnitude, out of four pre-defined beams, is selected. For the beam-switchable antenna array, we consider two kinds of UE antennas, with different beam-width and different peak-gain. Based on this testbed, we provide measurement results for millimeter-wave (mmWave) MU-MIMO performance in different real-life scenarios with static and mobile UEs. We explore the potential benefit of the mmWave MU-MIMO systems with antenna selection based on measured channel data, and discuss the performance results through real-time measurements.
  PubDate: TUE, 28 MAR 2023 10:04:40 -04
  Issue No: Vol. 22, No. 11 (2023)
Bit-Metric Decoding Rate in Multi-User MIMO Systems: Theory
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  Authors: K. Pavan Srinath;Jakob Hoydis;
  Pages: 7961 - 7974
  Abstract: LA is one of the most important aspects of wireless communications where the MCS used by the transmitter is adapted to the channel conditions in order to meet a certain target error-rate. In a SU-SISO system with out-of-cell interference, LA is performed by computing the post-equalization SINR at the receiver. The same technique can be employed in MU-MIMO receivers that use linear detectors. Another important use of post-equalization SINR is for PHY abstraction, where several PHY blocks like the channel encoder, the detector, and the channel decoder are replaced by an abstraction model in order to speed up system-level simulations. However, for MU-MIMO systems with non-linear receivers, there is no known equivalent of post-equalization SINR which makes both LA and PHY abstraction extremely challenging. This important issue is addressed in this two-part paper. In this part, a metric called the BMDR of a detector, which is the proposed equivalent of post-equalization SINR, is presented. Since BMDR does not have a closed form expression that would enable its instantaneous calculation, a machine-learning approach to predict it is presented along with extensive simulation results.
  PubDate: MON, 20 MAR 2023 10:11:10 -04
  Issue No: Vol. 22, No. 11 (2023)
Location Privacy-Aware and Energy-Efficient Offloading for Distributed
Edge Computing
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  Authors: Yulong He;Xiaofan He;Richeng Jin;Huaiyu Dai;
  Pages: 7975 - 7988
  Abstract: Driven by the ever-increasing scale and intensity of the computing tasks arising from various mobile applications, distributed edge computing has fostered wide research interests. It can effectively reduce the task processing delay by partitioning the original large-scale task into several small subtasks and offloading them to multiple edge nodes (ENs) for parallel computing. In edge computing, as the mobile user usually tends to offload computing tasks to closer ENs to save transmit power, the attacker may stealthily infer user location by exploiting this feature. Although there have been some pioneering works on offloading related location privacy, they mainly focused on the scenario where each task can only be offloaded to a single EN, and may not be directly applicable to distributed edge computing. Besides, the privacy issues considered in existing works are mainly based on good heuristics, and there is still a lack of concrete examples of location privacy attacks in edge computing. To the best of our knowledge, the location privacy issue in distributed edge computing still remains largely unexplored in existing literature. With this consideration, a location inference attack based on matrix sequential probability ratio test (MSPRT) is identified in this work. Besides, a countermeasure based on dynamic multi-EN selection is proposed, together with a location privacy-aware and energy-efficient distributed offloading scheme based on the generic Lyapunov optimization framework. Both theoretic analysis and simulations based on real-world channel measurements are employed to validate the feasibility of the identified MPSRT attack and the effectiveness of the proposed defense scheme.
  PubDate: MON, 20 MAR 2023 10:11:10 -04
  Issue No: Vol. 22, No. 11 (2023)
Resident Population Density-Inspired Deployment of K-Tier Aerial Cellular
Network
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  Authors: Ruibo Wang;Mustafa A. Kishk;Mohamed-Slim Alouini;
  Pages: 7989 - 8002
  Abstract: Using Unmanned Aerial Vehicles (UAVs) to enhance network coverage has proven a variety of benefits compared to terrestrial counterparts. One of the commonly used mathematical tools to model the locations of the UAVs is stochastic geometry (SG). However, in the existing studies, both users and UAVs are often modeled as homogeneous point processes. In this paper, we consider an inhomogeneous Poisson point process (PPP)-based model for the locations of the users that captures the degradation in the density of active users as we move away from the town center. In addition, we propose the deployment of aerial vehicles following the same inhomogeneity of the users to maximize the performance. In addition, a multi-tier network model is also considered to make better use of the rich space resources. Then, the analytical expressions of the coverage probability for a typical user and the total coverage probability are derived. Finally, we optimize the coverage probability with limitations of the total number of UAVs and the minimum local coverage probability. Finally we give the optimal UAV distribution parameters when the maximum overall coverage probability is reached.
  PubDate: MON, 20 MAR 2023 10:11:10 -04
  Issue No: Vol. 22, No. 11 (2023)
Analysis of Age of Information in Dual Updating Systems
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  Authors: Zhengchuan Chen;Dapeng Deng;Howard H. Yang;Nikolaos Pappas;Limei Hu;Yunjian Jia;Min Wang;Tony Q. S. Quek;
  Pages: 8003 - 8019
  Abstract: We study the average Age of Information (AoI) and peak AoI (PAoI) of a dual-queue status update system that monitors a common stochastic process through two independent channels. Although the double queue parallel transmission is instrumental in reducing AoI, the out of order of data arrivals also imposes a significant challenge to the performance analysis. We consider two settings: the M-M system where the service time of two servers is exponentially distributed; the M-D system in which the service time of one server is exponentially distributed and that of the other is deterministic. For the two dual-queue systems, closed-form expressions of average AoI and PAoI are derived by resorting to the graphic method and state flow graph analysis method. Our analysis reveals that when the two servers have the same service rate, compared with the single-queue system with an exponentially distributed service time, the average PAoI and the average AoI of the M-M system decrease by 33.3% and 37.5%, respectively, and those of the M-D system decrease by 27.7% and 39.7%, respectively. Numerical results show that the two dual-queue systems also outperform the M/M/2 single queue dual-server system with optimized arrival rate in terms of average AoI and PAoI.
  PubDate: TUE, 21 MAR 2023 10:04:17 -04
  Issue No: Vol. 22, No. 11 (2023)
Distributed Learning Over a Wireless Network With Non-Coherent Majority
Vote Computation
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  Authors: Alphan Şahin;
  Pages: 8020 - 8034
  Abstract: In this study, we propose an over-the-air computation (OAC) scheme to calculate the majority vote (MV) for federated edge learning (FEEL). With the proposed approach, edge devices (EDs) transmit the signs of local stochastic gradients, i.e., votes, by activating one of two orthogonal resources. The MVs at the edge server (ES) are obtained with non-coherent detectors by exploiting the accumulations on the resources. Hence, the proposed scheme eliminates the need for channel state information (CSI) at the EDs and ES. In this study, we analyze various gradient-encoding strategies through the weight functions and waveform configurations over orthogonal frequency division multiplexing (OFDM). We show that specific weight functions that enable absentee EDs (i.e., hard-coded participation with absentees (HPA)) or weighted votes (i.e., soft-coded participation (SP)) can substantially reduce the probability of detecting the incorrect MV. By taking path loss, power control, cell size, and fading channel into account, we prove the convergence of the distributed learning for a non-convex function for HPA. Through simulations, we show that the proposed scheme with HPA and SP can provide high test accuracy even when the time-synchronization and the power control are not ideal under heterogeneous data distribution scenarios.
  PubDate: TUE, 21 MAR 2023 10:04:17 -04
  Issue No: Vol. 22, No. 11 (2023)
Cell-Free Networking for Integrated Data and Energy Transfer: Digital Twin
Based Double Parameterized DQN for Energy Sustainability
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  Authors: Tingyu Shui;Jie Hu;Kun Yang;Honghui Kang;Hua Rui;Bo Wang;
  Pages: 8035 - 8049
  Abstract: Cell-free networking enables full cooperation among distributed access points (APs). This paper focuses on reducing the long-term energy consumption of a cell-free network in the downlink integrated data and energy transfer (IDET) for achieving energy sustainability. The resultant design includes both the AP classification on a large time-scale and the beamforming of the APs on a small time-scale in order to simultaneously satisfy the IDET requirements of data users and energy users. For dealing with binary integer actions (AP classification) and continuous actions (beamforming) together, we innovatively propose a stable double parameterized deep-Q-network (DP-DQN), which can be enhanced by a digital twin (DT) running in the intelligent core processor (ICP) so as to achieve faster and more stable convergence. Therefore, the cell-free network may avoid suffering from performance fluctuation during the training process. The simulation results demonstrate that our DP-DQN exceeds in convergence compared to other benchmarks while guaranteeing an optimal solution.
  PubDate: WED, 22 MAR 2023 10:03:09 -04
  Issue No: Vol. 22, No. 11 (2023)
A Novel 6G ISAC Channel Model Combining Forward and Backward Scattering
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  Authors: Runruo Yang;Cheng-Xiang Wang;Jie Huang;El-Hadi M. Aggoune;Yang Hao;
  Pages: 8050 - 8065
  Abstract: The integrated sensing and communication (ISAC) refers to the integration of radio sensing and wireless communications to realize the multiplexing of space, time, and frequency resources. In the sixth generation (6G) wireless networks, ISAC is considered as one of the most promising technologies. In this paper, a novel ISAC channel model combining forward and backward scattering is proposed. In addition to the non-stationarity caused by motions, the correlations between sensing and communication channels are investigated. The channel characteristics of sensing such as forward scattering and backward scattering are introduced into the communication channel model. Utilizing the correlations between sensing and communication channels, the communication channel model is divided into line-of-sight (LOS), forward scattering, and backward scattering components. These three components are summed according to probability weighting to obtain a more accurate channel model for sensing assisted communication systems. Moreover, important statistical properties of the proposed ISAC channel model are derived and simulated. The analytical and simulation results match well, demonstrating the correctness of derivations and simulations. Some derived/simulated statistical properties are verified by corresponding measurement data, which indicates the utility of the proposed ISAC channel model.
  PubDate: WED, 22 MAR 2023 10:03:09 -04
  Issue No: Vol. 22, No. 11 (2023)
PRINCE: A Pruned AMP Integrated Deep CNN Method for Efficient Channel
Estimation of Millimeter-Wave and Terahertz Ultra-Massive MIMO Systems
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  Authors: Zhengdong Hu;Yuhang Chen;Chong Han;
  Pages: 8066 - 8079
  Abstract: Millimeter-wave (mmWave) and Terahertz (THz)-band communications exploit the abundant bandwidth to fulfill the increasing data rate demands of 6G wireless communications. To compensate for the high propagation loss with reduced hardware costs, ultra-massive multiple-input multiple-output (UM-MIMO) with a hybrid beamforming structure is a promising technology in the mmWave and THz bands. However, channel estimation (CE) is challenging for hybrid UM-MIMO systems, which requires recovering the high-dimensional channels from severely few channel observations. In this paper, a Pruned Approximate Message Passing (AMP) Integrated Deep Convolutional-neural-network (DCNN) CE (PRINCE) method is firstly proposed, which enhances the estimation accuracy of the AMP method by appending a DCNN network. Moreover, by truncating the insignificant feature maps in the convolutional layers of the DCNN network, a pruning method including training with regularization, pruning and refining procedures is developed to reduce the network scale. Simulation results show that the PRINCE achieves a good trade-off between the CE accuracy and significantly low complexity, with normalized-mean-square-error (NMSE) of −10 dB at signal-to-noise-ratio (SNR) as 10 dB after eliminating 80% feature maps.
  PubDate: WED, 22 MAR 2023 10:03:09 -04
  Issue No: Vol. 22, No. 11 (2023)
Joint User-Side Recommendation and D2D-Assisted Offloading for
Cache-Enabled Cellular Networks With Mobility Consideration
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  Authors: Meiyan Song;Hangguan Shan;Yaru Fu;Howard H. Yang;Fen Hou;Wei Wang;Tony Q. S. Quek;
  Pages: 8080 - 8095
  Abstract: Caching at the wireless edge is recognized as a promising solution to accommodate the explosive growth of traffic demand. However, the gain of edge caching is only pronounced given homogeneous user preference. To reap the full potential of caching, recommendation mechanism has emerged as an attractive technology due to its capability of reshaping users’ request distribution. In this work, we propose a joint user-side recommendation and device-to-device (D2D)-assisted offloading strategy, aiming to maximize the operator’s utility. Specifically, we consider that users can recommend their cached contents to encountered users. This strategy takes into account users’ personalized preferences and relative locations, and hence can directly offload the recommended contents through D2D links without burdening cellular links. We then develop a theoretical framework to evaluate the subsequent content transmission, accounting for the randomness of spatial deployment, user mobility, individual delay requirement, incentive, and protection mechanism for existing links. Based on the analytical results, we design a D2D-assisted offloading strategy, which allows the requester to postpone data reception in exchange for discounted service fees. Simulation results show that the operator’s utility can be significantly improved. Particularly, it is found that user mobility facilitates the above process.
  PubDate: THU, 23 MAR 2023 10:02:59 -04
  Issue No: Vol. 22, No. 11 (2023)
Aerial Bridge: A Secure Tunnel Against Eavesdropping in
Terrestrial-Satellite Networks
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  Authors: Qubeijian Wang;Hao Wang;Wen Sun;Nan Zhao;Hong-Ning Dai;Wei Zhang;
  Pages: 8096 - 8113
  Abstract: Terrestrial-satellite networks (TSNs) can provide worldwide users with ubiquitous and seamless network services. Meanwhile, malicious eavesdropping is posing tremendous challenges on secure transmissions of TSNs due to their widescale wireless coverage. In this paper, we propose an aerial bridge scheme to establish secure tunnels for legitimate transmissions in TSNs. With the assistance of unmanned aerial vehicles (UAVs), massive transmission links in TSNs can be secured without impacts on legitimate communications. Owing to the stereo position of UAVs and the directivity of directional antennas, the constructed secure tunnel can significantly relieve confidential information leakage, resulting in the precaution of wiretapping. Moreover, we establish a theoretical model to evaluate the effectiveness of the aerial bridge scheme compared with the ground relay, non-protection, and UAV jammer schemes. Furthermore, we conduct extensive simulations to verify the accuracy of theoretical analysis and present useful insights into the practical deployment by revealing the relationship between the performance and other parameters, such as the antenna beamwidth, flight height and density of UAVs.
  PubDate: THU, 23 MAR 2023 10:02:59 -04
  Issue No: Vol. 22, No. 11 (2023)
Hierarchical Deep Reinforcement Learning for Age-of-Information
Minimization in IRS-Aided and Wireless-Powered Wireless Networks
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  Authors: Shimin Gong;Leiyang Cui;Bo Gu;Bin Lyu;Dinh Thai Hoang;Dusit Niyato;
  Pages: 8114 - 8127
  Abstract: In this paper, we focus on a wireless-powered sensor network coordinated by a multi-antenna access point (AP). Each node can generate sensing information and report the latest information to the AP using the energy harvested from the AP’s signal beamforming. We aim to minimize the average age-of-information (AoI) by adapting the nodes’ scheduling and the transmission control strategies jointly. To reduce the transmission delay, an intelligent reflecting surface (IRS) is used to enhance the channel conditions by controlling the AP’s beamforming strategy and the IRS’s phase shifting matrix. Considering dynamic data arrivals at different sensing nodes, we propose a hierarchical deep reinforcement learning (DRL) framework for AoI minimization in two steps. The users’ transmission scheduling is firstly determined by the outer-loop DRL approach, e.g. the DQN or PPO algorithm, and then the inner-loop optimization is used to adapt either the uplink information transmission or downlink energy transfer to all nodes. A simple and efficient approximation is also proposed to reduce the inner-loop rum time overhead. Numerical results verify that the hierarchical learning framework outperforms typical baselines in terms of the average AoI and proportional fairness among different nodes.
  PubDate: MON, 27 MAR 2023 10:09:58 -04
  Issue No: Vol. 22, No. 11 (2023)
Antenna Selections Strategies for Massive MIMO Systems With
Limited-Resolution ADCs/DACs
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  Authors: Shiguo Wang;Min Zhu;Zhetao Li;Liang Yang;Cheng-Xiang Wang;Rukhsana Ruby;
  Pages: 8128 - 8140
  Abstract: In millimeter wave (mmWave) communication systems with massive multiple-input multiple-output (MIMO) architecture, selecting the antennas contributing most from the candidate array to transmit/receive signals is one of the effective solutions to reduce hardware cost and power consumption while maintaining high spectral efficiency. In this paper, for the communication systems where the base station (BS) equipped with massive MIMO antenna array communicates with multiple single-antenna users, the impact of limited-resolution analog-to-digital converters (ADCs) and digital-to-analog converters (DACs) on system capacity is investigated, and two antenna selection (AS) algorithms, namely quantization-aware greedy with square maximum-volume (QAG-SMV) and group-selection (GS) schemes, are proposed to enhance system capacity for the uplink and downlink transmission, respectively. Specifically, after the quantization noise caused by limited-resolution ADCs/DACs is converted to independent additive noise, the problem of maximizing system capacity is formulated. Then, two novel AS schemes are proposed to improve system capacity. Simulation results show that the proposed AS algorithms can obtain higher average system capacity, and the computational complexity is reduced as well.
  PubDate: MON, 10 APR 2023 06:46:05 -04
  Issue No: Vol. 22, No. 11 (2023)
Hierarchical Personalized Federated Learning Over Massive Mobile Edge
Computing Networks
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  Authors: Chaoqun You;Kun Guo;Howard H. Yang;Tony Q. S. Quek;
  Pages: 8141 - 8157
  Abstract: Personalized Federated Learning (PFL) is a new Federated Learning (FL) paradigm, particularly tackling the heterogeneity issues brought by various mobile user equipments (UEs) in mobile edge computing (MEC) networks. However, due to the ever-increasing number of UEs and the complicated administrative work it brings, it is desirable to switch the PFL algorithm from its conventional two-layer framework to a multiple-layer one. In this paper, we propose hierarchical PFL (HPFL), an algorithm for deploying PFL over massive MEC networks. The UEs in HPFL are divided into multiple clusters, and the UEs in each cluster forward their local updates to the edge server (ES) synchronously for edge model aggregation, while the ESs forward their edge models to the cloud server semi-asynchronously for global model aggregation. The above training manner leads to a tradeoff between the training loss in each round and the round latency. HPFL combines the objectives of training loss minimization and round latency minimization while jointly determining the optimal bandwidth allocation as well as the ES scheduling policy in the hierarchical learning framework. Extensive experiments verify that HPFL not only guarantees convergence in hierarchical aggregation frameworks but also has advantages in round training loss maximization and round latency minimization.
  PubDate: THU, 30 MAR 2023 10:03:02 -04
  Issue No: Vol. 22, No. 11 (2023)
Deep Reinforcement Learning Based Resource Allocation and Trajectory
Planning in Integrated Sensing and Communications UAV Network
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  Authors: Yunhui Qin;Zhongshan Zhang;Xulong Li;Wei Huangfu;Haijun Zhang;
  Pages: 8158 - 8169
  Abstract: In this paper, multi-UAVs serve as mobile aerial ISAC platforms to sense and communicate with on-ground target users. To optimize the communication and sensing performance, we formulate a joint user association, UAV trajectory planning and power allocation problem to maximize the minimum weighted spectral efficiency among UAVs. This paper exploits the centralized and the decentralized deep reinforcement learning (DRL) solutions to solve the sequential decision-making problem. On one hand, we first introduce the centralized soft actor-critic (SAC) algorithm. Then, we explore the equivalent transformation of the optimization objective based on symmetric group, propose the random and the adaptive data augmentation schemes to design the replay memory buffer of SAC, and accordingly propose SAC algorithms assisted by data augmentation to tackle the transformed problem. On the other hand, the multi-agent soft actor-critic (MASAC), a decentralized solution, is also introduced to solve this sequential decision-making problem. The experiment results reveal the effectiveness of the centralized and the decentralized solutions in considered scenarios. Specifically, the SAC assisted by the adaptive scheme significantly outperforms other centralized solutions in the training speed and the weighted spectral efficiency. Meanwhile, the decentralized MASAC algorithm behaves best in the early training speed.
  PubDate: TUE, 28 MAR 2023 10:04:40 -04
  Issue No: Vol. 22, No. 11 (2023)
Modeling and Analysis of Air-Ground Integrated Networks With Flexible Beam
Coverage
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  Authors: Na Deng;Haichao Wei;Martin Haenggi;
  Pages: 8170 - 8184
  Abstract: Air platforms, such as unmanned aerial vehicles, airships, and balloons are expected to complement traditional ground networks to provide flexible coverage solutions. However, most existing models for air-ground integrated networks (AGINs) neglect the spatial dependence caused by the complementary deployment of the aerial and ground nodes. Accordingly, in this paper, we propose two AGIN models with horizontal dependence that differ in the vertical dimension, namely uniformly independent altitudes and location-dependent altitudes. The air platforms serve as aerial base stations, distributed as a marked Poisson hole process, and provide flexible beam coverage through varying altitudes. Under this setup, we propose a region-based user association scheme and derive the association probabilities as well as the serving distance distributions of an arbitrarily located user. Considering Nakagami fading and air-to-ground propagation properties, we characterize the signal-to-interference ratio and area spectral efficiency for each model. Using the proposed analytical framework, we demonstrate the importance of deploying the air platforms more sensibly to provide targeted services and flexible beam coverage in reducing the load of base stations and improving the user coverage and network capacity performance.
  PubDate: TUE, 28 MAR 2023 10:04:40 -04
  Issue No: Vol. 22, No. 11 (2023)
Joint VNF Parallelization and Deployment in Mobile Edge Networks
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  Authors: Fengsen Tian;Junbin Liang;Jiangchuan Liu;
  Pages: 8185 - 8199
  Abstract: Mobile edge computing (MEC) has emerged as a promising computing paradigm that provides flexible and responsive local services for mobile user equipment at the network edge. Software instances for user equipment tasks are typically deployed as Virtualized Network Functions (VNFs) at resource-constrained edge nodes. Task data exchanged across the VNFs in serial can incur high task completion latency. It is therefore desirable to deploy certain VNFs in parallel. However, deciding where to deploy VNFs depends on which VNFs are parallel, and conversely, their deployment also affects their parallel execution. In this paper, for the first time, we jointly consider the parallelization and deployment strategies for VNFs at edge nodes. We closely examine the complexity of the joint optimization problem and introduce an Improved Service Function Graph (I-SFG) that reflects the coordination and dependency relations among the VNFs to provide parallel services for each piece of user equipment. We first propose an approach based on integer linear programming to find optimal solutions in small-scale scenarios and then present an effective solution through cascading I-SFG construction and VNF deployment approximation to solve large-scale problems. Theoretical analyses and experimental results show the superiority of our joint design and the proposed practical solution.
  PubDate: TUE, 28 MAR 2023 10:04:40 -04
  Issue No: Vol. 22, No. 11 (2023)
Joint Base Station and IRS Deployment for Enhancing Network Coverage: A
Graph-Based Modeling and Optimization Approach
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  Authors: Weidong Mei;Rui Zhang;
  Pages: 8200 - 8213
  Abstract: Intelligent reflecting surface (IRS) can be densely deployed in complex environment to create cascaded line-of-sight (LoS) paths between multiple base stations (BSs) and users via tunable IRS reflections, thereby significantly enhancing the coverage performance of wireless networks. To achieve this goal, it is vital to optimize the deployed locations of BSs and IRSs in the wireless network, which is investigated in this paper. Specifically, we divide the coverage area of the network into multiple non-overlapping cells and decide whether to deploy a BS/IRS in each cell given a total number of BSs/IRSs available. We show that to ensure the network coverage/communication performance, i.e., each cell has a direct/cascaded LoS path with at least one BS, as well as such LoS paths have the average number of IRS reflections less than a given threshold, there is a fundamental trade-off with the deployment cost or the number of BSs/IRSs needed. To optimally characterize this trade-off, we formulate a joint BS and IRS deployment problem based on graph theory, which, however, is difficult to be optimally solved due to the combinatorial optimization involved. To circumvent this difficulty, we first consider a simplified problem with given BS deployment and propose the optimal as well as an efficient suboptimal IRS deployment solution to it, by applying the branch-and-bound method and iteratively removing IRSs from the candidate locations, respectively. Next, an efficient sequential update algorithm is proposed for solving the joint BS and IRS deployment problem. Numerical results are provided to show the efficacy of the proposed design approach and optimization algorithms for the joint BS and IRS deployment. The trade-off between the network coverage performance and the number of deployed BSs/IRSs with different cost ratios is also unveiled.
  PubDate: TUE, 28 MAR 2023 10:04:40 -04
  Issue No: Vol. 22, No. 11 (2023)
Affine Frequency Division Multiplexing for Next Generation Wireless
Communications
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  Authors: Ali Bemani;Nassar Ksairi;Marios Kountouris;
  Pages: 8214 - 8229
  Abstract: Affine Frequency Division Multiplexing (AFDM), a new chirp-based multicarrier waveform for high mobility communications, is introduced here. AFDM is based on discrete affine Fourier transform (DAFT), a generalization of discrete Fourier transform, which is characterized by two parameters that can be adapted based on the Doppler spread of doubly dispersive channels. First, we derive the explicit input-output relation in the DAFT domain showing the effect of AFDM parameters in the input-output relation. Second, we show how the DAFT parameters underlying AFDM have to be set so that the resulting DAFT domain impulse response conveys a full delay-Doppler representation of the channel. Then, we show analytically that AFDM can achieve the optimal diversity order in doubly dispersive channels, where optimal diversity order refers to the number of multipath components separable in either the delay or the Doppler domain, due to its full delay-Doppler representation. Furthermore, we present a low complexity detection method taking advantage of zero-padding. We also propose an embedded pilot-aided channel estimation scheme for AFDM, in which both channel estimation and data detection are performed within the same AFDM frame. Finally, simulations corroborate the validity of our analytical results and show the significant performance gains of AFDM over state-of-the-art multicarrier schemes in high mobility scenarios.
  PubDate: WED, 29 MAR 2023 10:03:27 -04
  Issue No: Vol. 22, No. 11 (2023)
Joint Optimization of Sensing and Computation for Status Update in Mobile
Edge Computing Systems
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  Authors: Yi Chen;Zheng Chang;Geyong Min;Shiwen Mao;Timo Hämäläinen;
  Pages: 8230 - 8243
  Abstract: IoT devices have been widely utilized to detect state transition in the surrounding environment and transmit status updates to the base station for system operations. To guarantee the accuracy of system control, age of information (AoI) is introduced to quantify the freshness of the sensory data and meet the stringent timeliness requirement. Due to the limited computing resources, the status update can be offloaded to the mobile edge computing (MEC) server for execution. Since status updates generated by insufficient sensing operations may be invalid and lead to additional processing time, a joint data sensing and processing optimization problem needs to be considered. Therefore, this work formulates an NP-hard problem that considers the freshness of the status updates and energy consumption of the IoT devices. Subsequently, the problem is decomposed into sampling, sensing, and computation offloading optimization problems. To optimize the system overhead, a multi-variable iterative system cost minimization algorithm is proposed. Simulation results illustrate the efficacy of our method in decreasing the system cost, and indicate the influence of sensing and processing under different scenarios.
  PubDate: THU, 30 MAR 2023 10:03:02 -04
  Issue No: Vol. 22, No. 11 (2023)
OFDM-Based Massive Connectivity for LEO Satellite Internet of Things
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  Authors: Yong Zuo;Mingyang Yue;Mingchen Zhang;Sixian Li;Shaojie Ni;Xiaojun Yuan;
  Pages: 8244 - 8258
  Abstract: Low earth orbit (LEO) satellite has been considered as a potential supplement for the terrestrial Internet of Things (IoT). In this paper, we consider grant-free non-orthogonal random access (GF-NORA) in the orthogonal frequency division multiplexing (OFDM) system to increase access capacity and reduce access latency for LEO satellite-IoT. We focus on the joint device activity detection (DAD) and channel estimation (CE) problem at the satellite access point. The delay and the Doppler effect of the LEO satellite channel are assumed to be partially compensated. We propose an OFDM-symbol repetition technique to better distinguish the residual Doppler frequency shifts, and present a grid-based parametric probability model to characterize channel sparsity in the delay-Doppler-user domain, as well as to characterize the relationship between the channel states and the device activity. Based on that, we develop a robust Bayesian message-passing algorithm named modified variance state propagation (MVSP) for joint DAD and CE. Moreover, to tackle the mismatch between the real channel and its on-grid representation, an expectation–maximization (EM) framework is proposed to learn the grid parameters. Simulation results demonstrate that our proposed algorithms significantly outperform the existing approaches in both activity detection probability and channel estimation accuracy.
  PubDate: THU, 30 MAR 2023 10:03:02 -04
  Issue No: Vol. 22, No. 11 (2023)
Data Augmentation for Deep Receivers
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  Authors: Tomer Raviv;Nir Shlezinger;
  Pages: 8259 - 8274
  Abstract: Deep neural networks (DNNs) allow digital receivers to learn to operate in complex environments. To do so, DNNs should preferably be trained using large labeled data sets with a similar statistical relationship as the one under which they are to infer. For DNN-aided receivers, obtaining labeled data conventionally involves pilot signalling at the cost of reduced spectral efficiency, typically resulting in access to limited data sets. In this paper, we study how one can enrich a small set of labeled pilots data into a larger data set for training deep receivers. Motivated by the widespread use of data augmentation techniques for enriching visual and text data, we propose dedicated augmentation schemes that exploits the characteristics of digital communication data. We identify the key considerations in data augmentations for deep receivers as the need for domain orientation, class (constellation) diversity, and low complexity. Following these guidelines, we devise three complementing augmentations that exploit the geometric properties of digital constellations. Our combined augmentation approach builds on the merits of these different augmentations to synthesize reliable data from a momentary channel distribution, to be used for training deep receivers. Furthermore, we exploit previous channel realizations to increase the reliability of the augmented samples. The superiority of our approach is numerically evaluated for training several deep receiver architectures in different channel conditions. We consider both linear and non-linear synthetic channels, as well as the COST 2100 channel generator, for both single-input single-output and multiple-input multiple-output scenarios. We show that our combined augmentations approach allows DNN-aided receivers to achieve gains of up to 1 dB in bit error rate and of up to $\times 3$ in spectral efficiency, compared to regular non-augmented training. Moreover, we demonstrate that our augmentations benefit training even as the number of pilots increases, and perform an ablation study on the different augmentations, which shows that the combined approach surpasses each individual augmentation technique.
  PubDate: THU, 30 MAR 2023 10:03:02 -04
  Issue No: Vol. 22, No. 11 (2023)
An Efficient Two-Stage SPARC Decoder for Massive MIMO Unsourced Random
Access
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  Authors: Juntao You;Wenjie Wang;Shansuo Liang;Wei Han;Bo Bai;
  Pages: 8275 - 8289
  Abstract: In this paper, we study a concatenate coding scheme based on sparse regression code (SPARC) and tree code for unsourced random access in massive multiple-input and multiple-output systems. Our focus is concentrated on efficient decoding for the inner SPARC with practical concerns. A two-stage method is proposed to achieve near-optimal performance while maintaining low computational complexity. Specifically, a one-step thresholding-based algorithm is first used for reducing large dimensions of the SPARC decoding, after which a relaxed maximum-likelihood estimator is employed for refinement. Adequate simulation results are provided to validate the near-optimal performance and the low computational complexity. Besides, for covariance-based sparse recovery method, theoretical analyses are given to characterize the upper bound of the number of active users supported when convex relaxation is considered, and the probability of successful dimension reduction by the one-step thresholding-based algorithm.
  PubDate: FRI, 31 MAR 2023 10:04:25 -04
  Issue No: Vol. 22, No. 11 (2023)
Hybrid Far- and Near-Field Modeling for Reconfigurable Intelligent Surface
Assisted V2V Channels: A Sub-Array Partition Based Approach
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  Authors: Hao Jiang;Baiping Xiong;Hongming Zhang;Ertugrul Basar;
  Pages: 8290 - 8303
  Abstract: Reconfigurable intelligent surface (RIS)-assisted communications has been a hot topic due to its promising advantages for future wireless networks. Existing works on RIS-assisted channel modeling have mainly focused on far-field propagation condition with planar wavefront assumption. In essence, the far-field condition does not always hold because the RIS array dimension may be comparable to the terminal distance, especially in RIS-assisted mobile networks. To this end, we propose a hybrid far- and near-field stochastic channel model for characterizing a RIS-assisted vehicle-to-vehicle (V2V) propagation environment, which takes into account both far-field and near-field propagation conditions. To achieve the balance between the modeling accuracy and complexity for the investigation of the RIS-assisted V2V propagation characteristics, we develop a sub-array partitioning scheme to dynamically divide the entire RIS array into several smaller sub-arrays, which makes planar wavefront assumption applicable for the sub-arrays. Important channel statistical properties, including spatial cross-correlation functions (CCFs), temporal auto-correlation functions (ACFs), and frequency correlation functions (FCFs), are derived and investigated. Simulation results are provided to show the performance of the proposed sub-array partition based hybrid far- and near-field modeling solution for RIS-assisted V2V channels.
  PubDate: FRI, 31 MAR 2023 10:04:25 -04
  Issue No: Vol. 22, No. 11 (2023)
Channel Customization for Joint Tx-RISs-Rx Design in Hybrid mmWave Systems
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  Authors: Weicong Chen;Chao-Kai Wen;Xiao Li;Shi Jin;
  Pages: 8304 - 8319
  Abstract: In strong line-of-sight millimeter-wave (mmWave) wireless systems, the rank-deficient channel severely hampers spatial multiplexing. To address this inherent deficiency, multiple reconfigurable-intelligent-surfaces (RISs) are introduced in this study to customize the wireless channel. Utilizing the RIS to reshape electromagnetic waves, we theoretically show that a favorable channel with an arbitrary tunable rank and a minimized truncated condition number can be established by elaborately designing the placement and reflection matrix of RISs. Different from existing works on multi-RISs, the number of elements needed for each RIS to combat the path loss and the limited phase control is also considered. On the basis of the proposed channel customization, a joint transmitter-RISs-receiver (Tx-RISs-Rx) design under a hybrid mmWave system is investigated to maximize the spectral efficiency. Using the proposed scheme, the optimal singular value decomposition-based hybrid beamforming at the Tx and Rx can be obtained without matrix decomposition for the digital and analog beamforming. The bottoms of the sub-channel mode in the water-filling algorithm, which are conventionally uncontrollable, are proven to be independently adjustable by RISs. Moreover, the transmit power required for realizing multi-stream transmission is derived. Numerical results are presented to verify our theoretical analysis and exhibit substantial gains over systems without RISs.
  PubDate: TUE, 04 APR 2023 10:04:28 -04
  Issue No: Vol. 22, No. 11 (2023)
Toward Fast and Energy-Efficient Access to Cloudlets in Hostile
Environments
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  Authors: Jie Wang;Sigit Aryo Pambudi;Wenye Wang;Cliff Wang;
  Pages: 8320 - 8335
  Abstract: Cloudlets, which refer to the edge computing services deployed at the proximity of end devices, are key providers of connectivity, storage, and computation resources to many applications. While access to cloudlets is pervasive in typical settings, it can be difficult in challenging, even hostile environments, such as military or post-disaster scenarios, featuring multi-hop communication and energy-constrained end devices. In these cases, cloudlets may have become the only equipment powerful enough to execute life-critical applications, such as battle-field situation awareness, tactic cooperation, and search-and-rescue missions. Quality of these services is greatly influenced by the minimum time that a packet can be delivered, i.e., the cloudlet access delay (CAD), whose characteristics remain unknown. To address the open question of fast and efficient cloudlet access, we establish a packet mobility model that allows CAD and energy consumption to be analyzed as a function of the initial device-cloudlet distance. We find that the expected CAD scales either linearly or quadratically under distinct types of packet mobility, and the successful access rate (SAR) can be bounded by functions of the delay constraint. Based on these findings, we develop a packet shedding algorithm that saves 24% transmission power, and reduces the average CAD by 2%, while maintaining a similar SAR in simulated cloudlet access environments.
  PubDate: MON, 03 APR 2023 10:06:42 -04
  Issue No: Vol. 22, No. 11 (2023)
Quantization Bits Allocation for Wireless Federated Learning
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  Authors: Muhang Lan;Qing Ling;Song Xiao;Wenyi Zhang;
  Pages: 8336 - 8351
  Abstract: Federated learning (FL) enables multiple clients to collaborate on a common learning task via only exchanging model updates. With the progressive improvements in deep learning models, communication is becoming a primary bottleneck of FL. Quantization of model updates before transmitting is an effective technique to reduce communication overhead. Most prior literature assumes lossless transmission, but in practice, quantized model updates are distorted by wireless channels due to the variation of client locations. Therefore, this paper focuses on analysis and design of personalized model update quantization with explicitly incorporating channel diversity in wireless FL. We present a novel convergence analysis of quantized FL, which encompasses full and partial client participation, single and multiple local training iterations, and convex and non-convex loss functions. This analysis explicitly embodies the impact of personalized quantization error, channel diversity and model aggregation in FL, and also elucidates their tradeoff on tightening a convergence rate upper bound. An optimization framework, which seeks an optimal allocation scheme given a total budget of quantization bits, is proposed by minimizing an upper bound with respect to channel quality. A nearly optimal solution is derived for this non-convex integer programming problem via analytically solving Karush–Kuhn–Tucker (KKT) optimality conditions and linear search. From a perspective of outlier detection, this channel-aware allocation scheme is also extended to robust model aggregation against client dropouts. Comprehensive numerical evaluation demonstrates the performance enhancement of the proposed scheme over the vanilla allocation scheme with equal quantization bits, particularly in terms of training stability, test accuracy, and robustness.
  PubDate: MON, 03 APR 2023 10:06:42 -04
  Issue No: Vol. 22, No. 11 (2023)
Model-Based Deep Learning Receiver Design for Rate-Splitting Multiple
Access
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  Authors: Rafael Cerna Loli;Onur Dizdar;Bruno Clerckx;Cong Ling;
  Pages: 8352 - 8365
  Abstract: Effective and adaptive interference management is required in next generation wireless communication systems. To address this challenge, Rate-Splitting Multiple Access (RSMA), relying on multi-antenna rate-splitting (RS) at the transmitter and successive interference cancellation (SIC) at the receivers, has been intensively studied in recent years, albeit mostly under the assumption of perfect Channel State Information at the Receiver (CSIR) and ideal capacity-achieving modulation and coding schemes. To assess its practical performance, benefits, and limits under more realistic conditions, this work proposes a novel design for a practical RSMA receiver based on model-based deep learning (MBDL) methods, which aims to unite the simple structure of the conventional SIC receiver and the robustness and model agnosticism of deep learning techniques. The MBDL receiver is evaluated in terms of uncoded Symbol Error Rate (SER), throughput performance through Link-Level Simulations (LLS), and average training overhead. Also, a comparison with the SIC receiver, with perfect and imperfect CSIR, is given. Results reveal that the MBDL receiver outperforms by a significant margin the SIC receiver with imperfect CSIR, due to its ability to generate on demand non-linear symbol detection boundaries in a pure data-driven manner.
  PubDate: MON, 03 APR 2023 10:06:42 -04
  Issue No: Vol. 22, No. 11 (2023)
A Two-Dimensional FFT Precoded Filter Bank Scheme
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  Authors: Rogério Pereira Junior;Carlos Aurélio Faria da Rocha;Bruno S. Chang;Didier Le Ruyet;
  Pages: 8366 - 8377
  Abstract: This work proposes a new precoded filter bank (FB) system via a two-dimensional (2D) fast Fourier transform (2D-FFT). Its structure is similar to Orthogonal Time Frequency Space (OTFS) systems, where the OFDM transmitter is changed to a filter bank multi-carrier (FBMC) one, thus obtaining a lower out-of-band emission. The complex orthogonality of the FBMC transmission is guaranteed by using pre-coding based on a discrete Fourier transform, which is also used to implement the two-dimensional fast Fourier transform. Furthermore, due to the filtering process, the proposed system obtains a good error performance using a simple equalizer in the time-frequency domain. In this sense, we also introduce a hybrid receiver in the proposed system to exploit this feature. First, the time-frequency domain equalization is performed, followed by an interference cancellation on the delay-Doppler domain. Using the delay-Doppler domain detection, the simulation results show that the proposed system obtains an error performance similar to the OTFS system. Already using the simple equalizer in the time-frequency domain, the proposed scheme presents great advantage compared to the OTFS with the same equalizer in the studied scenarios.
  PubDate: MON, 03 APR 2023 10:06:42 -04
  Issue No: Vol. 22, No. 11 (2023)
Joint Direct and Indirect Channel Estimation for RIS-Assisted
Millimeter-Wave Systems Based on Array Signal Processing
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  Authors: Song Noh;Kyungsik Seo;Youngchul Sung;David J. Love;Junse Lee;Heejung Yu;
  Pages: 8378 - 8391
  Abstract: Reconfigurable intelligent surface (RIS)-assisted millimeter wave (mmWave) communication is a promising technology for enlarging the coverage area of millimeter wave systems. Unfortunately, realizing the full potential of these systems requires addressing numerous challenges in channel estimation. In this paper, channel estimation for RIS-assisted mmWave communications is considered. Under the assumption that the array manifolds of the base station antennas and the RIS reflecting elements are given by uniform arrays, an efficient two-stage channel estimation method based on array signal processing techniques is proposed. In the proposed algorithm, the direct and indirect channels are jointly estimated by space-time processing that exploits the sparsity in RIS-assisted mmWave channels and the features associated with uniform arrays. Then, several practical issues, including detection of the number of channel paths, imperfect RIS hardware, and complexity, are addressed. Extensions to the cases of uniform planar array-based RIS, wideband communication, and multiple users are also discussed. Numerical results validate the effectiveness of the proposed method.
  PubDate: FRI, 07 APR 2023 10:02:58 -04
  Issue No: Vol. 22, No. 11 (2023)
Intelligent Ultra-Reliable and Low Latency Communications: Security and
Flexibility
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  Authors: Jingxuan Zhang;Xiaodong Xu;Shujun Han;Kangjie Zhang;Ping Zhang;Shoushou Ren;
  Pages: 8392 - 8406
  Abstract: With the prosperity of emerging applications, the $6^{th}$ Generation mobile communication systems (6G) is coming at an unimaginable speed. It is expected to provide more intelligent, flexible, and secure services. As an essential pillar of 6G networks, ultra-Reliable Low Latency Communication (uRLLC) has promoted the vigorous development of intelligent communications. However, the existing networks cannot fully satisfy the strict and various requirements of uRLLC services, including delay, reliability and security. Considering the interaction between the physical layer and the upper layer, we propose a Cross-layer Flexible Security Solution (CFSS), which includes initiative waiting strategy, flexible transmission time interval scheduling strategy, and flexible pre-backup transmission strategy. While considering secure communication, CFSS could flexibly provide customized services to the users through cross-layer parameters configuration and resource allocation. In addition, we extend the Stochastic Network Calculus (SNC) modeling to the security field, and use Finite Blocklength Coding (FBC) to analyze the service process of uRLLC. Two cases of FBC are considered comprehensively, namely, given decoding error probability and given transmission rate. Finally, Experienced Meta-Asynchronous Advantage Actor-Critic (EM-A3C) algorithm is proposed to solve the complex optimization problem, the establishment of experience pool effectively improves the algorithm efficiency.
  PubDate: TUE, 04 APR 2023 10:04:28 -04
  Issue No: Vol. 22, No. 11 (2023)
Preallocation-Based Combinatorial Auction for Efficient Fair Channel
Assignments in Multi-Connectivity Networks
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  Authors: Dávid Csercsik;Eduard A. Jorswieck;
  Pages: 8407 - 8422
  Abstract: We consider a general multi-connectivity framework, intended for ultra-reliable low-latency communications (URLLC) services, and propose a novel, preallocation-based combinatorial auction approach for the efficient allocation of channels. We compare the performance of the proposed method with several other state-of-the-art and alternative channel-allocation algorithms. The two proposed performance metrics are the capacity-based and the utility-based context. In the first case, every unit of additional capacity is regarded as beneficial for any tenant, independent of the already allocated quantity, and the main measure is the total throughput of the system. In the second case, we assume a minimal and maximal required capacity value for each tenant, and consider the implied utility values accordingly. In addition to the total system performance, we also analyze fairness and computational requirements in both contexts. We conclude that at the cost of higher but still plausible computational time, the fairness-enhanced version of the proposed preallocation based combinatorial auction algorithm outperforms every other considered method when one considers total system performance and fairness simultaneously, and performs especially well in the utility context. Therefore, the proposed algorithm may be regarded as candidate scheme for URLLC channel allocation problems, where minimal and maximal capacity requirements have to be considered.
  PubDate: FRI, 07 APR 2023 10:02:58 -04
  Issue No: Vol. 22, No. 11 (2023)
Rate Splitting Multiple Access for Next Generation Cognitive Radio Enabled
LEO Satellite Networks
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  Authors: Wali Ullah Khan;Zain Ali;Eva Lagunas;Asad Mahmood;Muhammad Asif;Asim Ihsan;Symeon Chatzinotas;Björn Ottersten;Octavia A. Dobre;
  Pages: 8423 - 8435
  Abstract: Low Earth Orbit (LEO) satellite communication (SatCom) has drawn particular attention recently due to its high data rate services and low round-trip latency. It has low launching and manufacturing costs than Medium Earth Orbit (MEO) and Geostationary Earth Orbit (GEO) satellites. Moreover, LEO SatCom has the potential to provide global coverage with a high-speed data rate and low transmission latency. However, the spectrum scarcity might be one of the challenges in the growth of LEO satellites, impacting severe restrictions on developing ground-space integrated networks. To address this issue, cognitive radio and rate splitting multiple access (RSMA) are the two emerging technologies for high spectral efficiency and massive connectivity. This paper proposes a cognitive radio enabled LEO SatCom using RSMA radio access technique with the coexistence of GEO SatCom network. In particular, this work aims to maximize the sum rate of LEO SatCom by simultaneously optimizing the power budget over different beams, RSMA power allocation for users over each beam, and subcarrier user assignment while restricting the interference temperature to GEO SatCom. The problem of sum rate maximization is formulated as non-convex, where the global optimal solution is challenging to obtain. Thus, an efficient solution can be obtained in three steps: first we employ a successive convex approximation technique to reduce the complexity and make the problem more tractable. Second, for any given resource block user assignment, we adopt KarushKuhnTucker (KKT) conditions to calculate the transmit power over different beams and RSMA power allocation of users over each beam. Third, using the allocated power, we design an efficient algorithm based on the greedy approach for resource block user assignment. For comparison, we propose two suboptimal schemes with fixed power allocation over different beams and random resource block user assignment as the benchmark. Numerical results provided in this work are obtained based on the Monte Carlo simulations, which demonstrate the benefits of the proposed optimization scheme compared to the benchmark schemes.
  PubDate: FRI, 07 APR 2023 10:02:58 -04
  Issue No: Vol. 22, No. 11 (2023)
CFLIT: Coexisting Federated Learning and Information Transfer
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  Authors: Zehong Lin;Hang Liu;Ying-Jun Angela Zhang;
  Pages: 8436 - 8453
  Abstract: Future wireless networks are expected to support diverse mobile services, including artificial intelligence (AI) services and ubiquitous data transmissions. Federated learning (FL), as a revolutionary learning approach, enables collaborative AI model training across distributed mobile edge devices. By exploiting the superposition property of multiple-access channels, over-the-air computation allows concurrent model uploading from massive devices over the same radio resources, and thus significantly reduces the communication cost of FL. In this paper, we study the coexistence of over-the-air FL and traditional information transfer (IT) in a mobile edge network, where an access point (AP) coordinates a set of devices for over-the-air FL and serves multiple devices for information transfer in the meantime. We propose a coexisting federated learning and information transfer (CFLIT) communication framework, where the FL and IT devices share the wireless spectrum in an orthogonal frequency division multiplexing (OFDM) system. Under this framework, we aim to maximize the IT data rate and guarantee a given FL convergence performance by optimizing the long-term radio resource allocation. A key challenge that limits the spectrum efficiency of the coexisting system lies in the large overhead incurred by frequent communication between the server and edge devices for FL model aggregation. To address the challenge, we rigorously analyze the impact of the computation-to-communication ratio on the convergence of over-the-air FL in wireless fading channels. The analysis reveals the existence of an optimal computation-to-communication ratio that minimizes the amount of radio resources needed for over-the-air FL to converge to a given error tolerance. Based on the analysis, we propose a low-complexity online algorithm to jointly optimize the radio resource allocation for both the FL devices and IT devices. We further derive an analytical expression of the achievable data rate of IT users. Extensive numerical simulations verify the superior performance of the proposed design for the coexistence of FL and IT devices in wireless cellular systems.
  PubDate: TUE, 04 APR 2023 10:04:28 -04
  Issue No: Vol. 22, No. 11 (2023)
Coverage Performance Analysis of a Cache-Enabled UAV Base Station Assisted
Cellular Network
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  Authors: Qiangfeng Zhu;Jun Zheng;Abbas Jamalipour;
  Pages: 8454 - 8467
  Abstract: Unmanned Aerial Vehicle base stations (UBSs) can be used to assist a ground cellular network to enhance its network services for cellular users. This paper studies the coverage performance analysis of a cache-enabled UBS-assisted cellular network. Analytical models are derived for investigating the overall coverage probability of the network and the average achievable rate of a cellular user. In deriving the analytical models, an air-to-ground (A2G) channel model with both a line-of-sight (LoS) link and a non-line-of-sight (NLoS) link, a BS association strategy based on the strongest average received power, and a cache model with a probabilistic caching strategy are considered. The cache hit probability of UBSs based on the cache model is also taken into consideration. Moreover, the association probabilities with the association strategy are derived for different types of base stations. The derived analytical models are validated through simulation results and the impacts of system parameters on the coverage performance of the network are investigated through numerical results. Compared with existing relevant work, the novelty of this work is that the effects of both an access link and a backhaul link are taken into account in the coverage performance analysis. The obtained results can provide theoretical guidance for the deployment of UBSs in a cache-enabled UBS-assisted cellular network.
  PubDate: WED, 05 APR 2023 10:05:41 -04
  Issue No: Vol. 22, No. 11 (2023)
Tanner-Graph-Based Massive Multiple Access—Transmission and Decoding
Schemes
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  Authors: Jiaai Liu;Xiaodong Wang;
  Pages: 8468 - 8482
  Abstract: In this paper we consider two Tanner-graph-based transmission schemes for massive multiple access, which combine non-orthogonal multiple access (NOMA) and grant-based random access. Each user transmits two data streams repeatedly using several channel time-frequency resource blocks (RBs) and the transmission schedule is represented by a Tanner graph, where variable nodes and check nodes represent the transmitted signals and the RBs, respectively. In Scheme 1 each variable node represents a data stream of a user, whereas Scheme 2 employs rate splitting and each variable node represents the superimposed data streams of a user. On the receiver side, we first consider peeling decoders that serve both as pilot-based channel estimators and baseline decoders. We then develop message-passing decoders for both transmission schemes that can fully exploit the diversity afforded by the repetitive transmission across different RBs, as opposed to the peeling decoders. We also propose a neural decoder by deep unfolding the message-passing decoder and further performing a small number of training epochs using the pilots. Simulation results show that for Transmission Scheme 1, the message-passing decoder offers decoding performance improvement over the peeling decoder by orders of magnitude; and as a result, Scheme 1 substantially outperforms Scheme 2. Moreover, the neural decoder further improves upon the performance of the message-passing decoder, as more information is learned about the transmitted data over the training epochs.
  PubDate: FRI, 07 APR 2023 10:02:58 -04
  Issue No: Vol. 22, No. 11 (2023)
Joint Mobility Control and MEC Offloading for Hybrid
Satellite-Terrestrial-Network-Enabled Robots
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  Authors: Peng Wei;Wei Feng;Yanmin Wang;Yunfei Chen;Ning Ge;Cheng-Xiang Wang;
  Pages: 8483 - 8497
  Abstract: Benefiting from the fusion of communication and intelligent technologies, network-enabled robots have become important to support future machine-assisted and unmanned applications. To provide high-quality services for robots in wide areas, hybrid satellite-terrestrial networks are a key technology. Through hybrid networks, computation-intensive and latency-sensitive tasks can be offloaded to mobile edge computing (MEC) servers. However, due to the mobility of mobile robots and unreliable wireless network environments, excessive local computations and frequent service migrations may significantly increase the service delay. To address this issue, this paper aims to minimize the average task completion time for MEC-based offloading initiated by satellite-terrestrial-network-enabled robots. Different from conventional mobility-aware schemes, the proposed scheme makes the offloading decision by jointly considering the mobility control of robots. A joint optimization problem of task offloading and velocity control is formulated. Using Lyapunov optimization, the original optimization is decomposed into a velocity control subproblem and a task offloading subproblem. Then, based on the Markov decision process (MDP), a dual-agent reinforcement learning (RL) algorithm is proposed. The convergence and complexity of the improved RL algorithm are theoretically analyzed, and the simulation results show that the proposed scheme can effectively reduce the offloading delay.
  PubDate: FRI, 07 APR 2023 10:02:58 -04
  Issue No: Vol. 22, No. 11 (2023)
Amplitude-Constrained Constellation and Reflection Pattern Designs for
Directional Backscatter Communications Using Programmable Metasurface
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  Authors: Wei Wang;Bincheng Zhu;Yongming Huang;Wei Zhang;
  Pages: 8498 - 8511
  Abstract: The large scale reflector array of programmable metasurfaces is capable of increasing the power efficiency of backscatter communications via passive beamforming and thus has the potential to revolutionize the low-data-rate nature of backscatter communications. In this paper, we propose to design the power-efficient higher-order constellation and reflection pattern under the amplitude constraint brought by backscatter communications. For the constellation design, we adopt the amplitude and phase-shift keying (APSK) constellation and optimize the parameters of APSK such as ring number, ring radius, and inter-ring phase difference. Specifically, we derive closed-form solutions to the optimal ring radius and inter-ring phase difference for an arbitrary modulation order in the decomposed subproblems. For the reflection pattern design, we propose to optimize the passive beamforming vector by solving a multi-objective optimization problem that maximizes reflection power and guarantees beam homogenization within the interested angle range. To solve the problem, we propose a constant-modulus power iteration method, which is proven to be monotonically increasing, to maximize the objective function in each iteration. Numerical results show that the proposed APSK constellation design and reflection pattern design outperform the existing modulation and beam pattern designs in programmable metasurface enabled backscatter communications.
  PubDate: FRI, 07 APR 2023 10:02:58 -04
  Issue No: Vol. 22, No. 11 (2023)
Beamforming Design and Trajectory Optimization for UAV-Empowered Adaptable
Integrated Sensing and Communication
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  Authors: Cailian Deng;Xuming Fang;Xianbin Wang;
  Pages: 8512 - 8526
  Abstract: Unmanned aerial vehicle (UAV) has high flexibility and controllable mobility, therefore it is considered as a promising enabler for future integrated sensing and communication (ISAC). In this paper, we propose a novel adaptable ISAC (AISAC) mechanism in the UAV-empowered system, where the UAV performs sensing on demand during communication and the sensing duration is flexibly configured according to the application requirements rather than keeping the same with the communication duration. Our designed mechanism avoids the excessive sensing and waste of radio resources, therefore improving the resource utilization and system performance. In the UAV-empowered AISAC system, we aim at maximizing the average system throughput by optimizing the communication and sensing beamforming as well as the UAV trajectory while guaranteeing the quality-of-service requirements of communication and sensing. To efficiently solve the considered non-convex optimization problem, we propose an efficient alternating optimization algorithm to alternately optimize the communication and sensing beamforming as well as the UAV trajectory to obtain a suboptimal solution. Numerical results validate the superiority of the proposed adaptable mechanism and the effectiveness of the designed algorithm.
  PubDate: TUE, 11 APR 2023 10:03:10 -04
  Issue No: Vol. 22, No. 11 (2023)