Subjects -> COMMUNICATIONS (Total: 518 journals)
    - COMMUNICATIONS (446 journals)
    - DIGITAL AND WIRELESS COMMUNICATION (31 journals)
    - HUMAN COMMUNICATION (19 journals)
    - MEETINGS AND CONGRESSES (7 journals)
    - RADIO, TELEVISION AND CABLE (15 journals)

DIGITAL AND WIRELESS COMMUNICATION (31 journals)

Showing 1 - 31 of 31 Journals sorted alphabetically
Ada : A Journal of Gender, New Media, and Technology     Open Access   (Followers: 22)
Advances in Image and Video Processing     Open Access   (Followers: 24)
Communications and Network     Open Access   (Followers: 13)
E-Health Telecommunication Systems and Networks     Open Access   (Followers: 3)
EURASIP Journal on Wireless Communications and Networking     Open Access   (Followers: 14)
Future Internet     Open Access   (Followers: 84)
Granular Computing     Hybrid Journal  
IEEE Transactions on Wireless Communications     Hybrid Journal   (Followers: 25)
IEEE Wireless Communications Letters     Hybrid Journal   (Followers: 41)
IET Wireless Sensor Systems     Open Access   (Followers: 17)
International Journal of Communications, Network and System Sciences     Open Access   (Followers: 9)
International Journal of Digital Earth     Hybrid Journal   (Followers: 14)
International Journal of Embedded and Real-Time Communication Systems     Full-text available via subscription   (Followers: 9)
International Journal of Interactive Communication Systems and Technologies     Full-text available via subscription   (Followers: 2)
International Journal of Machine Intelligence and Sensory Signal Processing     Hybrid Journal   (Followers: 3)
International Journal of Mobile Computing and Multimedia Communications     Full-text available via subscription   (Followers: 2)
International Journal of Satellite Communications and Networking     Hybrid Journal   (Followers: 40)
International Journal of Wireless and Mobile Computing     Hybrid Journal   (Followers: 8)
International Journal of Wireless Networks and Broadband Technologies     Full-text available via subscription   (Followers: 2)
International Journals Digital Communication and Analog Signals     Full-text available via subscription   (Followers: 2)
Journal of Digital Information     Open Access   (Followers: 164)
Journal of Interconnection Networks     Hybrid Journal   (Followers: 1)
Journal of the Southern Association for Information Systems     Open Access   (Followers: 2)
Mobile Media & Communication     Hybrid Journal   (Followers: 10)
Nano Communication Networks     Hybrid Journal   (Followers: 5)
Psychology of Popular Media Culture     Full-text available via subscription   (Followers: 2)
Signal, Image and Video Processing     Hybrid Journal   (Followers: 13)
Ukrainian Information Space     Open Access  
Vehicular Communications     Full-text available via subscription   (Followers: 4)
Vista     Open Access   (Followers: 2)
Wireless Personal Communications     Hybrid Journal   (Followers: 6)
Similar Journals
Journal Cover
IEEE Transactions on Wireless Communications
Journal Prestige (SJR): 1.246
Citation Impact (citeScore): 6
Number of Followers: 25  
 
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 1536-1276
Published by IEEE Homepage  [228 journals]
  • IEEE Transactions on Wireless Communications Publication Information

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      Abstract: Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication.
      PubDate: Jan. 2023
      Issue No: Vol. 22, No. 1 (2023)
       
  • IEEE Transactions on Wireless Communications Society Information

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      Abstract: Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication.
      PubDate: Jan. 2023
      Issue No: Vol. 22, No. 1 (2023)
       
  • Hierarchical Federated Learning With Quantization: Convergence Analysis
           and System Design

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      Authors: Lumin Liu;Jun Zhang;Shenghui Song;Khaled B. Letaief;
      Pages: 2 - 18
      Abstract: Federated learning (FL) is a powerful distributed machine learning framework where a server aggregates models trained by different clients without accessing their private data. Hierarchical FL, with a client-edge-cloud aggregation hierarchy, can effectively leverage both the cloud server’s access to many clients’ data and the edge servers’ closeness to the clients to achieve a high communication efficiency. Neural network quantization can further reduce the communication overhead during model uploading. To fully exploit the advantages of hierarchical FL, an accurate convergence analysis with respect to the key system parameters is needed. Unfortunately, existing analysis is loose and does not consider model quantization. In this paper, we derive a tighter convergence bound for hierarchical FL with quantization. The convergence result leads to practical guidelines for important design problems such as the client-edge aggregation and edge-client association strategies. Based on the obtained analytical results, we optimize the two aggregation intervals and show that the client-edge aggregation interval should slowly decay while the edge-cloud aggregation interval needs to adapt to the ratio of the client-edge and edge-cloud propagation delay. Simulation results shall verify the design guidelines and demonstrate the effectiveness of the proposed aggregation strategy.
      PubDate: Jan. 2023
      Issue No: Vol. 22, No. 1 (2023)
       
  • A Unified Framework for Pushing in Two-Tier Heterogeneous Networks With
           mmWave Hotspots

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      Authors: Zhanyuan Xie;Wei Chen;H. Vincent Poor;
      Pages: 19 - 31
      Abstract: Millimeter-wave (mmWave) communications have attracted substantial attention due to their potential to provide very large bandwidths. Unfortunately, the propagation of millimeter waves suffers from severe path loss and blocking, which limits the coverage of mmWave communication systems. To overcome this, mmWave hotspot empowered two-tier heterogeneous networks are expected to play an important role in the sixth generation (6G) systems. When the deployment of mmWave hotspots is not dense enough, or even sparse, assuring the quality of service (QoS) for mobile users becomes rather challenging. In this paper, we investigate pushing in two-tier heterogeneous networks with mmWave hotspots, in which popular content items are cached by a mobile user when they can be served by a mmWave hotspot. To this end, a unified framework is presented to analyze and optimize the effective throughput of pushing. Based on the effective throughput analysis, pushing policies with different mobility models and/or mmWave hotspot distributions are presented. Both theoretical and numerical results demonstrate the substantial caching gain due to user mobility in mmWave hotspot empowered two-tier networks.
      PubDate: Jan. 2023
      Issue No: Vol. 22, No. 1 (2023)
       
  • AoI Minimization for WSN Data Collection With Periodic Updating Scheme

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      Authors: Guangyang Zhang;Chao Shen;Qingjiang Shi;Bo Ai;Zhangdui Zhong;
      Pages: 32 - 46
      Abstract: In this paper, we consider the design of a wireless sensor network (WSN) that aims at monitoring the environment and collecting data periodically. In view of the limited energy and computational capability of the sensor nodes, a mobile edge computing (MEC) server is deployed in the WSN as a data processing unit. The goal of the design is to maintain the freshness of the data, which is characterized by the criterion of the age of information (AoI). Therefore, we analyze the long-term average AoI of the considered network. Then, the energy and time constraints for the WSN are modeled with consideration of transmission and computation. Next, a non-convex average AoI minimization problem is formulated subject to the energy and time constraints by jointly optimizing the sampling rate, computing scheduling, and transmit power. To tackle the challenging problem, the geometric programming and successive convex approximation (SCA) technique are applied to develop an algorithm with convergence guarantee. Moreover, to exhibit the benefits of the MEC server, a joint design is investigated for the WSN without the MEC server. Finally, the numerical results demonstrate the efficiency of our proposed SCA-based algorithm and show the impact of the sampling rate on the AoI performance.
      PubDate: Jan. 2023
      Issue No: Vol. 22, No. 1 (2023)
       
  • Distributed Reconfigurable Intelligent Surfaces Assisted Indoor
           Positioning

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      Authors: Teng Ma;Yue Xiao;Xia Lei;Wenhui Xiong;Ming Xiao;
      Pages: 47 - 58
      Abstract: Recently, communications with the aid of reconfigurable intelligent surface (RIS), which operates with the aim of enhancing the system communication performance, have aroused extensive researches. Furthermore, the use of RIS for positioning has been considered. Therefore, we focus on a practical structure of indoor positioning assisted by distributed RISs through utilizing their ability to manipulate multipath signals, through the developed quasi-static and dynamic modes. Specifically, in the quasi-static mode, for reducing the implementation cost, the reflection coefficients for each RIS are preset and remain constant. In the dynamic mode, the reflection coefficients can be timely updated with a two-step positioning approach toward more accurate positioning performance. Furthermore, the Cramér-Rao lower bound of the developed positioning scheme is quantified through theoretical analysis. Both theoretical analysis and simulation results demonstrate that RIS has the potential to realize accurate positioning even with a single access point, due to its ability to mark the channel and replace traditional active positioning anchors. Meanwhile, we also show that the developed two-step positioning scheme can achieve considerable performance gain in accurate positioning.
      PubDate: Jan. 2023
      Issue No: Vol. 22, No. 1 (2023)
       
  • Impatient Queuing for Intelligent Task Offloading in Multiaccess Edge
           Computing

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      Authors: Bin Han;Vincenzo Sciancalepore;Yihua Xu;Di Feng;Hans D. Schotten;
      Pages: 59 - 72
      Abstract: Multi-access edge computing (MEC) emerges as an essential part of the upcoming Fifth Generation (5G) and future beyond-5G mobile communication systems. It adds computational power towards the edge of cellular networks, much closer to energy-constrained user devices, and therewith allows the users to offload tasks to the edge computing nodes for low-latency applications with very-limited battery consumption. However, due to the high dynamics of user demand and server load, task congestion may occur at the edge nodes resulting in long queuing delay. Such delays can significantly degrade the quality of experience (QoE) of some latency-sensitive applications, raise the risk of service outage, and cannot be efficiently resolved by conventional queue management solutions. In this article, we study a latency-outage critical scenario, where users intend to limit the risk of latency outage. We propose an impatience-based queuing strategy for such users to intelligently choose between MEC offloading and local computation, allowing them to rationally renege from the task queue. The proposed approach is demonstrated by numerical simulations to be efficient for generic service model, when a perfect queue status information is available. For the practical case where the users obtain only imperfect queue status information, we design an optimal online learning strategy to enable its application in Poisson service scenarios.
      PubDate: Jan. 2023
      Issue No: Vol. 22, No. 1 (2023)
       
  • Task-Oriented Communication for Multidevice Cooperative Edge Inference

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      Authors: Jiawei Shao;Yuyi Mao;Jun Zhang;
      Pages: 73 - 87
      Abstract: This paper investigates task-oriented communication for multi-device cooperative edge inference, where a group of distributed low-end edge devices transmit the extracted features of local samples to a powerful edge server for inference. While cooperative edge inference can overcome the limited sensing capability of a single device, it substantially increases the communication overhead and may incur excessive latency. To enable low-latency cooperative inference, we propose a learning-based communication scheme that optimizes local feature extraction and distributed feature encoding in a task-oriented manner, i.e., to remove data redundancy and transmit information that is essential for the downstream inference task rather than reconstructing the data samples at the edge server. Specifically, we leverage Tishby’s information bottleneck (IB) principle (Tishby et al., 2000) to extract the task-relevant feature at each edge device, and adopt the distributed information bottleneck (DIB) framework of Aguerri and Zaidi, 2021, to formalize a single-letter characterization of the optimal rate-relevance tradeoff for distributed feature encoding. To admit flexible control of the communication overhead, we extend the DIB framework to a distributed deterministic information bottleneck (DDIB) objective that explicitly incorporates the representational costs of the encoded features. As the IB-based objectives are computationally prohibitive for high-dimensional data, we adopt variational approximations to make the optimization problems tractable. To compensate for the potential performance loss due to the variational approximations, we also develop a selective retransmission (SR) mechanism to identify the redundancy in the encoded features among multiple edge devices to attain additional communication overhead reduction. Extensive experiments on multi-view image classification and multi-view object recognition tasks evidence that the proposed task-oriented commu-ication scheme achieves a better rate-relevance tradeoff than existing methods.
      PubDate: Jan. 2023
      Issue No: Vol. 22, No. 1 (2023)
       
  • Fault-Tolerant Cooperative Signal Detection for Petahertz Short-Range
           Communication With Continuous Waveform Wideband Detectors

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      Authors: Sudhanshu Arya;Yeon Ho Chung;
      Pages: 88 - 106
      Abstract: Motivated by unique scattering properties at petahertz frequencies, we present a novel statistical model of fault-tolerant cooperative signal detection for short-range optical petahertz wireless communications, where a single-scattering assumption holds valid. We characterize the received non-line-of-sight (NLOS) signal by a fault-tolerant continuous waveform wideband detector with a non-zero failure probability. To better reflect the physical properties of the petahertz communication, both signal-independent and signal-dependent noise sources are considered in characterizing the received signal. The distribution of the received signal is quantified with each scattered path following the Málaga distribution. We leverage the location flexibility of randomly distributed collaborative users, considering each user experiences an independent channel condition. Utilizing the Neyman-Pearson criterion, we develop the binary hypothesis testing problem and subsequently derive the likelihood ratio for the test statistics. Moreover, to quantify the performance, a framework is developed for the average area under the receiver operating characteristic (ROC) curve for both single user and cooperative scenarios. An optimal decision fusion with a majority rule for fault-tolerant signal detection is applied to exploit the maximum spectrum opportunity. It is found that with the optimal voting rule and for a given target error rate, the network requires fewer collaborative secondary users than the total number of users available in an optical network. However, in the limiting case, it is shown that, as the cost function approaches its minimum or maximum value within its allowable range, the optimal number of collaborative users becomes independent of the failure probabilities. With the realistic assumption of fault-tolerant users, it is found that the false alarm probability increases faster than the detection probability.
      PubDate: Jan. 2023
      Issue No: Vol. 22, No. 1 (2023)
       
  • Joint Scheduling of Proactive Pushing and On-Demand Transmission Over
           Shared Spectrum for Profit Maximization

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      Authors: Haiming Hui;Wei Chen;
      Pages: 107 - 121
      Abstract: Proactive pushing has emerged as a promising solution to scale the service capacity by utilizing idle spectrum resources when on-demand transmission cannot fully exploit the spectrum resources during off-peak times. How to schedule both pushing and on-demand transmission jointly to meet a higher spectrum efficiency becomes a critical issue. Moreover, efficient and fair spectrum sharing among multiple resource schedulers remains open. In this paper, we introduce virtual network operators (VNOs) as schedulers that pay for consumed bandwidth and jointly schedule pushing and on-demand services. We adopt nonlinear spectrum pricing schemes with a convex and increasing price function enabling each VNO to share spectrum resources appropriately and independently. Considering the revenue from users and the cost for spectrum, we formulate a Markov decision process (MDP) to maximize the profit of VNO. A modified value iteration algorithm is applied to solve the MDP with reduced computational complexity. Furthermore, we show the structure of the optimal policy and provide the upper and lower bounds for the optimal performance. We present a low-complexity heuristic policy that can scale in practice with large state spaces and action spaces.
      PubDate: Jan. 2023
      Issue No: Vol. 22, No. 1 (2023)
       
  • Downlink Channel Estimation for FDD Massive MIMO Using Conditional
           Generative Adversarial Networks

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      Authors: Bitan Banerjee;Robert C. Elliott;Witold A. Krzymień;Hamid Farmanbar;
      Pages: 122 - 137
      Abstract: For implementation of massive multiple-input multiple-output (MIMO) cellular systems in frequency division duplex (FDD) mode, accurate estimation of downlink channel state information (CSI) is necessary, but full radio channel reciprocity between the uplink and downlink does not exist in that mode. Existing work on estimating downlink CSI in FDD massive MIMO systems has considered such approaches as angle-of-arrival reciprocity, compressive sensing, using second-order channel statistics (particularly the channel covariance matrix (CCM)), and machine learning using deep neural networks (DNNs). Typical DNN-based approaches are unsuitable for this problem because DNNs require large datasets, thousands of training epochs, and are susceptible to environmental variations. To overcome these shortcomings, we develop a conditional generative adversarial network (CGAN) approach to uplink-to-downlink mapping of both CCMs and CSI. To apply this method, we convert the uplink and downlink CCMs/CSI to images and employ CGAN techniques previously applied to image translation. The normalized mean square error performance of the proposed CGAN is evaluated for several array sizes for both CCM and CSI mapping. For uplink-to-downlink CSI mapping, we also examine the spectral efficiency performance of our CGAN-based method, as well as the impact of pilot reuse; both simulated and measured CSI data are considered. Our results demonstrate performance improvement over existing algorithms.
      PubDate: Jan. 2023
      Issue No: Vol. 22, No. 1 (2023)
       
  • NOMA-Based Hybrid Satellite-UAV-Terrestrial Networks for 6G Maritime
           Coverage

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      Authors: Xinran Fang;Wei Feng;Yanmin Wang;Yunfei Chen;Ning Ge;Zhiguo Ding;Hongbo Zhu;
      Pages: 138 - 152
      Abstract: Current fifth-generation (5G) networks do not cover maritime areas, causing difficulties in developing maritime Internet of Things (IoT). To tackle this problem, we establish a nearshore network by collaboratively using on-shore terrestrial base stations (TBSs) and tethered unmanned aerial vehicles (UAVs). These TBSs and UAVs form virtual clusters in a user-centric manner. Within each virtual cluster, non-orthogonal multiple access (NOMA) is adopted for agilely including various maritime IoT devices, which are sparsely distributed over the vast ocean. The nearshore network also shares the spectrum with marine satellites. In such a NOMA-based hybrid satellite-UAV-terrestrial network, interference among different network segments, different clusters, and different users occurs. We thereby formulate a joint power allocation problem to maximize the sum rate of the network. Different from existing studies, we use large-scale channel state information (CSI) only for optimization to reduce system overhead. The large-scale CSI is obtained by using the position information of maritime IoT devices. The problem is non-convex with intractable non-linear constraints. We tackle these difficulties by adopting max-min optimization, the auxiliary function method, and the successive convex approximation technique. An iterative power allocation algorithm is accordingly proposed, which is shown to be effective for coverage enhancement by simulations. This shows the potential of NOMA-based hybrid satellite-UAV-terrestrial networks for maritime on-demand coverage.
      PubDate: Jan. 2023
      Issue No: Vol. 22, No. 1 (2023)
       
  • RadiOrchestra: Proactive Management of Millimeter-Wave Self-Backhauled
           Small Cells via Joint Optimization of Beamforming, User Association, Rate
           Selection, and Admission Control

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      Authors: Luis F. Abanto-Leon;Arash Asadi;Andres Garcia-Saavedra;Gek Hong Sim;Matthias Hollick;
      Pages: 153 - 173
      Abstract: Millimeter-wave self-backhauled small cells are a key component of next-generation wireless networks. Their dense deployment will increase data rates, reduce latency, and enable efficient data transport between the access and backhaul networks, providing greater flexibility not previously possible with optical fiber. Despite their high potential, operating dense self-backhauled networks optimally is an open challenge, particularly for radio resource management (RRM). This paper presents, RadiOrchestra, a holistic RRM framework that models and optimizes beamforming, rate selection as well as user association and admission control for self-backhauled networks. The framework is designed to account for practical challenges such as hardware limitations of base stations (e.g., computational capacity, discrete rates), the need for adaptability of backhaul links, and the presence of interference. Our framework is formulated as a nonconvex mixed-integer nonlinear program, which is challenging to solve. To approach this problem, we propose three algorithms that provide a trade-off between complexity and optimality. Furthermore, we derive upper and lower bounds to characterize the performance limits of the system. We evaluate the developed strategies in various scenarios, showing the feasibility of deploying practical self-backhauling in future networks.
      PubDate: Jan. 2023
      Issue No: Vol. 22, No. 1 (2023)
       
  • Learning-Based Downlink Power Allocation in Cell-Free Massive MIMO Systems

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      Authors: Mahmoud Zaher;Özlem Tuğfe Demir;Emil Björnson;Marina Petrova;
      Pages: 174 - 188
      Abstract: This paper considers a cell-free massive multiple-input multiple-output (MIMO) system that consists of a large number of geographically distributed access points (APs) serving multiple users via coherent joint transmission. The downlink performance of the system is evaluated, with maximum ratio and regularized zero-forcing precoding, under two optimization objectives for power allocation: sum spectral efficiency (SE) maximization and proportional fairness. We present iterative centralized algorithms for solving these problems. Aiming at a less computationally complex and also distributed scalable solution, we train a deep neural network (DNN) to approximate the same network-wide power allocation. Instead of training our DNN to mimic the actual optimization procedure, we use a heuristic power allocation, based on large-scale fading (LSF) parameters, as the pre-processed input to the DNN. We train the DNN to refine the heuristic scheme, thereby providing higher SE, using only local information at each AP. Another distributed DNN that exploits side information assumed to be available at the central processing unit is designed for improved performance. Further, we develop a clustered DNN model where the LSF parameters of a small number of APs, forming a cluster within a relatively large network, are used to jointly approximate the power coefficients of the cluster.
      PubDate: Jan. 2023
      Issue No: Vol. 22, No. 1 (2023)
       
  • Simultaneously Transmitting and Reflecting Reconfigurable Intelligent
           Surface Assisted NOMA Networks

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      Authors: Xinwei Yue;Jin Xie;Yuanwei Liu;Zhihao Han;Rongke Liu;Zhiguo Ding;
      Pages: 189 - 204
      Abstract: Simultaneously transmitting/refracting and reflecting reconfigurable intelligent surface (STAR-RIS) has been introduced to achieve full coverage area. This paper investigate the performance of STAR-RIS assisted non-orthogonal multiple access (NOMA) networks over Rician fading channels, where the incidence signals sent by base station are reflected and transmitted to the nearby user and distant user, respectively. To evaluate the performance of STAR-RIS-NOMA networks, we derive new approximate expressions of outage probability and ergodic rate for a pair of users, in which the imperfect successive interference cancellation (ipSIC) and perfect SIC (pSIC) schemes are taken into consideration. Based on the asymptotic expressions, the diversity orders of the nearby user with ipSIC/pSIC and distant user are achieved carefully.The high signal-to-noise ratio slopes of ergodic rates for nearby user with pSIC and distant user are equal to $one$ and $zero$ , respectively. In addition, the system throughput of STAR-RIS-NOMA is discussed in delay-limited and delay-tolerant modes. Simulation results are provided to verify the accuracy of the theoretical analyses and demonstrate that: 1) The outage probability of STAR-RIS-NOMA outperforms that of STAR-RIS assisted orthogonal multiple access (OMA) and conventional cooperative communication systems; 2) With the increasing of reflecting elements $K$ and Rician factor $kappa $ , the STAR-RIS-NOMA networks are capable of attaining the enhanced performance; and 3) The ergodic rates of STAR-RIS-NOMA are superior to that of STAR-RIS-OMA.
      PubDate: Jan. 2023
      Issue No: Vol. 22, No. 1 (2023)
       
  • Innovative Attack Detection Solutions for Wireless Networks With
           Application to Location Security

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      Authors: Danilo Orlando;Stefania Bartoletti;Ivan Palamà;Giuseppe Bianchi;Nicola Blefari Melazzi;
      Pages: 205 - 219
      Abstract: Modern wireless communication networks are threatened by new generations of radio hackers. These are skilled attackers equipped with low-cost software radios, suitably instrumented so as to monitor, degrade, or even alter the radio signals. The aim of this paper is to devise innovative detection architectures against the most common classes of threats: broadband noise jammers, whose goal is to reduce the signal-to-noise ratio, and spoofing/meaconing attacks, which aim to inject false or incorrect information into the receiver. To this end, we resort to the hypothesis testing theory and solve the associated problems by means of the GLRT possibly accounting for penalty terms. The resulting decision schemes represent the main technical novelty of this work. The analysis of their performance focuses on a location security case study for 4G/5G cellular networks. To this end, we leverage measurement models from the cellular localization literature and generate data according to these models. The numerical results show the effectiveness of the proposed approaches in comparison with suitable counterparts.
      PubDate: Jan. 2023
      Issue No: Vol. 22, No. 1 (2023)
       
  • Cooperative Satellite-Aerial-Terrestrial Systems: A Stochastic Geometry
           Model

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      Authors: Zhe Song;Jianping An;Gaofeng Pan;Shuai Wang;Haoxing Zhang;Yunfei Chen;Mohamed-Slim Alouini;
      Pages: 220 - 236
      Abstract: Nowadays, satellite and aerial platforms are playing an important role in realizing global seamless wireless coverage. In this paper, a cooperative satellite-aerial-terrestrial network (SATN) is considered, in which two kinds of relaying links, satellite and aerial relaying links, are used to assist a group of aerial terminals to forward their information to a remote terrestrial destination (D). Specifically, we model these aerial platforms sharing the same frequency band as a Matérn hard-core point process type-II. Also, a group of aerial jammers at D’s side is modeled as a Poisson point process. To demonstrate the end-to-end (e2e) performance of the two relaying links, the statistical characteristics of the received signal-to-interference are characterized and then a closed-form expression for the outage probability (OP) over the uplink from the aerial source to the satellite/the aerial relay, the downlink from the satellite/the aerial relay to D, and the inter-aerial relay link are derived. Numerical results are presented to verify the proposed analysis models and compare the outage performance of the considered cooperative SATN with the two relay links under numerous scenarios.
      PubDate: Jan. 2023
      Issue No: Vol. 22, No. 1 (2023)
       
  • A Novel Differential Chaos Shift Keying Scheme With Multidimensional Index
           Modulation

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      Authors: Huan Ma;Yi Fang;Pingping Chen;Shahid Mumtaz;Yonghui Li;
      Pages: 237 - 256
      Abstract: A new differential chaos shift keying scheme with multidimensional index modulation, referred to as MIM-DCSK scheme, is proposed in this paper. This design objective of the proposed MIM-DCSK scheme is to enhance the data rate, energy efficiency, and spectral efficiency of traditional DCSK scheme. In the proposed MIM-DCSK scheme, in addition to the information bits allocated for physical transmission, multidimensional transmission entities, namely the time slot, carrier, and Walsh code are simultaneously considered as indices to convey additional information bits, thus achieving high data rate, spectral efficiency, and energy efficiency. The theoretical bit-error-rate (BER) expressions of the MIM-DCSK scheme are derived over additive white Gaussian noise (AWGN) and multipath Rayleigh fading channels. Furthermore, the data rate, complexity, spectral efficiency, and energy efficiency of the MIM-DCSK scheme are analyzed. Simulation results verify the accuracy of the theoretical analysis and illustrate the superiority of the proposed scheme. The proposed MIM-DCSK scheme is a promising solution for low-power and low-cost short-wireless communications.
      PubDate: Jan. 2023
      Issue No: Vol. 22, No. 1 (2023)
       
  • Learning Emergent Random Access Protocol for LEO Satellite Networks

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      Authors: Ju-Hyung Lee;Hyowoon Seo;Jihong Park;Mehdi Bennis;Young-Chai Ko;
      Pages: 257 - 269
      Abstract: A mega-constellation of low-altitude earth orbit (LEO) satellites (SATs) are envisaged to provide a global coverage SAT network in beyond fifth-generation (5G) cellular systems. LEO SAT networks exhibit extremely long link distances of many users under time-varying SAT network topology. This makes existing multiple access protocols, such as random access channel (RACH) based cellular protocol designed for fixed terrestrial network topology, ill-suited. To overcome this issue, in this paper, we propose a novel contention-based random access solution for LEO SAT networks, dubbed emergent random access channel protocol (eRACH). In stark contrast to existing model-based and standardized protocols, eRACH is a model-free approach that emerges through interaction with the non-stationary network environment, using multi-agent deep reinforcement learning (MADRL). Furthermore, by exploiting known SAT orbiting patterns, eRACH does not require central coordination or additional communication across users, while training convergence is stabilized through the regular orbiting patterns. Compared to RACH, we show from various simulations that our proposed eRACH yields 54.6% higher average network throughput with around two times lower average access delay while achieving 0.989 Jain’s fairness index.
      PubDate: Jan. 2023
      Issue No: Vol. 22, No. 1 (2023)
       
  • Knowledge-Guided Learning for Transceiver Design in Over-the-Air Federated
           Learning

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      Authors: Yinan Zou;Zixin Wang;Xu Chen;Haibo Zhou;Yong Zhou;
      Pages: 270 - 285
      Abstract: In this paper, we consider communication-efficient over-the-air federated learning (FL), where multiple edge devices with non-independent and identically distributed datasets perform multiple local iterations in each communication round and then concurrently transmit their updated gradients to an edge server over the same radio channel for global model aggregation using over-the-air computation (AirComp). We derive the upper bound of the time-average norm of the gradients to characterize the convergence of AirComp-assisted FL, which reveals the impact of the model aggregation errors accumulated over all communication rounds on convergence. Based on the convergence analysis, we formulate an optimization problem to minimize the upper bound to enhance the learning performance, followed by proposing an alternating optimization algorithm to facilitate the transceiver design for AirComp-assisted FL. As the alternating optimization algorithm suffers from high computation complexity, we further develop a knowledge-guided learning algorithm that exploits the structure of the analytic expression of the transmit power to achieve computation-efficient transceiver design. Simulation results demonstrate that the proposed knowledge-guided learning algorithm achieves a comparable performance as the alternating optimization algorithm, but with a much lower computation complexity. Moreover, both proposed algorithms outperform the baseline methods in terms of convergence speed and test accuracy.
      PubDate: Jan. 2023
      Issue No: Vol. 22, No. 1 (2023)
       
  • Multiuser Co-Inference With Batch Processing Capable Edge Server

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      Authors: Wenqi Shi;Sheng Zhou;Zhisheng Niu;Miao Jiang;Lu Geng;
      Pages: 286 - 300
      Abstract: Graphics processing units (GPUs) can improve deep neural network inference throughput via batch processing, where multiple tasks are concurrently processed. We focus on novel scenarios that the energy-constrained mobile devices offload inference tasks to an edge server with GPU. The inference task is partitioned into sub-tasks for a finer granularity of offloading and scheduling, and the user energy consumption minimization problem under inference latency constraints is investigated. To deal with the coupled offloading and scheduling introduced by concurrent batch processing, we first consider an offline problem with a constant edge inference latency and the same latency constraint. It is proven that optimizing the offloading policy of each user independently and aggregating all the same sub-tasks in one batch is optimal, and thus the independent partitioning and same sub-task aggregating (IP-SSA) algorithm is inspired. Further, the optimal grouping (OG) algorithm is proposed to optimally group tasks when the latency constraints are different. Finally, when future task arrivals cannot be precisely predicted, a deep deterministic policy gradient (DDPG) agent is trained to call OG. Experiments show that IP-SSA reduces up to 94.9% user energy consumption in the offline setting, while DDPG-OG outperforms DDPG-IP-SSA by up to 8.92% in the online setting.
      PubDate: Jan. 2023
      Issue No: Vol. 22, No. 1 (2023)
       
  • Bayesian Receiver Design for Asynchronous Massive Connectivity

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      Authors: Shuchao Jiang;Chongbin Xu;Xiaojun Yuan;Zeyu Han;Zhengxing Wang;Xin Wang;
      Pages: 301 - 316
      Abstract: In this paper, we consider asynchronous massive connectivity, where massive low-power and low-rate devices with sporadic activity patterns connect to a multi-antenna access point (AP) in an asynchronous manner. Asynchronous transmission minimizes the amount of coordination between the devices and the AP, and thereby simplifies the transmitter design, yet at the cost of a more challenging receiver design. Specifically, asynchronous transmission results in inter-symbol interference (ISI) since the sampling at AP generally cannot match with the symbol intervals of uncoordinated devices. To enable reliable reception, we develop a turbo approximate message passing (TAMP) algorithm that consists of a channel-signal decomposition (CSD) module and a delay learning (DL) module. The CSD carries out sparse matrix factorization to estimate the channels and the ISI corrupted signals of active devices, and the DL is designed to estimate the delay of each active user and resolve the corresponding ISI based on the Bayesian principle. To refine the delay estimation, we further divide the DL module into symbol-level delay learning (SDL) and sub-symbol-level delay learning (sub-SDL) submodules. In particular, the sub-SDL estimates the residue delays (obtained by taking modulo of the symbol interval) and then finely compensates the ISI. Due to the continuity and randomness of time delay, the receive signal constellation consists of lines and curves instead of discrete points, even if the transmit signal constellation is discrete. To reduce the complexity of soft demodulation, we introduce a truncation and projection based approximation method to simplify the related message calculation. Numerical results demonstrate the superior performance of the proposed TAMP algorithm. Particularly, the TAMP algorithm is able to approach the single-user bound with known user delay.
      PubDate: Jan. 2023
      Issue No: Vol. 22, No. 1 (2023)
       
  • Rate-Splitting Multiple Access for Satellite-Terrestrial Integrated
           Networks: Benefits of Coordination and Cooperation

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      Authors: Longfei Yin;Bruno Clerckx;
      Pages: 317 - 332
      Abstract: This paper investigates the joint beamforming design problem to achieve max-min rate fairness in a satellite-terrestrial integrated network (STIN) where the satellite provides wide coverage to multibeam multicast satellite users (SUs), and the terrestrial base station (BS) serves multiple cellular users (CUs) in a densely populated area. Both the satellite and BS operate in the same frequency band. Since rate-splitting multiple access (RSMA) has recently emerged as a promising strategy for non-orthogonal transmission and robust interference management in multi-antenna wireless networks, we present two RSMA-based STIN schemes, namely the coordinated scheme relying on channel state information (CSI) sharing and the cooperative scheme relying on CSI and data sharing. Our objective is to maximize the minimum fairness rate amongst all SUs and CUs subject to transmit power constraints at the satellite and the BS. A joint beamforming algorithm is proposed to reformulate the original problem into an approximately equivalent convex one, which can be iteratively solved. Moreover, an expectation-based robust joint beamforming algorithm is proposed against the practical environment when the satellite channel phase uncertainties are considered. Simulation results demonstrate the effectiveness and robustness of our proposed RSMA schemes for STIN and exhibit significant performance gains compared with various baseline strategies.
      PubDate: Jan. 2023
      Issue No: Vol. 22, No. 1 (2023)
       
  • A Bipartite Graph Neural Network Approach for Scalable Beamforming
           Optimization

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      Authors: Junbeom Kim;Hoon Lee;Seung-Eun Hong;Seok-Hwan Park;
      Pages: 333 - 347
      Abstract: Deep learning (DL) techniques have been intensively studied for the optimization of multi-user multiple-input single-output (MU-MISO) downlink systems owing to the capability of handling nonconvex formulations. However, the fixed computation structure of existing deep neural networks (DNNs) lacks flexibility with respect to the system size, i.e., the number of antennas or users. This paper develops a bipartite graph neural network (BGNN) framework, a scalable DL solution designed for multi-antenna beamforming optimization. The MU-MISO system is first characterized by a bipartite graph where two disjoint vertex sets, each of which consists of transmit antennas and users, are connected via pairwise edges. These vertex interconnection states are modeled by channel fading coefficients. Thus, a generic beamforming optimization process is interpreted as a computation task over a weighted bipartite graph. This approach partitions the beamforming optimization procedure into multiple suboperations dedicated to individual antenna vertices and user vertices. Separated vertex operations lead to scalable beamforming calculations that are invariant to the system size. The vertex operations are realized by a group of DNN modules that collectively form the BGNN architecture. Identical DNNs are reused at all antennas and users so that the resultant learning structure becomes flexible to the network size. Component DNNs of the BGNN are trained jointly over numerous MU-MISO configurations with randomly varying network sizes. As a result, the trained BGNN can be universally applied to arbitrary MU-MISO systems. Numerical results validate the advantages of the BGNN framework over conventional methods.
      PubDate: Jan. 2023
      Issue No: Vol. 22, No. 1 (2023)
       
  • Throughput Maximization for a Multicarrier Cell-Less NOMA Network: A
           Framework Based on Ensemble Metaheuristics

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      Authors: Fábio de Oliveira Torres;Valdivino Alexandre de Santiago Júnior;Daniel Benevides da Costa;Diego Lisboa Cardoso;Roberto Célio Limão de Oliveira;
      Pages: 348 - 361
      Abstract: Data collection and manipulation from real environments are two tasks that, in a well-organized way, can support the next generation of wireless networks (NGWNs) in resource allocation-related tasks such as spectrum frequency and transmission power. Due to the complexity of NGWNs, several metaheuristics have been proposed to help in these operations. However, a large number of these studies present solutions using only one algorithm or procedure, i.e., the research uses just one metaheuristic to resolve the radio resource allocation problem. This fact tends to lead to a loss of performance because, in this way, there is no considerable variability in the search strategies for better solutions. To tackle this problem, we propose a framework that maximizes the total throughput of an NGWN that implements the multi-carrier cell-less non-orthogonal multiple access (MC-CL-NOMA) architecture. The framework executes this activity using scenario information to feed an ensemble metaheuristic method. The empirical results show that the framework presents better performance than utilizing one metaheuristic individually. Moreover, the proposed method outperforms algorithms applicable in future 6G NOMA scenarios.
      PubDate: Jan. 2023
      Issue No: Vol. 22, No. 1 (2023)
       
  • Beamforming Optimization for Active Intelligent Reflecting Surface-Aided
           SWIPT

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      Authors: Ying Gao;Qingqing Wu;Guangchi Zhang;Wen Chen;Derrick Wing Kwan Ng;Marco Di Renzo;
      Pages: 362 - 378
      Abstract: Active intelligent reflecting surface (IRS) has been recently proposed to alleviate the product path loss attenuation inherent in the IRS-aided cascaded channel. In this paper, we study an active IRS-aided simultaneous wireless information and power transfer (SWIPT) system. Specifically, an active IRS is deployed to assist a multi-antenna access point (AP) to convey information and energy simultaneously to multiple single-antenna information users (IUs) and energy users (EUs). Two joint transmit and reflect beamforming optimization problems are investigated with different practical objectives. The first problem maximizes the weighted sum-power harvested by the EUs subject to individual signal-to-interference-plus-noise ratio (SINR) constraints at the IUs, while the second problem maximizes the weighted sum-rate of the IUs subject to individual energy harvesting (EH) constraints at the EUs. The optimization problems are non-convex and difficult to solve optimally. To tackle these two problems, we first rigorously prove that dedicated energy beams are not required for their corresponding semidefinite relaxation (SDR) reformulations and the SDR is tight for the first problem, thus greatly simplifying the AP precoding design. Then, by capitalizing on the techniques of alternating optimization (AO), SDR, and successive convex approximation (SCA), computationally efficient algorithms are developed to obtain suboptimal solutions of the resulting optimization problems. Simulation results demonstrate that, given the same total system power budget, significant performance gains in terms of operating range of wireless power transfer (WPT), total harvested energy, as well as achievable rate can be obtained by our proposed designs over benchmark schemes (especially the one adopting a passive IRS). Moreover, it is advisable to deploy an active IRS in the proximity of the users for the effective operation of WPT/SWIPT.
      PubDate: Jan. 2023
      Issue No: Vol. 22, No. 1 (2023)
       
  • Deep Learning for Estimation and Pilot Signal Design in Few-Bit Massive
           MIMO Systems

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      Authors: Ly V. Nguyen;Duy H. N. Nguyen;A. Lee Swindlehurst;
      Pages: 379 - 392
      Abstract: Estimation in few-bit MIMO systems is challenging, since the received signals are nonlinearly distorted by the low-resolution ADCs. In this paper, we propose a deep learning framework for channel estimation, data detection, and pilot signal design to address the nonlinearity in such systems. The proposed channel estimation and data detection networks are model-driven and have special structures that take advantage of domain knowledge in the few-bit quantization process. While the first data detection network, B-DetNet, is based on a linearized model obtained from the Bussgang decomposition, the channel estimation network and the second data detection network, FBM-CENet and FBM-DetNet respectively, rely on the original quantized system model. To develop FBM-CENet and FBM-DetNet, the maximum-likelihood channel estimation and data detection problems are reformulated to overcome the indeterminant gradient issue. An important feature of the proposed FBM-CENet structure is that the pilot matrix is integrated into the weight matrices of its channel estimator. Thus, training the proposed FBM-CENet enables a joint optimization of both the channel estimator at the base station and the pilot signal transmitted from the users. Simulation results show significant performance gains in estimation accuracy by the proposed deep learning framework.
      PubDate: Jan. 2023
      Issue No: Vol. 22, No. 1 (2023)
       
  • Energy Efficiency Maximization of Massive MIMO Communications With Dynamic
           Metasurface Antennas

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      Authors: Li You;Jie Xu;George C. Alexandropoulos;Jue Wang;Wenjin Wang;Xiqi Gao;
      Pages: 393 - 407
      Abstract: Future wireless communications are largely inclined to deploy massive numbers of antennas at the base stations (BSs) by leveraging cost- and energy-efficient as well as environmentally friendly antenna arrays. The emerging technology of dynamic metasurface antennas (DMAs) is promising to realize such massive antenna arrays with reduced physical size, hardware cost, and power consumption. The goal of this paper is the optimization of the energy efficiency (EE) performance of DMA-assisted massive multiple-input multiple-output (MIMO) wireless communications. Focusing on the uplink, we propose an algorithmic framework for designing the transmit precoding of each multi-antenna user and the DMA tuning strategy at the BS to maximize the EE performance, considering the availability of either instantaneous or statistical channel state information (CSI). Specifically, the proposed framework is shaped around Dinkelbach’s transform, alternating optimization, and deterministic equivalent methods. In addition, we obtain a closed-form solution to the optimal transmit signal directions for the statistical CSI case, which simplifies the corresponding transmission design for the multiple-antenna case. Our numerical results verify the good convergence behavior of the proposed algorithms, and showcase the considerable EE performance gains of the DMA-assisted massive MIMO transmissions over the baseline schemes.
      PubDate: Jan. 2023
      Issue No: Vol. 22, No. 1 (2023)
       
  • Reinforcement Learning Based Latency Minimization in Secure NOMA-MEC
           Systems With Hybrid SIC

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      Authors: Kaidi Wang;Haodong Li;Zhiguo Ding;Pei Xiao;
      Pages: 408 - 422
      Abstract: In this paper, physical layer security (PLS) in a non-orthogonal multiple access (NOMA)-based mobile edge computing (MEC) system is investigated, where hybrid successive interference cancellation (SIC) decoding is considered. Specifically, users intend to complete confidential tasks with the help of the MEC server, while an eavesdropper attempts to intercept the offloaded tasks. By jointly designing computational resource allocation, task assignment, and power allocation, a latency minimization problem is formulated. Based on the interactions between local computing time and MEC processing time, the closed-from solutions of computational resource allocation and task assignment are derived. After that, a strategy selection mechanism is established to select offloading strategies based on the corresponding conditions. Moreover, according to the analysis of hybrid SIC decoding, the conditions of different decoding orders in secure NOMA networks are derived. Furthermore, a reinforcement learning based algorithm is proposed to solve the power allocation problems for NOMA and OMA offloading strategies. This work is extended to a multi-user scenario, in which a matching-based algorithm is proposed to solve the formulated sub-channel assignment problem. Simulation results indicate that: i) the proposed solution can significantly reduce the latency and provide dynamic strategy selection for various scenarios; ii) the NOMA offloading strategy with hybrid SIC decoding can outperform other strategies in the considered system.
      PubDate: Jan. 2023
      Issue No: Vol. 22, No. 1 (2023)
       
  • Reconfigurable Intelligent Surface Based Uplink MU-MIMO Symbiotic Radio
           System

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      Authors: Jinlin Hu;Ying-Chang Liang;Yiyang Pei;Sumei Sun;Ruolun Liu;
      Pages: 423 - 438
      Abstract: In this paper, we investigate a novel uplink reconfigurable intelligent surface (RIS) based multi-user multi-input multi-output symbiotic radio system. It indicates that each RIS, as an Internet-of-Things (IoT) device, enhances the primary transmission from a nearby user to the base station (BS), and simultaneously transmits its own information to the BS by backscattering modulation. By embedding environmental sensors on the RISs, the proposed system enables the IoT transmission of locally collected environmental data to the BS while assisting the primary communications from the users to the BS. We consider both the case of perfect and imperfect channel state information (CSI), and design the active beamforming at the BS and the passive beamforming at the RISs jointly to maximize the weighted sum-rate of both the primary and IoT transmissions. For the perfect CSI case, we propose an algorithm based on the block coordinate descent (BCD) method to solve the problem. We also propose another algorithm with a similar framework to reduce the computational complexity. For the imperfect CSI case, an algorithm based on BCD and the online successive convex approximation technique is proposed. Simulation results show that the proposed system achieves significant performance gain over a number of baseline schemes for both the perfect and imperfect CSI cases. Furthermore, when the channel estimation error is small, the performance loss due to imperfect CSI is insignificant.
      PubDate: Jan. 2023
      Issue No: Vol. 22, No. 1 (2023)
       
  • Short-Packet Communications in Multihop Networks With WET: Performance
           Analysis and Deep Learning-Aided Optimization

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      Authors: Toan-Van Nguyen;Van-Dinh Nguyen;Daniel Benevides da Costa;Thien Huynh-The;Rose Qingyang Hu;Beongku An;
      Pages: 439 - 456
      Abstract: In this paper, we study short-packet communications in multi-hop networks with wireless energy transfer, where relay nodes harvest energy from power beacons to transmit short packets to multiple destinations. It is proposed a novel cooperative beamforming relay selection (CRS) scheme which incorporates partial relay selection and distributed multiuser beamforming to achieve a high-reliable transmission in two consecutive hops. A closed-form expression for the average block error rate (BLER) of the CRS scheme is derived, based on which an asymptotic analysis is also carried out. To achieve optimal channel uses allocation, we formulate a fairness end-to-end throughput maximization problem which is generally NP-hard due to the non-concavity of the objective function and mixed-integer constraints. To solve this challenging problem efficiently, we first relax channel uses to be continuous and transform the relaxed problem into an equivalent non-convex one, but with a more tractable form. We then develop a low-complexity iterative algorithm relying on inner approximation framework to convexify non-convex parts that converges to at least a locally optimal solution. Towards real-time settings, we design an efficient deep convolutional neural network (CNN) with multiscale-accumulation connections to achieve the sub-optimal solution of the relaxed problem via real-time inference processes. Numerical results are presented to verify the analytical derivations and to demonstrate performance improvements of the CRS scheme over the benchmark ones in terms of BLER, reliability, latency, and throughput in various settings. Moreover, the designed CNN provides the lowest root-mean-square error compared to the state-of-the-art deep learning approaches while the CNN-aided optimization framework estimates accurately the optimal channel uses allocation with low execution time.
      PubDate: Jan. 2023
      Issue No: Vol. 22, No. 1 (2023)
       
  • Modular Meta-Learning for Power Control via Random Edge Graph Neural
           Networks

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      Authors: Ivana Nikoloska;Osvaldo Simeone;
      Pages: 457 - 470
      Abstract: In this paper, we consider the problem of power control for a wireless network with an arbitrarily time-varying topology, including the possible addition or removal of nodes. A data-driven design methodology that leverages graph neural networks (GNNs) is adopted in order to efficiently parametrize the power control policy mapping the channel state information (CSI) to transmit powers. The specific GNN architecture, known as random edge GNN (REGNN), defines a non-linear graph convolutional filter whose spatial weights are tied to the channel coefficients. While prior work assumed a joint training approach whereby the REGNN-based policy is shared across all topologies, this paper targets adaptation of the power control policy based on limited CSI data regarding the current topology. To this end, we propose a novel modular meta-learning technique that enables the efficient optimization of module assignment. While black-box meta-learning optimizes a general-purpose adaptation procedure via (stochastic) gradient descent, modular meta-learning finds a set of reusable modules that can form components of a solution for any new network topology. Numerical results validate the benefits of meta-learning for power control problems over joint training schemes, and demonstrate the advantages of modular meta-learning when data availability is extremely limited.
      PubDate: Jan. 2023
      Issue No: Vol. 22, No. 1 (2023)
       
  • Transmission Scheme, Detection and Power Allocation for Uplink User
           Cooperation With NOMA and RSMA

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      Authors: Omid Abbasi;Halim Yanikomeroglu;
      Pages: 471 - 485
      Abstract: In this paper, we propose two novel cooperative-non-orthogonal-multiple-access (C-NOMA) and cooperative-rate-splitting-multiple-access (C-RSMA) schemes for uplink user cooperation. At the first mini-slot of these schemes, each user transmits its signal and receives the transmitted signal of the other user in full-duplex mode, and at the second mini-slot, each user relays the other user’s message with amplify-and-forward (AF) protocol. At both schemes, to achieve better spectral efficiency, users transmit signals in the non-orthogonal mode in both mini-slots. In C-RSMA, we also apply the rate-splitting method in which the message of each user is divided into two streams. In the proposed detection schemes for C-NOMA and C-RSMA, we apply a combination of maximum-ratio-combining (MRC) and successive-interference-cancellation (SIC). Then, we derive the achievable rates for C-NOMA and C-RSMA, and formulate two optimization problems to maximize the minimum rate of two users by considering the proportional fairness coefficient. We propose two power allocation algorithms based on successive-convex-approximation (SCA) and geometric-programming (GP) to solve these non-convex problems. Next, we derive the asymptotic outage probability of the proposed C-NOMA and C-RSMA schemes, and prove that they achieve diversity order of two. Finally, the above-mentioned performance is confirmed by simulations.
      PubDate: Jan. 2023
      Issue No: Vol. 22, No. 1 (2023)
       
  • Model-Based Reinforcement Learning With Kernels for Resource Allocation in
           RAN Slices

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      Authors: Juan J. Alcaraz;Fernando Losilla;Andrea Zanella;Michele Zorzi;
      Pages: 486 - 501
      Abstract: Network slicing is a key feature of 5G and beyond networks, allowing the deployment of separate logical networks (network slices), sharing a common underlying physical infrastructure, and characterized by distinct descriptors and behaviors. The dynamic allocation of physical network resources among coexisting slices should address a challenging trade-off: to use resources efficiently while assigning each slice sufficient resources to meet its service level agreement (SLA). We consider the allocation of time-frequency resources from a new perspective: to design a control algorithm capable of learning over the operating network, while keeping the SLA violation rate under an acceptable level during the learning process. For this purpose, traditional model-free reinforcement learning (RL) methods present several drawbacks: low sample efficiency, extensive exploration of the policy space, and inability to discriminate between conflicting objectives, causing inefficient use of the resources and/or frequent SLA violations during the learning process. To overcome these limitations, we propose a model-based RL approach built upon a novel modeling strategy that comprises a kernel-based classifier and a self-assessment mechanism. In numerical experiments, our proposal, referred to as kernel-based RL, clearly outperforms state-of-the-art RL algorithms in terms of SLA fulfillment, resource efficiency, and computational overhead.
      PubDate: Jan. 2023
      Issue No: Vol. 22, No. 1 (2023)
       
  • Leveraging Secondary Reflections and Mitigating Interference in
           Multi-IRS/RIS Aided Wireless Networks

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      Authors: Tu V. Nguyen;Diep N. Nguyen;Marco Di Renzo;Rui Zhang;
      Pages: 502 - 517
      Abstract: Reconfigurable surfaces (RS) have recently emerged as an enabler for smart radio environments where they are used to actively tailor/control the radio propagation (e.g., to support users under adverse channel conditions). If multiple RSs are deployed (e.g., coated on various buildings) to support different groups of users, it is critical to jointly optimize the phase-shifts of all the RSs to mitigate interference amongst them as well as to leverage the secondary reflections amongst them. Motivated by these considerations, this paper considers the uplink transmissions of multiple users that are grouped and supported by multiple RSs to communicate with a multi-antenna base station (BS). We first formulate two optimization problems: the weighted sum-rate maximization and the minimum achievable rate (from all users) maximization. Unlike existing works that considered single user or single RS or multiple RSs without inter-RS reflections, the considered problems require the joint optimization of the phase-shifts of all RS elements and all beamformers at the multi-antenna BS. The two problems turn out to be non-convex and thus are difficult to be solved in general. Moreover, the inter-RS reflections give rise to the coupling of the phase-shifts amongst the RSs, making the optimization problems even more challenging to solve. To tackle them, we design alternating optimization algorithms that provably converge to locally optimal solutions. Simulation results reveal that by effectively mitigating interference and leveraging the secondary reflections amongst the RSs, there is a great benefit of deploying more RSs to support different groups of users so as to achieve a higher rate per user. This gain is even more significant with a larger number of elements per RS. Without properly dealing with the secondary reflections, by contrast, increasing the number of RSs can adversely impact the network throughput, especially for high transmit power.
      PubDate: Jan. 2023
      Issue No: Vol. 22, No. 1 (2023)
       
  • Laser-Powered UAVs for Wireless Communication Coverage: A Large-Scale
           Deployment Strategy

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      Authors: Mohamed-Amine Lahmeri;Mustafa A. Kishk;Mohamed-Slim Alouini;
      Pages: 518 - 533
      Abstract: The use of unmanned aerial vehicles (UAVs) is strongly advocated for sixth-generation (6G) networks, as the 6G standard will not be limited to improving broadband services, but will also target the extension of the geographical cellular coverage. In this context, the deployment of UAVs is considered a key solution for seamless connectivity and reliable coverage. That being said, it is important to underline that although UAVs are characterized by their high mobility and their ability to establish line-of-sight (LOS) links, their use is still impeded by several factors such as weather conditions, their limited computing power, and, most importantly, their limited energy. In this work, we are aiming for the novel technology that enables indefinite wireless power transfer for UAVs using laser beams. We propose a novel UAV deployment strategy, based on which we analyze the overall performance of the system in terms of wireless coverage. To this end, we use tractable tools from stochastic geometry to model the complex communication system. We analyze the user’s connectivity profile under different laser charging capabilities and in different type of environments. We show a decrease in the coverage probability by more than 12% in moderate-to-strong turbulence conditions compared to low turbulence conditions. We also show how the connection rate to the aerial network significantly decreases in favor of the terrestrial network for short laser charging ranges. We conclude that laser-powered drones are considered interesting alternatives when placed in LOS with users, in low-to-moderate optical turbulence, and at reasonable ranges from the charging stations.
      PubDate: Jan. 2023
      Issue No: Vol. 22, No. 1 (2023)
       
  • Hybrid Mechanical and Electronic Beam Steering for Maximizing OAM Channel
           Capacity

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      Authors: Rui Chen;Zhenyang Tian;Wen-Xuan Long;Xiaodong Wang;Wei Zhang;
      Pages: 534 - 549
      Abstract: Radio frequency-orbital angular momentum (RF-OAM) is a novel approach of multiplexing a set of orthogonal modes on the same frequency channel to achieve high spectrum efficiencies. Since OAM requires precise alignment of the transmit and the receive antennas, the electronic beam steering approach has been proposed for the uniform circular array (UCA)-based OAM communication system to circumvent large performance degradation induced by small antenna misalignment in practical environment. However, in the case of large-angle misalignment, the OAM channel capacity cannot be effectively compensated only by the electronic beam steering. To solve this problem, we propose a hybrid mechanical and electronic beam steering scheme, in which mechanical rotating devices controlled by pulse width modulation (PWM) signals as the execution unit are utilized to eliminate the large misalignment angle, while electronic beam steering is in charge of the remaining small misalignment angle caused by perturbations. Furthermore, due to the interferometry, the receive signal-to-noise ratios (SNRs) are not uniform at the elements of the receive UCA. Therefore, a rotatable UCA structure is proposed for the OAM receiver to maximize the channel capacity, in which the simulated annealing algorithm is adopted to obtain the optimal rotation angle at first, then the servo system performs mechanical rotation, at last the electronic beam steering is adjusted accordingly. Both mathematical analysis and simulation results validate that the proposed hybrid mechanical and electronic beam steering scheme can effectively eliminate the effect of diverse misalignment errors of any practical OAM channel and maximize the OAM channel capacity.
      PubDate: Jan. 2023
      Issue No: Vol. 22, No. 1 (2023)
       
  • Concurrent Multiband Direct RF Sampling Receivers

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      Authors: Stephen Henthorn;Timothy O’Farrell;Mohammad Reza Anbiyaei;Kenneth Lee Ford;
      Pages: 550 - 562
      Abstract: Direct radio frequency (RF) sampling receivers are investigated for use in concurrent multiband reception for mobile broadband (MBB) applications. The recent proliferation of different frequency bands and standards in wireless communications has allowed large increases in mobility and throughput, but the number of receivers in a device is limited by physical space and power consumption. Software Defined Radio (SDR) is increasingly being explored to reduce the number of analog RF components required. This paper examines the use of direct RF digitization, allowing tunable and concurrent reception of multiple bands with a single RF front-end. Full mathematical models of both Nyquist and subband sampling receivers are presented and used to investigate a quadband LTE receiver, which is modeled in Simulink and implemented in a hardware-in-the-loop (HWIL) testbed. Individual bands are simulated to have at worst -95dBm sensitivity for 16-QAM with Nyquist sampling and -83dBm with subband sampling. Desensitization of the receivers due to multiband processing is evaluated theoretically and experimentally, showing a maximum of 3dB degradation, which is within the LTE standard for adjacent band interference.
      PubDate: Jan. 2023
      Issue No: Vol. 22, No. 1 (2023)
       
  • Channel Tracking and Prediction for IRS-Aided Wireless Communications

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      Authors: Yi Wei;Ming-Min Zhao;An Liu;Min-Jian Zhao;
      Pages: 563 - 579
      Abstract: For intelligent reflecting surface (IRS)-aided wireless communications, channel estimation is essential and usually requires excessive channel training overhead when the number of IRS reflecting elements is large. The acquisition of accurate channel state information (CSI) becomes more challenging when the channel is not quasi-static due to the mobility of the transmitter and/or receiver. In this work, we study an IRS-aided wireless communication system with a practical channel model that characterizes the time-varying propagation property and propose an innovative two-stage transmission protocol. In the first stage, we send pilot symbols and track the direct/reflected channels based on the received signal, and then data signals are transmitted. In the second stage, instead of sending pilot symbols first, we directly predict the direct/reflected channels and all the time slots are used for data transmission. Based on the proposed transmission protocol, we propose a two-stage channel tracking and prediction (2SCTP) scheme to obtain the direct and reflected channels with low channel training overhead, which is achieved by exploiting the temporal correlation of the time-varying channels. Specifically, we first consider a special case where the IRS-access point (AP) channel is assumed to be static, for which a Kalman filter (KF)-based algorithm and a long short-term memory (LSTM)-based neural network are proposed for channel tracking and prediction, respectively. Then, for the more general case where the IRS-AP, user-IRS and user-AP channels are all assumed to be time-varying, we present a generalized KF (GKF)-based channel tracking algorithm, where proper approximations are employed to handle the underlying non-Gaussian random variables. Numerical simulations are provided to verify the effectiveness of our proposed transmission protocol and channel tracking/prediction algorithms as compared to existing ones.
      PubDate: Jan. 2023
      Issue No: Vol. 22, No. 1 (2023)
       
  • PhysFad: Physics-Based End-to-End Channel Modeling of RIS-Parametrized
           Environments With Adjustable Fading

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      Authors: Rashid Faqiri;Chloé Saigre-Tardif;George C. Alexandropoulos;Nir Shlezinger;Mohammadreza F. Imani;Philipp del Hougne;
      Pages: 580 - 595
      Abstract: Programmable radio environments parametrized by reconfigurable intelligent surfaces (RISs) are emerging as a new wireless communications paradigm, but currently used channel models for the design and analysis of signal-processing algorithms cannot include fading in a manner that is faithful to the underlying wave physics. To overcome this roadblock, we introduce a physics-based end-to-end model of RIS-parametrized wireless channels with adjustable fading (coined PhysFad) which is based on a first-principles coupled-dipole formalism. PhysFad naturally incorporates the notions of space and causality, dispersion (i.e., frequency selectivity) and the intertwinement of each RIS element’s phase and amplitude response, as well as any arising mutual coupling effects including long-range mesoscopic correlations. The latter are induced by reverberation and yield a highly nonlinear parametrization of wireless channels through RISs, a pivotal property which is to date completely overlooked. PhysFad offers the to-date missing tuning knob for physics-compliant adjustable fading. We thoroughly characterize PhysFad and demonstrate its capabilities for a prototypical problem of RIS-enabled over-the-air channel equalization in rich-scattering wireless communications. We also share a user-friendly version of our code to help the community transition towards physics-based models with adjustable fading.
      PubDate: Jan. 2023
      Issue No: Vol. 22, No. 1 (2023)
       
  • Quadratic Displacement Operators—Theory and Application to
           Millimeter-Wave Channel Tracking

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      Authors: Mohammadreza Robaei;Robert Akl;
      Pages: 596 - 610
      Abstract: First, a family of quadratic displacement operators based on group Fourier Transform has been proposed for joint distribution analysis. Second, considering the quadratic displacement operators, a novel millimeter-wave massive MIMO channel tracking has been proposed in Time-Angle (TA) plane. Due to a poor scattering environment, millimeter-wave communication suffers from an ill-conditioned channel matrix. Computationally effective compressed sensing has been proposed to estimate a few dominant paths. These methods rely on orthogonal pilot signals to estimate the channel reliably. However, applying compressed-sensing solutions to the continuous channel leads to significant pilot overhead. To reduce the pilot overhead, one needs to consider the channel dynamics, particularly the spectral overlap between the channel samples. Considering that channel spectral properties can be represented as a joint time-angle distribution, we propose a novel quadratic displacement operator in TA-plane. Considering group Fourier transform in TA-plane, we show that the subderivative of an angular parameter is the dual group of the original angular signal. For a group transform defined over the finite Abelian group, with respect to the finite-dimensional antenna arrays, the purposed time-angle representation matches the cover space of manifold defined in the virtual channel model recommended by A. Sayeed. In TA-plane, the quadratic displacement operator maps old time-angle coordinate onto a new coordinate by taking to account the local spectral properties of the channel samples. By applying the purposed quadratic displacement operator to the Saleh-Valenzuela channel model, the time evolution operator for directional millimeter-wave channel has been derived. Assuming independently identically distributed multipath contribution, we show that channel Wigner distribution is the direct sum of the cluster-wise Wigner distribution. Accordingly, we have shown that the continuous linear time-varia-t channel transfer function can be represented as the Hadamard product between Wigner distribution of the channel and Saleh-Valenzuela model. Considering the fact that the density matrix is a Weyl correspondence of Wigner distribution, a union of subspaces method has been employed to construct the density matrix for channels samples with slightly different states. Numerical results have been presented to evaluate the performance of the proposed channel tracking method.
      PubDate: Jan. 2023
      Issue No: Vol. 22, No. 1 (2023)
       
  • Joint Design for Simultaneously Transmitting and Reflecting (STAR) RIS
           Assisted NOMA Systems

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      Authors: Jiakuo Zuo;Yuanwei Liu;Zhiguo Ding;Lingyang Song;H. Vincent Poor;
      Pages: 611 - 626
      Abstract: Different from traditional reflection-only reconfigurable intelligent surfaces (RISs), simultaneously transmitting and reflecting RISs (STAR-RISs) represent a novel technology, which extends the half-space coverage to full-space coverage by simultaneously transmitting and reflecting incident signals. STAR-RISs provide new degrees-of-freedom (DoF) for manipulating signal propagation. Motivated by the above, a novel STAR-RIS assisted non-orthogonal multiple access (NOMA) (STAR-RIS-NOMA) system is proposed in this paper. Our objective is to maximize the achievable sum rate by jointly optimizing the decoding order, power allocation coefficients, active beamforming, and transmission and reflection beamforming. However, the formulated problem is non-convex with intricately coupled variables. To tackle this challenge, a suboptimal two-layer iterative algorithm is proposed. Specifically, in the inner-layer iteration, for a given decoding order, the power allocation coefficients, active beamforming, transmission and reflection beamforming are optimized alternatingly. For the outer-layer iteration, the decoding order of NOMA users in each cluster is updated with the solutions obtained from the inner-layer iteration. Moreover, an efficient decoding order determination scheme is proposed based on the equivalent-combined channel gains. Simulation results are provided to demonstrate that the proposed STAR-RIS-NOMA system, aided by our proposed algorithm, outperforms conventional RIS-NOMA and RIS assisted orthogonal multiple access (RIS-OMA) systems.
      PubDate: Jan. 2023
      Issue No: Vol. 22, No. 1 (2023)
       
  • Spectrum Surveying: Active Radio Map Estimation With Autonomous UAVs

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      Authors: Raju Shrestha;Daniel Romero;Sundeep Prabhakar Chepuri;
      Pages: 627 - 641
      Abstract: Radio maps find numerous applications in wireless communications and mobile robotics tasks, including resource allocation, interference coordination, and mission planning. Although numerous existing techniques construct radio maps from spatially distributed measurements, the locations of such measurements are predetermined beforehand. In contrast, this paper proposes spectrum surveying, where a mobile robot such as an unmanned aerial vehicle (UAV) collects measurements at a set of locations that are actively selected to obtain high-quality map estimates in a short surveying time. This is performed in two steps. First, two novel algorithms, a model-based online Bayesian estimator and a data-driven deep learning algorithm, are devised for updating a map estimate and an uncertainty metric that indicates the informativeness of measurements at each possible location. These algorithms offer complementary benefits and feature constant complexity per measurement. Second, the uncertainty metric is used to plan the trajectory of the UAV to gather measurements at the most informative locations. To overcome the combinatorial complexity of this problem, a dynamic programming approach is proposed to obtain lists of waypoints through areas of large uncertainty in linear time. Numerical experiments conducted on a realistic dataset confirm that the proposed scheme constructs accurate radio maps quickly.
      PubDate: Jan. 2023
      Issue No: Vol. 22, No. 1 (2023)
       
  • Performance Analysis of Self-Interference Cancellation in Full-Duplex
           Massive MIMO Systems: Subtraction Versus Spatial Suppression

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      Authors: Soo-Min Kim;Yeon-Geun Lim;Linglong Dai;Chan-Byoung Chae;
      Pages: 642 - 657
      Abstract: Massive multiple-input multiple-output (MIMO) and full-duplex (FD) are promising candidates for achieving the spectral efficiency to meet the needs of 5G communications. One essential key to realizing practical FD massive MIMO systems is how to effectively mitigate the self-interference (SI). Conventionally, however, the performance comparison of different SI methods by reflecting the actual channel characteristics was insufficient in the literature. Accordingly, this paper presents a performance analysis of SI cancellation (SIC) methods in FD massive MIMO systems. Analytical and numerical results confirm that, in an imperfect channel-estimation case, the ergodic rates performance of the spatial suppression in the uplink outperforms those of the SI subtraction, due to the correlation between the precoder and the estimation error of the SI channel. In addition, we discuss which method performs better under different given system constraints such as uplink and downlink sum rates, the total transmit power, and the power scaling law.
      PubDate: Jan. 2023
      Issue No: Vol. 22, No. 1 (2023)
       
  • Multipolarization Superposition Beamforming: Novel Scheme of Transmit
           Power Allocation and Subcarrier Assignment

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      Authors: Paul Oh;Sean Kwon;
      Pages: 658 - 670
      Abstract: The 5th generation (5G) new radio (NR) access technology and the beyond-5G future wireless communication require extremely high data rate and spectrum efficiency. Energy-efficient transmission/reception schemes are also regarded as an important component. The polarization domain has attracted substantial attention in this aspect. This paper is the first to propose multi-polarization superposition beamforming (MPS-Beamforming) with cross-polarization discrimination (XPD) and cross-polarization ratio (XPR)-aware transmit power allocation utilizing the 5G NR antenna panel structure. The appropriate orthogonal frequency division multiplexing (OFDM) subcarrier assignment algorithm is also proposed to verify the theoretical schemes via simulations. The detailed theoretical derivation along with comprehensive simulation results illustrate that the proposed novel scheme of MPS-Beamforming is significantly beneficial to the improvement of the performance in terms of the symbol error rate (SER) and signal-to-noise ratio (SNR) gain at the user equipment (UE). For instance, a provided practical wireless channel environment in the simulations exhibits 8 dB SNR gain for 10−4 SER in a deterministic channel, and 4 dB SNR gain for 10−5 SER in abundant statistical channel realizations.
      PubDate: Jan. 2023
      Issue No: Vol. 22, No. 1 (2023)
       
  • Throughput Maximization for UAV-Enabled Integrated Periodic Sensing and
           Communication

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      Authors: Kaitao Meng;Qingqing Wu;Shaodan Ma;Wen Chen;Kunlun Wang;Jun Li;
      Pages: 671 - 687
      Abstract: Driven by unmanned aerial vehicle (UAV)’s advantages of flexible observation and enhanced communication capability, it is expected to revolutionize the existing integrated sensing and communication (ISAC) system and promise a more flexible joint design. Nevertheless, the existing works on ISAC mainly focus on exploring the performance of both functionalities simultaneously during the entire considered period, which may ignore the practical asymmetric sensing and communication requirements. In particular, always forcing sensing along with communication may make it is harder to balance between these two functionalities due to shared spectrum resources and limited transmit power. To address this issue, we propose a new integrated periodic sensing and communication (IPSAC) mechanism for the UAV-enabled ISAC system to provide a more flexible trade-off between two integrated functionalities. Specifically, the system achievable rate is maximized via jointly optimizing UAV trajectory, user association, target sensing selection, and transmit beamforming, while meeting the sensing frequency and beam pattern gain requirement for the given targets. Despite that this problem is highly non-convex and involves closely coupled integer variables, we derive the closed-form optimal beamforming vector to dramatically reduce the complexity of beamforming design, and present a tight lower bound of the achievable rate to facilitate UAV trajectory design. Based on the above results, we propose a two-layer penalty-based algorithm to efficiently solve the considered problem. To draw more important insights, the optimal achievable rate and the optimal UAV location are analyzed under a special case of infinity number of antennas. Furthermore, we prove the structural symmetry between the optimal solutions in different ISAC frames without location constraints in our considered UAV-enabled ISAC system. Based on this, we propose an efficient algorithm for solving the problem wi-h location constraints. Numerical results validate the effectiveness of our proposed designs and also unveil a more flexible trade-off in ISAC systems over benchmark schemes.
      PubDate: Jan. 2023
      Issue No: Vol. 22, No. 1 (2023)
       
  • Dynamic Data Collection and Neural Architecture Search for Wireless Edge
           Intelligence Systems

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      Authors: Benshun Yin;Zhiyong Chen;Meixia Tao;
      Pages: 688 - 703
      Abstract: With the booming development of Internet of things (IoT) devices and machine learning (ML) technique, edge machine learning is emerging to process the enormous sampled data for realizing intelligent applications at the network edge. With limited edge resources, a well-structured neural network and numerous training data are the two main factors that affect the performance of edge machine learning. In this paper, we cooperatively optimize the data collection and the neural architecture to minimize the energy consumption of devices and the error on a specific task. We derive the Rademacher complexity bounds theoretically to evaluate the generalization error of the neural architectures in the search space and then formulate the optimization problem accordingly. Then we develop a scheme to solve the problem that dynamically performs the data collection based on policy gradient reinforcement learning and the parameter-sharing neural architecture search (NAS) algorithm. By this way, the transmission power of each device can be adjusted based on the data quality assessed by the NAS result in each round to effectively collect data. And with the growing high-quality data, the NAS algorithm can gradually find the optimal architecture for the task. Experimental results show that the neural architectures found by the proposed algorithm outperform the existing architectures while saving energy in the device.
      PubDate: Jan. 2023
      Issue No: Vol. 22, No. 1 (2023)
       
  • Message-Passing Based User Association and Bandwidth Allocation in HetNets
           With Wireless Backhaul

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      Authors: Hongju Lee;Junhee Park;Sang Hyun Lee;Inkyu Lee;
      Pages: 704 - 717
      Abstract: This work presents a joint design of user association and resource allocation in a heterogeneous network, which is comprised of a single macro base station and a group of small base stations interconnected through wireless backhaul. In such a configuration, we optimize user association and resource allocation so that the total sum of generalized utilities is maximized. This problem is cast as a combinatorial formulation with a fractional objective. To handle this design challenge, we develop a novel message-passing framework to obtain an efficient joint autonomous solution for user association and resource allocation. The simulation results show that the proposed algorithm outperforms existing techniques with various network utility functions.
      PubDate: Jan. 2023
      Issue No: Vol. 22, No. 1 (2023)
       
  • Covert Beamforming Design for Integrated Radar Sensing and Communication
           Systems

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      Authors: Shuai Ma;Haihong Sheng;Ruixin Yang;Hang Li;Youlong Wu;Chao Shen;Naofal Al-Dhahir;Shiyin Li;
      Pages: 718 - 731
      Abstract: We propose covert beamforming design frameworks for integrated radar sensing and communication (IRSC) systems, where the radar can covertly communicate with legitimate users under the cover of the probing waveforms without being detected by the eavesdropper. Specifically, by jointly designing the target detection beamformer and communication beamformer, we aim to maximize the radar detection mutual information (MI) (or the communication rate) subject to the covert constraint, the communication rate constraint (or the radar detection MI constraint), and the total power constraint. For the perfect eavesdropper’s channel state information (CSI) scenario, we transform the covert beamforming design problems into a series of convex subproblems, by exploiting semidefinite relaxation, which can be solved via the bisection search method. Considering the high complexity of iterative optimization, we further propose a single-iterative covert beamformer design scheme based on the zero-forcing criterion. For the imperfect eavesdropper’s CSI scenario, we develop a relaxation and restriction method to tackle the robust covert beamforming design problems. Simulation results demonstrate the effectiveness of the proposed covert beamforming schemes for perfect and imperfect CSI scenarios.
      PubDate: Jan. 2023
      Issue No: Vol. 22, No. 1 (2023)
       
  • IEEE Open Access

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      Pages: 732 - 732
      Abstract: Presents information on the above named publication.
      PubDate: Jan. 2023
      Issue No: Vol. 22, No. 1 (2023)
       
 
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