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IEEE Wireless Communications Letters
Journal Prestige (SJR): 0.678  Citation Impact (citeScore): 3 Number of Followers: 41  Hybrid journal (It can contain Open Access articles)ISSN (Online) 2162-2337 Published by IEEE  [228 journals]
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- IEEE Wireless Communications Letters Publication Information
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PubDate: March 2023
Issue No: Vol. 12, No. 3 (2023)
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- IEEE Communications Society Information
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PubDate: March 2023
Issue No: Vol. 12, No. 3 (2023)
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- QoS Guaranteed Power Minimization and Beamforming for IRS-Assisted NOMA
Systems-
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Authors: Guoquan Li;Hui Zhang;Yuhui Wang;Yongjun Xu;
Pages: 391 - 395
Abstract: In this letter, to solve the problem of large interference among users and reduce the transmit power consumption of base station while guaranteeing target quality of service (QoS) in downlink intelligent reflecting surface (IRS) assisted non-orthogonal multiple access (NOMA) systems, we present a joint optimization algorithm to design active beamforming for base station and passive beamforming (phase shift matrix) for IRS based on semidefinite relaxation (SDR). The non-convex constraint of signal-to-interference-plus-noise ratio (SINR) is transformed into affine constraint by increasing the dimension of optimization variables. The two subproblems of transmit power minimization and phase shift feasibility are solved iteratively using alternating optimization. To solve the computing performance degradation of SDR in largescale problems, we further propose a low-complexity algorithm based on successive convex approximation (SCA) and the original problem is then determined iteratively by relaxing the constant modulus constraint of IRS. Simulation results show that both algorithms have lower power consumption than the existing algorithms with different number of IRS reflecting elements or transmit antennas, and the SCA algorithm can approach the performance of SDR with lower complexity.
PubDate: March 2023
Issue No: Vol. 12, No. 3 (2023)
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- Cooperative Resource Allocation Based on Soft Actor–Critic With Data
Augmentation in Cellular Network-
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Authors: Yunhui Qin;Zhongshan Zhang;Wei Huangfu;Haijun Zhang;Keping Long;
Pages: 396 - 400
Abstract: This letter investigates the cooperative resource allocation of cellular networks with simultaneous wireless information and power transfer in the time-varying channel environment. The soft actor-critic (SAC) algorithm is exploited to tackle the optimization problem which aims to find a feasible resource allocation policy to maximize the data rate and system fairness while minimizing the channel switching penalty. Considering the costly agent-to-environment interactions and the restricted empirical dataset of the SAC algorithm, this letter explores the permutation equivalence of the optimization objective, and designs two data augmentation schemes for the experience replay buffer of SAC. The cumulative discount reward shows that data augmentation assisted algorithms outperform the baseline in the learning speed. The simulation results referring to the average data rate and system fairness show that the proposed schemes benefit to the training model and effectively improve the performance of algorithms.
PubDate: March 2023
Issue No: Vol. 12, No. 3 (2023)
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- Poisoning Bearer Context Migration in O-RAN 5G Network
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Authors: Sanaz Soltani;Mohammad Shojafar;Alessandro Brighente;Mauro Conti;Rahim Tafazolli;
Pages: 401 - 405
Abstract: Open Radio Access Network (O-RAN) improves the flexibility and programmability of the 5G network by applying the Software-Defined Network (SDN) principles. O-RAN defines a near-real time Radio Intelligent Controller (RIC) to decouple the RAN functionalities into the control and user planes. Although the O-RAN security group offers several countermeasures against threats, RIC is still prone to attacks. In this letter, we introduce a novel attack, named Bearer Migration Poisoning (BMP), that misleads the RIC into triggering a malicious bearer migration procedure. The adversary aims to change the user plane traffic path and causes significant network anomalies such as routing blackholes. BMP has a remarkable feature that even a weak adversary with only two compromised hosts could launch the attack without compromising the RIC, RAN components, or applications. Based on our numerical results, the attack imposes a dramatic increase in signalling cost by approximately 10 times. Our experiment results show that the attack significantly degrades the downlink and uplink throughput to nearly 0 Mbps, seriously impacting the service quality and end-user experience.
PubDate: March 2023
Issue No: Vol. 12, No. 3 (2023)
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- Downlink CSI Recovery in Massive MIMO Systems by Proactive Sensing
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Authors: Lei Li;Minghe Zhu;Shuqiang Xia;Tsung-Hui Chang;
Pages: 406 - 410
Abstract: Accurate channel state information (CSI) is critical to harvest the potential gain brought by massive multiple-input multiple-output (MIMO). However, the acquisition of downlink CSI is challenging in the frequency division duplex (FDD) systems due to limited feedback and the use of low-resolution codebooks. In this letter, we propose a novel sensing-assisted CSI recovery (SACR) scheme, where the BS proactively senses the downlink channel structure so that accurate CSI recovery at the BS can be achieved with only a few number of low-resolution user feedbacks. Numerical results show that the proposed scheme significantly outperforms the existing methods.
PubDate: March 2023
Issue No: Vol. 12, No. 3 (2023)
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- Active IRS-Assisted Integrated Sensing and Communication in C-RAN
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Authors: Yu Zhang;Jiachi Chen;Caijun Zhong;Hong Peng;Weidang Lu;
Pages: 411 - 415
Abstract: This letter investigates an active intelligent reflecting surface (IRS)-aided integrated sensing and communication (ISAC) system in the scenario of cloud radio access network (C-RAN). In the system, the active IRS is deployed to amplify and reflect the joint communication and sensing signals from the remote radio heads (RRHs). Our goal is to form the desired reflective beam pattern from the active IRS towards the sensing targets located in non-line-of-sight (NLoS) areas for the RRHs, while satisfying the signal-to-interference-plus-noise ratio (SINR) requirement for the communication users and the capacity limit for the fronthaul links between the baseband unit (BBU) pool and RRHs. Specifically, we propose an efficient joint design for the fronthaul compression and the beamforming of the RRHs and the active IRS. Via numerical simulations, the effectiveness of the proposed design is verified.
PubDate: March 2023
Issue No: Vol. 12, No. 3 (2023)
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- Low-Complexity Suboptimal ML Detection for OFDM-IM Systems
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Authors: Kee-Hoon Kim;
Pages: 416 - 420
Abstract: Orthogonal frequency division multiplexing with index modulation (OFDM-IM) is a novel multicarrier scheme, which uses ${k}$ out of ${n}$ subcarriers as active subcarriers to transmit data. For detecting the subcarrier activation pattern (SAP) at the receiver, maximum likelihood (ML) detection cannot be used because of its high computational complexity. Instead, the detector selecting the most likely active ${k}$ subcarriers is used, which is called a ${k}$ largest values ( ${k}$ lv) detector. However, this method degrades the detection performance especially if the ratio of illegal SAPs to SAPs is high. In this letter, the suboptimal ML detector is proposed, which is a simple modification of the ${k}$ lv detector, but very efficient. The proposed detector has a similar detection performance compared to the ML detection, which is suitable for flexible implementation of OFDM-IM systems.
PubDate: March 2023
Issue No: Vol. 12, No. 3 (2023)
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- An Attention-Assisted UWB Ranging Error Compensation Algorithm
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Authors: Xu He;Lingfei Mo;Qing Wang;
Pages: 421 - 425
Abstract: Ultra wide-band (UWB) brings many benefits to wireless sensing. Theoretically, UWB can achieve centimeter-level ranging accuracy. However, in practical applications, multipath fading channel (MPF) problems and antenna delay effects will adversely affect the ranging accuracy of UWB. The sample data of UWB contain rich channel characteristics, which can be extracted to characterize the implicit relation of UWB ranging error. Undoubtedly, the UWB channel characteristics will show different importance in characterizing UWB ranging errors in different environments. Therefore, this letter proposes an attention-assisted UWB ranging error compensation algorithm. Using the attention mechanism, the significance of the extracted UWB channel characteristics in various environments can be re-evaluated to improve the performance of the deep neural network (DNN) model. The experimental results prove that using the proposed algorithm, the 75% error lines of the original indoor and outdoor ranging errors are reduced from 13.32 cm and 19.41 cm to 5.74 cm and 5.05 cm, and median errors are compensated from 7.00 cm and 15.42 cm to 2.78 cm and 2.69 cm, respectively.
PubDate: March 2023
Issue No: Vol. 12, No. 3 (2023)
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- Optimization of Spectrum Utilization Efficiency in Cognitive Radio
Networks-
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Authors: Mohsin Ali;Muhammad Naveed Yasir;Dost Muhammad Saqib Bhatti;Haewoon Nam;
Pages: 426 - 430
Abstract: Cognitive radio (CR) is considered as a key technology to overcome the problem of spectrum scarcity for wireless applications by the researchers. Efficient spectrum utilization is the most important and core purpose of CR systems after successful spectrum sensing. In this letter, different cases are investigated in which secondary users (SUs) can transmit their data after declaring the primary user (PU) is absent as a result of spectrum sensing. SU’s transmission in some of these cases may lead to interference in PU’s communication, which may cause inefficient spectrum utilization. In this letter, an optimization problem is formulated to optimize the sensing time that maximizes the spectrum utilization efficiency (SUE) under the constraints of high target detection probability and low probability of interference to PU. A trade-off between sensing time and spectrum utilization efficiency is shown in a given time frame for all possible cases. Theoretical and simulation results of proposed system show good match to each others. Finally proposed system is compared with conventional systems and 45% better performance in terms of optimal sensing time is achieved with maximum SUE.
PubDate: March 2023
Issue No: Vol. 12, No. 3 (2023)
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- Joint User Scheduling and Phase Shift Design for RIS Assisted Multi-Cell
MISO Systems-
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Authors: Luoluo Jiang;Xiao Li;Michail Matthaiou;Shi Jin;
Pages: 431 - 435
Abstract: This letter investigates the joint user scheduling and phase shift design for reconfigurable intelligent surface (RIS) assisted multi-cell downlink systems. A closed-form ergodic sum spectral efficiency (SE) approximation is utilized as the optimization metric. Based on this approximation, we schedule the users, whose cascaded channels are mostly correlated with each other’s, to maximize each user’s effective signal. Moreover, the RIS phase shift is designed to be the mean of the scheduled users’ cascaded channel phases. With the proposed transmission design, we find the optimal RIS deployment to achieve the highest maximum throughput which depends only on the relative locations of the BSs and RIS. In addition, we consider a more practical discrete RIS phase shift design based on a discrete Fourier transform (DFT) codebook. Simulation results show that the proposed low-complexity scheduling algorithm performs well.
PubDate: March 2023
Issue No: Vol. 12, No. 3 (2023)
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- Robust Channel Invariant Deep Noncooperative Spectrum Sensing
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Authors: Zhengyang Su;Kah Chan Teh;Sirajudeen Gulam Razul;Alex C. Kot;
Pages: 436 - 440
Abstract: Deep learning (DL) has been introduced to cognitive radio network to solve the problem of spectrum scarcity and further enhance the spectrum utilization. However, many DL-based spectrum sensing methods are sensitive to the environment, which means the sensing model needs to be re-trained with a large number of labelled samples in a new environment. In this letter, we propose a novel DL-based channel environment-robust spectrum sensing network named ER-SNet, which contains the encoder part extracting channel invariant features and the classifier part for true hypothesis prediction. Extensive simulations have been conducted to show the performance improvement and robustness of the proposed algorithm in sensing weak signals over different channel conditions.
PubDate: March 2023
Issue No: Vol. 12, No. 3 (2023)
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- Sensing-Aided Uplink Channel Estimation for Joint Communication and
Sensing-
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Authors: Xu Chen;Zhiyong Feng;J. Andrew Zhang;Zhiqing Wei;Xin Yuan;Ping Zhang;
Pages: 441 - 445
Abstract: The joint communication and sensing (JCAS) technique has drawn great attention due to its high spectrum efficiency by using the same transmit signal for both communication and sensing. Exploiting the correlation between the uplink (UL) channel and the sensing results, we propose a sensing-aided Kalman filter (SAKF)-based channel state information (CSI) estimation method for UL JCAS, which exploits the angle-of-arrival (AoA) estimation to improve the CSI estimation accuracy. A Kalman filter (KF)-based CSI enhancement method is proposed to refine the least-square CSI estimation by exploiting the estimated AoA as the prior information. Simulation results show that the bit error rates (BER) of UL communication using the proposed SAKF-based CSI estimation method approach those using the minimum mean square error (MMSE) method, while at significantly reduced complexity.
PubDate: March 2023
Issue No: Vol. 12, No. 3 (2023)
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- RIS-Assisted Cooperative NOMA With SWIPT
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Authors: Juanjuan Ren;Xianfu Lei;Zhangjie Peng;Xiaohu Tang;Octavia A. Dobre;
Pages: 446 - 450
Abstract: This letter proposes a two-stage reconfigurable intelligent surface (RIS)-assisted transmission scheme for cooperative non-orthogonal multiple access networks with simultaneous wireless information and power transfer. We focus on improving the achievable rate of the strong user with guaranteed weak user’s quality of service by jointly optimizing power splitting factors, beamforming coefficients, and RIS reflection coefficients in two transmission stages. To tackle this challenging problem, we first use the alternating optimization framework to transform it into three subproblems, and then apply the penalty-based arithmetic-geometric mean approximation algorithm and the successive convex approximation-based method to solve them. Numerical results verify the superiority of the proposed algorithm over the baseline schemes.
PubDate: March 2023
Issue No: Vol. 12, No. 3 (2023)
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- Hybrid Beamforming Design for ITS-Assisted Wireless Networks
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Authors: Wannian Du;Zheng Chu;Gaojie Chen;Pei Xiao;Zihuai Lin;Cheng Huang;Wanming Hao;
Pages: 451 - 455
Abstract: This letter proposes a hybrid beamforming design for an intelligent transmissive surface (ITS)-assisted transmitter wireless network. We aim to suppress the sidelobes and optimize the mainlobes of the transmit beams by minimizing the proposed cost function based on the least squares (LS) for the digital beamforming vector of the base station (BS) and the phase shifts of the ITS. To solve the minimization problem, we adopt an efficient algorithm based on the alternating optimization (AO) method to design the digital beamforming vector and the phase shifts of the ITS in an alternating manner. In particular, the alternating direction method of multipliers (ADMM) algorithm is utilized to obtain the optimal phase shifts of the ITS. Finally, we verify the improvement achieved by the proposed algorithm in terms of the beam response compared to the benchmark schemes by the simulation results.
PubDate: March 2023
Issue No: Vol. 12, No. 3 (2023)
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- Learning From Noisy Labels for MIMO Detection With One-Bit ADCs
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Authors: Jinsung Park;Namyoon Lee;Song-Nam Hong;Yo-Seb Jeon;
Pages: 456 - 460
Abstract: This letter presents a data detection method for multiple-input multiple-output systems with one-bit analog-to-digital converters. The basic idea is to learn the likelihood function of the system from training samples. To this end, a training data generation strategy is first proposed, which labels a one-bit received signal with a symbol index determined by channel-based data detection. This strategy requires no extra training overhead beyond pilot symbols for channel estimation, but leads to noisy labels due to data detection errors. For accurate learning from the noisy labels, an expectation-maximization algorithm is also developed. This algorithm learns both the likelihood function and the transition probability from each noisy label to a true label. Numerical results demonstrate that the presented method performs similar to the optimal maximum likelihood detection.
PubDate: March 2023
Issue No: Vol. 12, No. 3 (2023)
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- Ultra-Reliable and Low-Latency Multiple-Antenna Communications in the High
SNR Regime-
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Authors: Changkun Li;Yalei Wang;Wei Chen;H. Vincent Poor;
Pages: 461 - 465
Abstract: Emerging time-sensitive applications such as automated driving, factory automation, and telesurgery have stimulated an increased interest in Ultra-Reliable and Low-Latency Communications (URLLC). Meanwhile, Multiple-Input Multiple-Output (MIMO) techniques have played a vital role in radio access networks since the 2000s, due to their spatial multiplexing and diversity gains over fading channels. Thus, it is of interest to consider the fundamental performance limits of URLLC operating over MIMO channels. In this letter, we consider this problem in the asymptotic regime of high Signal-to-Noise Ratio (SNR) for short packets involving finite-blocklength codes. In particular, we show that a fundamental tradeoff exists among service capability, latency, and error probability in the high SNR regime. We present a piecewise linear gain conservation equation to characterize this cross-layer tradeoff, which is also validated by extensive numerical results.
PubDate: March 2023
Issue No: Vol. 12, No. 3 (2023)
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- A Lattice-Reduction-Aided Sphere Decoder for Underwater Acoustic FBMC/OQAM
Communications-
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Authors: Xuesong Lu;Yulin Jiang;Yan Wei;Xingbin Tu;Fengzhong Qu;
Pages: 466 - 470
Abstract: Because of the inherent interference, it is difficult for filter-bank multi-carrier (FBMC) with the offset-quadrature amplitude modulation (OQAM) systems to achieve high-performance symbol detection, especially over the underwater acoustic (UWA) channels. This letter explores a novel receiver approach to solve this problem by establishing a unified transmission matrix (UTM) model and proposing a lattice-reduction-aided sphere decoder (LRA-SD). The simulation and sea trial results indicate that in the UWA FBMC/OQAM communication systems, the UTM-based LRA-SD outperforms the linear detectors and is more computationally efficient than the maximum likelihood (ML) detector.
PubDate: March 2023
Issue No: Vol. 12, No. 3 (2023)
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- AcsiNet: Attention-Based Deep Learning Network for CSI Prediction in FDD
MIMO Systems-
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Authors: Ya Jiang;Wenbin Lin;Weikun Zhao;Chaofeng Wang;
Pages: 471 - 475
Abstract: In 5G frequency division duplex (FDD) systems, the user equipment needs to feedback the measured downlink channel state information (CSI) to the base station to improve the throughput. For massive multiple-input-multiple-output (MIMO) systems, each antenna in base station needs its CSI feedback, which results in significant transmission overhead and latency. We propose an attention-based deep learning network to directly predict the downlink CSI from the corresponding uplink one, eliminating the feedback overhead completely. Specifically, the uplink CSI is first compressed based on the 3D inverse discrete Fourier transform, then is fed into an attention-based deep learning network which can focus on key CSI characteristics. The simulation results show that the proposed method achieves high prediction accuracy and low complexity, indicating prospective applications in FDD massive MIMO systems.
PubDate: March 2023
Issue No: Vol. 12, No. 3 (2023)
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- Neuromorphic Integrated Sensing and Communications
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Authors: Jiechen Chen;Nicolas Skatchkovsky;Osvaldo Simeone;
Pages: 476 - 480
Abstract: Neuromorphic computing is an emerging technology that support event-driven data processing for applications requiring efficient online inference and/or control. Recent work has introduced the concept of neuromorphic communications, whereby neuromorphic computing is integrated with impulse radio (IR) transmission to implement low-energy and low-latency remote inference in wireless Internet-of-Things (IoT) networks. In this letter, we introduce neuromorphic integrated sensing and communications (N-ISAC), a novel solution that enables efficient online data decoding and radar sensing. N-ISAC leverages a common IR waveform for the dual purpose of conveying digital information and of detecting the presence or absence of a radar target. A spiking neural network (SNN) is deployed at the receiver to decode digital data and to detect the radar target using directly the received signal. The SNN operation is optimized by balancing performance metrics for data communications and radar sensing, highlighting synergies and trade-offs between the two applications.
PubDate: March 2023
Issue No: Vol. 12, No. 3 (2023)
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- On the Sum Spectral Efficiency of Dynamic TDD-Enabled Cell-Free Massive
MIMO Systems-
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Authors: Anubhab Chowdhury;Chandra R. Murthy;
Pages: 481 - 485
Abstract: We examine the sum spectral efficiency (SE) performance of a cell-free massive multiple-input multiple-output (CF-mMIMO) system, where each access point (AP) can operate either in the uplink or downlink mode in each slot, corresponding to dynamic time-division duplexing (DTDD) across the APs. We derive the sum SE of the system under a weighted combining of the signals received at the central processing unit. We show that the sum SE is a sub-modular function of the subset of active APs. We exploit this to develop a novel, low-complexity, greedy algorithm for choosing the mode of operation of the APs which is guaranteed to achieve within $(1-1/e)$ of the sum SE attained via a full-complexity brute-force search. Our results show that DTDD with greedy AP mode selection can nearly double the sum SE compared to a TDD based CF-system where all APs operate in the uplink or downlink modes simultaneously. Thus, it is a promising duplexing scheme for beyond 5G communications.
PubDate: March 2023
Issue No: Vol. 12, No. 3 (2023)
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- A Knowledge-Based Deep Learning Detection Scheme for Downlink SCMA Systems
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Authors: Yu Zheng;Xiaoming Hou;Hui Wang;Ming Jiang;Shengli Zhang;
Pages: 486 - 490
Abstract: Sparse code multiple access (SCMA) is a promising code-domain non-orthogonal multiple access technology for future wireless communication systems. In the SCMA detector, deep learning (DL) technology has been adopted to improve the detection efficiency. However, most previous schemes are completely data-driven designed without using prior knowledge. In this letter, we propose a knowledge-based deep learning detection scheme (K-DLD) that incorporates prior knowledge into SCMA detection to lighten the neural network. Moreover, we propose to use a thinner but deeper network to further reduce the model complexity. Simulation results show that the proposed schemes significantly reduce the computation time without any performance loss.
PubDate: March 2023
Issue No: Vol. 12, No. 3 (2023)
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- Gaining From Multiple Ambient Sources: Signaling Matrix for Multi-Antenna
Backscatter Devices-
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Authors: Xiyu Wang;Hüseyin Yiğitler;Riku Jäntti;
Pages: 491 - 495
Abstract: The backscatter device (BD) in ambient backscatter communication (AmBC) systems is often illuminated by multiple ambient sources in reality. Although multiple ambient sources can benefit the AmBC systems, this scenario has been rarely studied. This letter gives an answer to the overlooked question of how to effectively obtain the gain from multiple ambient sources—using a proper signaling matrix on a multi-antenna BD. We optimize the BD signaling matrix based on the criterion of minimizing the AmBC system bit-error-rate (BER) performance. The derived signaling matrices combine the ambient signals and steer the backscatter signal toward the receiver to increase the received signal strength. The simulation results show that the multi-antenna BD effectively uses multiple ambient sources, and the derived BD signaling matrices robustly achieve a larger communication range or a higher datarate of AmBC systems.
PubDate: March 2023
Issue No: Vol. 12, No. 3 (2023)
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- A Linear Time Algorithm for the Optimal Discrete IRS Beamforming
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Authors: Shuyi Ren;Kaiming Shen;Xin Li;Xin Chen;Zhi-Quan Luo;
Pages: 496 - 500
Abstract: It remains an open problem to find the optimal configuration of phase shifts under the discrete constraint for intelligent reflecting surface (IRS) in polynomial time. The above problem is widely believed to be difficult because it is not linked to any known combinatorial problems that can be solved efficiently. The branch-and-bound algorithms and the approximation algorithms constitute the best results in this area. Nevertheless, this letter shows that the global optimum can actually be reached in linear time on average in terms of the number of reflective elements (REs) of IRS. The main idea is to geometrically interpret the discrete beamforming problem as choosing the optimal point on the unit circle. Although the number of possible combinations of phase shifts grows exponentially with the number of REs, it turns out that there are only a linear number of circular arcs that possibly contain the optimal point. Furthermore, the proposed algorithm can be viewed as a novel approach to a special case of the discrete quadratic program (QP).
PubDate: March 2023
Issue No: Vol. 12, No. 3 (2023)
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- A Novel Two-Parametric ISI-Free Pulse Based on Inverse Hyperbolic
Functions-
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Authors: Dimitrios Tyrovolas;Songbing Liang;George K. Karagiannidis;Stylianos D. Assimonis;
Pages: 501 - 504
Abstract: In this letter, a new two-parametric pulse that outperforms state-of-the-art pulses with the best performance in terms of BER is proposed, which is studied in terms of frequency and time domain characteristics. Specifically, the pulse is based on the inverse-hyperbolic functions acsch and asech which are used for the first time for an ISI-free pulse, and its design depends only on the roll-off factor and the timing jitter parameter (i.e., two-parametric design). Finally, the proposed pulse is shown to outperform most of the well-known pulses reported in the literature, since it presents lower error probability, smaller maximum distortion and wider eye-diagram.
PubDate: March 2023
Issue No: Vol. 12, No. 3 (2023)
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- Resource Allocation for RSMA-Based Coordinated Direct and Relay
Transmission-
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Authors: Haoran Pang;Fei Ji;Lexi Xu;Yuan Liu;Miaowen Wen;
Pages: 505 - 509
Abstract: In this letter, we investigate a rate splitting multiple access (RSMA) based coordinated direct and relay transmission (CDRT) system, where a base station (BS) serves two users using the rate splitting strategy, and a decode-and-forward (DF) relay aids the BS in establishing a communication link with a far user. We focus on a max-min fairness problem aiming at maximizing the minimum achievable rate of two users. The precoding vectors of the BS and the relay, the common rate of each user and the time allocation coefficients are jointly optimized under the constraint of limited system resources. Unfortunately, the optimization problem is non-convex due to the coupled optimization variables and the non-convex objective. Therefore, we transform the original problem into a difference of convex (DC) program and obtain a sub-optimal solution by adopting an iterative constrained concave convex procedure (CCCP) based algorithm. Simulation results demonstrate substantial performance gains of our proposed RSMA-CDRT over the conventional CDRT strategies and the flexibility in adapting to various channel environments.
PubDate: March 2023
Issue No: Vol. 12, No. 3 (2023)
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- Semantic Communications With Discrete-Time Analog Transmission: A PAPR
Perspective-
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Authors: Yulin Shao;Deniz Gunduz;
Pages: 510 - 514
Abstract: Recent progress in deep learning (DL)-based joint source-channel coding (DeepJSCC) has led to a new paradigm of semantic communications. Two salient features of DeepJSCC-based semantic communications are the exploitation of semantic-aware features directly from the source signal, and the discrete-time analog transmission (DTAT) of these features. Compared with traditional digital communications, semantic communications with DeepJSCC provide superior reconstruction performance at the receiver and graceful degradation with diminishing channel quality, but also exhibit a large peak-to-average power ratio (PAPR) in the transmitted signal. An open question has been whether the gains of DeepJSCC come at the expense of high-PAPR continuous-amplitude signal, which can limit its adoption in practice. In this letter, we first show that conventional DeepJSCC does suffer from high PAPR. Then, we explore three PAPR reduction techniques and confirm that the superior image reconstruction performance of DeepJSCC can be retained while the PAPR is suppressed to an acceptable level. This is an important step towards the implementation of DeepJSCC in practical semantic communication systems.
PubDate: March 2023
Issue No: Vol. 12, No. 3 (2023)
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- A Wideband Generalization of the Near-Field Region for Extremely Large
Phased-Arrays-
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Authors: Nitish Deshpande;Miguel R. Castellanos;Saeed R. Khosravirad;Jinfeng Du;Harish Viswanathan;Robert W. Heath;
Pages: 515 - 519
Abstract: The narrowband and far-field assumption in conventional wireless system design leads to a mismatch with the optimal beamforming required for wideband and near-field systems. This discrepancy is exacerbated for larger apertures and bandwidths. To characterize the behavior of near-field and wideband systems, we derive the beamforming gain expression achieved by a frequency-flat phased array designed for plane-wave propagation. To determine the far-field to near-field boundary for a wideband system, we propose a frequency-selective distance metric. The proposed far-field threshold increases for frequencies away from the center frequency. The analysis results in a fundamental upper bound on the product of the array aperture and the system bandwidth. We present numerical results to illustrate how the gain threshold affects the maximum usable bandwidth for the n260 and n261 5G NR bands.
PubDate: March 2023
Issue No: Vol. 12, No. 3 (2023)
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- Cost-Efficient RIS-Assisted Transmitter Design With Discrete Phase Shifts
for Wireless Communication-
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Authors: Xiangyu Pi;Pengfei Yi;Zhenyu Xiao;Wei Zhang;Zhu Han;Xiang-Gen Xia;
Pages: 520 - 524
Abstract: In this letter, in order to achieve higher spectral and energy efficiency, we propose a novel cost-efficient transmitter conceptual design based on reconfigurable intelligent surface (RIS) with discrete phase shifts. The key idea is to directly utilize the digital signal to adjust the discrete reflection coefficients of RIS, resulting that the phases of the reflected carrier signal being modulated without the need for complex digital signal processing (DSP) hardware and costly radio frequency (RF) chains. Furthermore, a joint digital modulation and beamforming method is developed to enable information transmission as well as enhance signal strength. Based on the proposed transmitter, we derive the closed-form expressions of the signal-to-noise ratio (SNR) and bit error rate (BER) of the received signal and analyze the impact of hardware constraints on communication performance. Extensive simulation results validate that the novel design of RIS-assisted transmitter provides a cost-effective and power-efficient solution for wireless communications.
PubDate: March 2023
Issue No: Vol. 12, No. 3 (2023)
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- Spatial Signal Design for Positioning via End-to-End Learning
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Authors: Steven Rivetti;José Miguel Mateos-Ramos;Yibo Wu;Jinxiang Song;Musa Furkan Keskin;Vijaya Yajnanarayana;Christian Häger;Henk Wymeersch;
Pages: 525 - 529
Abstract: This letter considers the problem of end-to-end (E2E) learning for joint optimization of transmitter precoding and receiver processing for mmWave downlink positioning. Considering a multiple-input single-output (MISO) scenario, we propose a novel autoencoder (AE) architecture to estimate user equipment (UE) position with multiple base stations (BSs) and demonstrate that E2E learning can match model-based design, both for angle-of-departure (AoD) and position estimation, under ideal conditions without model deficits and outperform it in the presence of hardware impairments.
PubDate: March 2023
Issue No: Vol. 12, No. 3 (2023)
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- Efficient MIMO PHY Abstraction With Imperfect CSI for Fast Simulations
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Authors: Liu Cao;Lyutianyang Zhang;Sian Jin;Sumit Roy;
Pages: 530 - 534
Abstract: This letter continues our prior work in Jin et al. (2021) on efficient PHY layer abstractions for scaling link simulations for complex scenarios to include the essential real-world impact of imperfect channel estimation. We extend the EESM-log-SGN abstraction by incorporating a model for effective SINR including channel estimation error, for multiple-input and multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) configurations, tailored towards IEEE 802.11ac/ax networks. The developed methods are then validated under different MIMO configurations for subsequent inclusion in ns-3 (www.nsnam.org) based cross-layer network performance evaluation.
PubDate: March 2023
Issue No: Vol. 12, No. 3 (2023)
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- Deep Learning of Near Field Beam Focusing in Terahertz Wideband Massive
MIMO Systems-
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Authors: Yu Zhang;Ahmed Alkhateeb;
Pages: 535 - 539
Abstract: Employing large antenna arrays and utilizing large bandwidth have the potential of bringing very high data rates to future wireless communication systems. However, this brings the system into the near-field regime and also makes the conventional transceiver architectures suffer from the wideband effects. To address these problems, in this letter, we propose a low-complexity frequency-aware beamforming solution that is designed for hybrid time-delay and phase-shifter based RF architectures. To reduce the complexity, the joint design problem of the time delays and phase shifts is decomposed into two subproblems, where a signal model inspired online learning framework is proposed to learn the shifts of the quantized analog phase shifters, and a low-complexity geometry-assisted method is leveraged to configure the delay settings of the time-delay units. Simulation results highlight the efficacy of the proposed solution in achieving robust performance across a wide frequency range for large antenna array systems.
PubDate: March 2023
Issue No: Vol. 12, No. 3 (2023)
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- Joint Pilot Spacing and Power Optimization Scheme for Nonstationary
Wireless Channel: A Deep Reinforcement Learning Approach-
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Authors: Xin Lin;Aijun Liu;Chen Han;Xiaohu Liang;Yangyang Li;
Pages: 540 - 544
Abstract: Pilot-assisted channel estimation techniques are essential for wireless communication systems. Most studies focus on the estimation algorithms and interpolation techniques. However, the design of pilot pattern is often neglected. In this letter, we propose a joint pilot spacing and power optimization scheme based on deep reinforcement learning (DRL) to address the mismatch problem of pilot configuration for nonstationary wireless channel. First, we model the adaptive pilot design decision-making process as a Markov Decision Process (MDP) to reduce pilot overhead and power loss. Then a deep Q-network (DQN) based learning algorithm is proposed to optimize the spacing and power of pilots so as to maximize estimation performance while reducing system cost. Simulation results show that the performance of the proposed approach is better than conventional pilot configuration algorithms. Moreover, we analyze the key factors that affect the performance of the proposed scheme.
PubDate: March 2023
Issue No: Vol. 12, No. 3 (2023)
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- A New Noncoherent Gaussian Signaling Scheme for Low Probability of
Detection Communications-
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Authors: Yuma Katsuki;Giuseppe Thadeu Freitas de Abreu;Koji Ishibashi;Naoki Ishikawa;
Pages: 545 - 549
Abstract: We propose a novel, Gaussian signaling mechanism for low probability of detection (LPD) communication systems with either single or multiple antennas. The new scheme is designed to allow the noncoherent detection of Gaussian-distributed signals, enabling LPD communications using signals that follow the complex Gaussian distribution in the time and frequency domains. It is demonstrated via simulations that the proposed scheme achieves better performance than a comparable conventional scheme over the entire SNR region, with the advantage becoming more significant in scenarios with lower overhead.
PubDate: March 2023
Issue No: Vol. 12, No. 3 (2023)
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- Coverage Analysis for Cellular-Connected Random 3D Mobile UAVs With
Directional Antennas-
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Authors: Hongguang Sun;Chao Ma;Linyi Zhang;Jiahui Li;Xijun Wang;Shuqin Li;Tony Q. S. Quek;
Pages: 550 - 554
Abstract: This letter proposes an analytical framework to evaluate the coverage performance of a cellular-connected unmanned aerial vehicle (UAV) network in which UAV user equipments (UAV-UEs) are equipped with directional antennas and move according to a three-dimensional (3D) mobility model. The ground base stations (GBSs) equipped with practical down-tilted antennas are distributed according to a Poisson point process (PPP). With tools from stochastic geometry, we derive the handover probability and coverage probability of a random UAV-UE under the strongest average received signal strength (RSS) association strategy. The proposed analytical framework allows to investigate the effect of UAV-UE antenna beamwidth, mobility speed, cell association, and vertical motions on both the handover probability and coverage probability. We conclude that the optimal UAV-UE antenna beamwidth decreases with the GBS density, and the omnidirectional antenna model is preferred in the sparse network scenario. What’s more, the superiority of the strongest average RSS association over the nearest association diminishes with the increment of GBS density.
PubDate: March 2023
Issue No: Vol. 12, No. 3 (2023)
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- Oversampling-Based Combining Under ISI Channels
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Authors: Jui Teng Wang;
Pages: 555 - 559
Abstract: We propose in this letter the oversampling based combining scheme under inter-symbol interference (ISI) channels, where the receiver samples the received signal at the rate that is higher than the symbol rate and performs combining over the channel length for each transmitted symbol. We show that by increasing the sampling rate, the achievable rate of the proposed scheme approaches the achievable rate of the ISI-free scheme in multi-path channels. Furthermore, we prove that if the sufficient condition is satisfied, then increasing the sampling rate makes the proposed scheme eliminate the effect of ISI so that the signal to interference-plus-noise ratio (SINR) for the output of the combiner approaches the SNR for AWGN channel (without fading and without ISI). This result leads to the facts that the error probability of the proposed scheme can approach that of AWGN channel and the achievable rate of the proposed scheme can approach the channel capacity of AWGN channel. The proposed scheme is a linear scheme and the achievable rate of the proposed scheme can be close to the channel capacity of AWGN channel with small increase in the computational complexity.
PubDate: March 2023
Issue No: Vol. 12, No. 3 (2023)
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- Beam Selection for Ambient Backscatter Communication in Beamspace mmWave
Symbiotic Radio-
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Authors: Muhammad Bilal Janjua;Hasan Tahir Abbas;Khalid A. Qaraqe;Hüseyin Arslan;
Pages: 560 - 564
Abstract: The Internet of Things revolution has profoundly impacted wireless communication systems. Access to high data rates is now just as important as low power operation. The use of incident millimeter-wave (mmWave) signals for ambient backscatter communication (AmBC) has shown significant promise for delivering high data rates. However, due to channel sparsity, incident signal availability to backscatter devices (BDs) at mmWave is erratic. In order to address the incident signal inaccessibility problem and enable high data-rate AmBC, this letter presents an efficient beam selection method in the beamspace millimeter-wave symbiotic radio system. The proposed method improves the overall system’s sum-rate performance by up to 30% while ensuring signal accessibility to BDs.
PubDate: March 2023
Issue No: Vol. 12, No. 3 (2023)
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- Overhead-Free Blockage Detection and Precoding Through Physics-Based Graph
Neural Networks: LIDAR Data Meets Ray Tracing-
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Authors: Matteo Nerini;Bruno Clerckx;
Pages: 565 - 569
Abstract: In this letter, we address blockage detection and precoder design for multiple-input multiple-output (MIMO) links, without communication overhead required. Blockage detection is achieved by classifying light detection and ranging (LIDAR) data through a physics-based graph neural network (GNN). For precoder design, a preliminary channel estimate is obtained by running ray tracing on a 3D surface obtained from LIDAR data. This estimate is successively refined and the precoder is designed accordingly. Numerical simulations show that blockage detection is successful with 95% accuracy. Our digital precoding achieves 90% of the capacity and analog precoding outperforms previous works exploiting LIDAR for precoder design.
PubDate: March 2023
Issue No: Vol. 12, No. 3 (2023)
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