{"title":"Performance Analysis of Cascaded RIS-Assisted Two-Way Wireless Communication System","authors":"Huihan Liu;Pu Miao;Kang Song","doi":"10.1109/LCOMM.2025.3573621","DOIUrl":"https://doi.org/10.1109/LCOMM.2025.3573621","url":null,"abstract":"In this letter, a cascaded reconfigurable intelligent surfaces (RIS)-assisted two-way communication system is proposed, and its performance is explored under <inline-formula> <tex-math>$kappa -mu $ </tex-math></inline-formula> fading channels, supporting simultaneous information exchange between the transmitter and receiver. To more accurately reflect practical communication scenarios, the impacts of transceiver hardware impairments (HWI), phase errors, path loss, and loop interference (LI) are taken into account. Approximate closed-form expressions for the outage probability (OP) and ergodic capacity (EC) are derived, and the accuracy of the theoretical formulas is validated through Monte Carlo simulations. The results demonstrate that the effects of HWI, phase errors, path loss, and LI degrade the communication performance of the system. In addition, the EC of the proposed system is improved by approximately twice the one-way system, highlighting the significant advantage of two-way communication in enhancing spectral efficiency.","PeriodicalId":13197,"journal":{"name":"IEEE Communications Letters","volume":"29 7","pages":"1744-1748"},"PeriodicalIF":3.7,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144606337","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Houfeng Chen;Shaohua Yue;Marco Di Renzo;Hongliang Zhang
{"title":"Degrees of Freedom of Holographic MIMO in Multi-User Near-Field Channels","authors":"Houfeng Chen;Shaohua Yue;Marco Di Renzo;Hongliang Zhang","doi":"10.1109/LCOMM.2025.3555796","DOIUrl":"https://doi.org/10.1109/LCOMM.2025.3555796","url":null,"abstract":"Holographic multiple-input multiple-output (HMIMO) is an emerging technology for 6G communications, in which numerous antenna units are integrated in a limited space. As the HMIMO array aperture expands, the near-field region of the array is dramatically enlarged, resulting in more users being located in the near-field region. This creates new opportunities for wireless communications. In this context, the evaluation of the spatial degrees of freedom (DoF) of HMIMO multi-user systems in near-field channels is an open problem, as the analytical methods utilized for evaluating the DoF in far-field channels cannot be directly applied. In this letter, we propose a novel method to calculate the DoF of HMIMO in multi-user near-field channels. We first derive the DoF for a single user in the near field, and then extend the analysis to multi-user scenarios. In this latter scenario, we focus on the impact of spatial blocking between HMIMO users. The derived analytical framework reveals that the DoF of HMIMO in multi-user near-field channels is not in general given by the sum of the DoF of the HMIMO single-user setting because of spatial blocking. Simulation results demonstrate the effectiveness of the proposed method. In the considered case study, the number of DoF reduces by 21.2% on average due to spatial blocking.","PeriodicalId":13197,"journal":{"name":"IEEE Communications Letters","volume":"29 6","pages":"1186-1190"},"PeriodicalIF":3.7,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144272795","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Yijian Hou;Kaisa Zhang;Gang Chuai;Weidong Gao;Xiangyu Chen;Siqi Liu
{"title":"Service Association and Subcarrier Allocation in Vehicular Slicing Networks With Dynamic Multi-Service","authors":"Yijian Hou;Kaisa Zhang;Gang Chuai;Weidong Gao;Xiangyu Chen;Siqi Liu","doi":"10.1109/LCOMM.2025.3555768","DOIUrl":"https://doi.org/10.1109/LCOMM.2025.3555768","url":null,"abstract":"The vehicular slicing network with dynamic multi-service (VSN-DMS) enables vehicles to support multiple services with dynamically changing types. In this architecture, vehicles can access multiple base stations (BSs) and leverage different network slices (NSs) to meet the requirements of various services. However, frequent service variations and vehicle mobility introduce high handover (HO) costs and inefficient decision making. This letter proposes a novel two-timescale optimization framework to jointly optimize vehicle service (VS) association and subcarrier (SC) allocation. Specifically, in the long-timescale, we propose a federated deep reinforcement learning (FDRL) algorithm to optimize VS association. Unlike conventional reinforcement learning approaches that require centralized data collection, FDRL enables distributed model training across vehicles while aggregating parameters in the cloud. This mitigates the issue of insufficient training data caused by frequent VS changes, while also ensuring vehicle data privacy. In the short-timescale, we propose a versatile alternating coalitional game and swap-matching game algorithm (ACGSM) to optimize SC allocation. By iteratively executing coalitional formation and swap-matching mechanisms, ACGSM efficiently improves the quality of the solution while adapting to dynamic network conditions. Simulation results demonstrate the convergence of FDRL and show that the proposal outperforms benchmarks in improving long-term QoS and reducing the number of HOs.","PeriodicalId":13197,"journal":{"name":"IEEE Communications Letters","volume":"29 5","pages":"1131-1135"},"PeriodicalIF":3.7,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143932261","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Xin Wang;Can Zheng;Pengjiang Hu;Junan Yang;Chung G. Kang
{"title":"A Sparsity-Agnostic SL0 Channel Estimation Approach for OTFS Systems","authors":"Xin Wang;Can Zheng;Pengjiang Hu;Junan Yang;Chung G. Kang","doi":"10.1109/LCOMM.2025.3554379","DOIUrl":"https://doi.org/10.1109/LCOMM.2025.3554379","url":null,"abstract":"Orthogonal time frequency space (OTFS) modulation has been shown to support reliable communication in high mobility scenarios. Due to the sparsity of the delay-Doppler (DD) domain, most existing algorithms utilize compressed sensing (CS) for OTFS channel estimation, while requiring a known channel sparsity. However, in cases that the sparsity is not readily accessible, the estimation accuracy of these CS algorithms decreases dramatically. In this letter, we propose a Smoothed <inline-formula> <tex-math>$ell _{0}$ </tex-math></inline-formula> (SL0) algorithm free of prior knowledge about channel sparsity for OTFS channel estimation. We obtain a novel vector expression of the input-output relationship in the DD domain and formulate it as a sparse signal recovery problem. In the case of unknown sparsity, a low-complexity SL0 algorithm is introduced to solve the problem with a faster reconstruction speed. Simulation results show that the proposed algorithm has significant advantages in estimation accuracy and complexity compared to algorithms that also do not require channel sparsity.","PeriodicalId":13197,"journal":{"name":"IEEE Communications Letters","volume":"29 5","pages":"1097-1101"},"PeriodicalIF":3.7,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143937964","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Task-Oriented Single- and Multi-Source Semantic Source Coding","authors":"Licheng Chen;Yan Li;Yunquan Dong","doi":"10.1109/LCOMM.2025.3554652","DOIUrl":"https://doi.org/10.1109/LCOMM.2025.3554652","url":null,"abstract":"Semantic communication aims at conveying semantic features rather than transmitting lossless data. The development of semantic communication is greatly hindered due to the lack of a general mathematical model for semantics, i.e., the absence of a measure of task-oriented source information. To address this problem, we propose a task-oriented concept for source information validity and source compression algorithms. Specifically, we consider semantic communication models in single- and multi-source systems and use the semantic context as well as correlation among sources to reduce redundant transmissions of information. We also study the relationship of code rates between Shannon, distributed, and semantic source coding, detailing the properties of the task and source under equal rates. Simulation results using public datasets demonstrate the validity and feasibility of the proposed semantic source coding algorithm across multiple communication tasks.","PeriodicalId":13197,"journal":{"name":"IEEE Communications Letters","volume":"29 5","pages":"1107-1111"},"PeriodicalIF":3.7,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143938011","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Jisong Xu;Chaowei Wang;Danhao Deng;Yehao Li;Mingliang Pang;Zhi Zhang;Dongming Wang
{"title":"Joint AP Scheduling and Power Allocation Based on Synergistic DRL for Cell-Free Massive MIMO","authors":"Jisong Xu;Chaowei Wang;Danhao Deng;Yehao Li;Mingliang Pang;Zhi Zhang;Dongming Wang","doi":"10.1109/LCOMM.2025.3554179","DOIUrl":"https://doi.org/10.1109/LCOMM.2025.3554179","url":null,"abstract":"The challenges of energy consumption posed by 5G have emerged as a critical bottleneck for next generation mobile communications. In response to the “Dual Carbon” initiative, we focus on enhancing downlink energy efficiency (EE) in cell-free massive MIMO systems. Unlike most existing studies, which overlook the dynamic fluctuations in users’ downlink rate demands, we aim to optimize the overall downlink energy efficiency while maintaining a constrained satisfaction ratio for users’ spectral efficiency (SE) requirements. In this letter, we propose a synergistic Deep Reinforcement Learning (DRL) cell-free framework, which utilizes the Advantage Actor-Critic (A2C) to jointly and dynamically adjust the idle/active states of access points (APs) and allocate the transmitting power. Simulation results demonstrate that the synergistic A2C-based scheme with idle/active scheduling can effectively improve the energy efficiency of cell-free massive MIMO system, while ensuring the satisfaction of spectral efficiency requirements.","PeriodicalId":13197,"journal":{"name":"IEEE Communications Letters","volume":"29 5","pages":"1082-1086"},"PeriodicalIF":3.7,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143938028","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Collision Resolution in RFID Systems Using Antenna Arrays and Mix Source Separation","authors":"Mohamed Siala;Noura Sellami","doi":"10.1109/LCOMM.2025.3554980","DOIUrl":"https://doi.org/10.1109/LCOMM.2025.3554980","url":null,"abstract":"In this letter, we propose an efficient mix source separation (MSS) algorithm for collision resolution in radio frequency identification (RFID) systems equipped with an antenna array at the reader. We first introduce an approach that exploits the zero constant modulus (ZCM) criterion to separate colliding tags through gradient descent, without using pilot symbols. We show that the ZCM characteristic, considered alone, in the design of the objective function can lead to significant ambiguities in the determination of the beamformers used in the recovery of tag messages. To address this limitation, we propose a more sophisticated approach, relying on a hybrid objective function, incorporating a new ambiguity-raising criterion in addition to the ZCM criterion.","PeriodicalId":13197,"journal":{"name":"IEEE Communications Letters","volume":"29 5","pages":"1122-1125"},"PeriodicalIF":3.7,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143932235","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Shun Han;Wen Fang;Mengyuan Xu;Mingliang Xiong;Hao Deng;Qingwen Liu
{"title":"Multi-Receiver Transmission Analysis for Mobile SLIPT Using Resonant Beam","authors":"Shun Han;Wen Fang;Mengyuan Xu;Mingliang Xiong;Hao Deng;Qingwen Liu","doi":"10.1109/LCOMM.2025.3554770","DOIUrl":"https://doi.org/10.1109/LCOMM.2025.3554770","url":null,"abstract":"Simultaneous lightwave information and power transfer (SLIPT) technology offers a promising solution to address the communication and power demands of Internet of Things (IoT) devices. The resonant beam (RB) system, based on the self-aligning properties of a spatially separated laser resonator (SSLR), enables energy transmission without precise alignment. However, existing RB-based SLIPT systems are limited to single-receiver configurations. This work introduces a multi-receiver RB-SLIPT system capable of simultaneously transmitting energy and information to multiple receivers. An analytical model based on multi-mode rate equations and diffraction transmission theory is developed to evaluate the energy transfer and communication performance in multi-receiver scenarios. Numerical simulation results demonstrate that in a dual-receiver scenario, the proposed system is capable of transmitting 2 W of optical power to each receiver while achieving a communication capacity of 5.8 bit/s/Hz.","PeriodicalId":13197,"journal":{"name":"IEEE Communications Letters","volume":"29 5","pages":"1112-1116"},"PeriodicalIF":3.7,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143932260","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Cooperative Sensing Based on Coverage Prediction in Heterogeneous Spectrum Availability","authors":"Zhibo Chen;Wenming Zhu;Jingming Hu;Daoxing Guo","doi":"10.1109/LCOMM.2025.3555146","DOIUrl":"https://doi.org/10.1109/LCOMM.2025.3555146","url":null,"abstract":"This letter proposes a novel cooperative spectrum sensing (CSS) scheme based on primary users (PUs) coverage prediction for cognitive radio networks in heterogeneous spectrum availability (HetSA) environments. To achieve accurate sensing, a conditional generative adversarial network (cGAN) is employed to learn and construct the radio map, based on limited sensing data from secondary users (SUs). Subsequently, an improved density peak clustering (DPC) algorithm is used to identify the density distribution of PU signal strength and accurately predict PU coverage areas, exhibiting robustness against channel fading and noise. Finally, a radio map coverage prediction based CSS (RMCP-CSS) detector is proposed, which efficiently fuses predicted PU coverage for enhanced spectrum sensing. Simulation results demonstrate that the proposed RMCP-CSS achieves superior detection performance compared to state-of-the-art CSS algorithms, making it well-suited for small-scale, dense sensor deployments in HetSA scenarios.","PeriodicalId":13197,"journal":{"name":"IEEE Communications Letters","volume":"29 6","pages":"1171-1175"},"PeriodicalIF":3.7,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144272777","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Md Zobaer Islam;Ethan Abele;Fahim Ferdous Hossain;Arsalan Ahmad;Sabit Ekin;John F. O’Hara
{"title":"Free-Space Optical Channel Turbulence Prediction: A Machine Learning Approach","authors":"Md Zobaer Islam;Ethan Abele;Fahim Ferdous Hossain;Arsalan Ahmad;Sabit Ekin;John F. O’Hara","doi":"10.1109/LCOMM.2025.3555162","DOIUrl":"https://doi.org/10.1109/LCOMM.2025.3555162","url":null,"abstract":"Channel turbulence is a formidable obstacle for free-space optical (FSO) communication. Anticipation of turbulence levels is highly important for mitigating disruptions but has not been demonstrated without dedicated, auxiliary hardware. We show that machine learning (ML) can be applied to raw FSO data streams to rapidly predict channel turbulence levels with no additional sensing hardware. FSO was conducted through a controlled channel in the lab under six distinct turbulence levels, and the efficacy of using ML to classify turbulence levels was examined. ML-based turbulence level classification was found to be >98% accurate with multiple ML training parameters. Classification effectiveness was found to depend on the timescale of changes between turbulence levels but converges when turbulence stabilizes over about a one minute timescale.","PeriodicalId":13197,"journal":{"name":"IEEE Communications Letters","volume":"29 5","pages":"1126-1130"},"PeriodicalIF":3.7,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143932234","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}