Pub Date : 2026-01-28DOI: 10.1109/TCOMM.2026.3657448
Sheng Chen;Pengyu Wang;Mingkun Li;Emad F. Khalaf;Ali Morfeq;Naif D. Alotaibi
Multiple-input multiple-output (MIMO) technology in conjunction with orthogonal frequency division multiplexing (OFDM) transmission is widely adopted in fifth-generation mobile networks to support multiple users. However, in these mobile communication systems, high power amplifiers (HPAs) at user terminals’ transmitters are driven into their saturation regions, which makes the multiuser frequency-selective MIMO-OFDM uplink channel nonlinear and renders the standard multiuser detection (MUD) at the base station (BS) ineffective. In this paper machine learning is employed to combat the distortions in the uplink of this multiuser frequency-selective MIMO-OFDM communication system. More specifically, a powerful complex-valued B-spline neural network (BSNN) based design is developed to simultaneously identify the system’s channel impulse response (CIR) matrix and the BSNN model for the nonlinear transmitters’ HPA together with the BSNN inversion for the nonlinear HPA at transmitters. This enables the BS to effectively implement MUD by utilizing the estimated MIMO-OFDM CIR matrix as well as to compensate for the transmitter HPAs’ saturation distortions using the estimated BSNN inversion. A simulation study is included to evaluate the effectiveness of this novel BSNN assisted design in combating multiuser and dispersive channel interference as well as nonlinear distortions for multiuser MIMO-OFDM nonlinear uplink.
{"title":"B-Spline Neural Network-Based Multiuser MIMO-OFDM Nonlinear Uplink","authors":"Sheng Chen;Pengyu Wang;Mingkun Li;Emad F. Khalaf;Ali Morfeq;Naif D. Alotaibi","doi":"10.1109/TCOMM.2026.3657448","DOIUrl":"10.1109/TCOMM.2026.3657448","url":null,"abstract":"Multiple-input multiple-output (MIMO) technology in conjunction with orthogonal frequency division multiplexing (OFDM) transmission is widely adopted in fifth-generation mobile networks to support multiple users. However, in these mobile communication systems, high power amplifiers (HPAs) at user terminals’ transmitters are driven into their saturation regions, which makes the multiuser frequency-selective MIMO-OFDM uplink channel nonlinear and renders the standard multiuser detection (MUD) at the base station (BS) ineffective. In this paper machine learning is employed to combat the distortions in the uplink of this multiuser frequency-selective MIMO-OFDM communication system. More specifically, a powerful complex-valued B-spline neural network (BSNN) based design is developed to simultaneously identify the system’s channel impulse response (CIR) matrix and the BSNN model for the nonlinear transmitters’ HPA together with the BSNN inversion for the nonlinear HPA at transmitters. This enables the BS to effectively implement MUD by utilizing the estimated MIMO-OFDM CIR matrix as well as to compensate for the transmitter HPAs’ saturation distortions using the estimated BSNN inversion. A simulation study is included to evaluate the effectiveness of this novel BSNN assisted design in combating multiuser and dispersive channel interference as well as nonlinear distortions for multiuser MIMO-OFDM nonlinear uplink.","PeriodicalId":13041,"journal":{"name":"IEEE Transactions on Communications","volume":"74 ","pages":"4030-4045"},"PeriodicalIF":8.3,"publicationDate":"2026-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146070324","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-28DOI: 10.1109/TCOMM.2026.3658354
Zhang-Li-Han Liu;Qi-Yue Yu
This paper presents polarized element-pair (EP) codes for polarization-adjusted finite-field multiple-access (PA-FFMA) systems. The core innovation of FFMA systems lies in exchanging the order of channel coding and multiplexing operations, making the superposition signals a structured codeword sharing the entire degrees of freedom (DoFs). In the FFMA framework, element-pairs (EPs) serve as virtual resources for user separation, where different EP codes provide distinct error performance characteristics. This paper proposes polarized EP codes for a massive-user FFMA system, indicating that the superposition signals are codewords of polar codes. We derive the channel capacity for this polarized EP code-based PA-FFMA system, then develop an optimal power allocation scheme to maximize the multiuser channel capacity. The code construction employs the Monte Carlo method for selecting the polarized index set. For decoding, we introduce two specialized algorithms: a successive cancellation list (SCL) decoder for the balanced information-parity section scenarios, and a top $L$ bifurcated minimum distance (Top$L$ -BMD) decoder for small payload cases. Simulations show that FFMA with SCL achieves about 0.7 dB gain at $mathrm {BER}=10^{-5}$ with 7 users over Polar Spreading. As the user load increases, Polar Spreading is about 2 dB worse than uncoded BPSK at $mathrm {BER}=10^{-5}$ with 18 users, while FFMA with Top$L$ -BMD remains about 2.3 dB better than uncoded BPSK with 31 users. The proposed decoding algorithms also have lower complexity than the Polar Spreading system.
{"title":"Polarized Element-Pair Code-Based FFMA Over a Gaussian Multiple-Access Channel","authors":"Zhang-Li-Han Liu;Qi-Yue Yu","doi":"10.1109/TCOMM.2026.3658354","DOIUrl":"10.1109/TCOMM.2026.3658354","url":null,"abstract":"This paper presents polarized element-pair (EP) codes for polarization-adjusted finite-field multiple-access (PA-FFMA) systems. The core innovation of FFMA systems lies in exchanging the order of channel coding and multiplexing operations, making the superposition signals a structured codeword sharing the entire degrees of freedom (DoFs). In the FFMA framework, element-pairs (EPs) serve as virtual resources for user separation, where different EP codes provide distinct error performance characteristics. This paper proposes polarized EP codes for a massive-user FFMA system, indicating that the superposition signals are codewords of polar codes. We derive the channel capacity for this polarized EP code-based PA-FFMA system, then develop an optimal power allocation scheme to maximize the multiuser channel capacity. The code construction employs the Monte Carlo method for selecting the polarized index set. For decoding, we introduce two specialized algorithms: a successive cancellation list (SCL) decoder for the balanced information-parity section scenarios, and a top <inline-formula> <tex-math>$L$ </tex-math></inline-formula> bifurcated minimum distance (Top<inline-formula> <tex-math>$L$ </tex-math></inline-formula>-BMD) decoder for small payload cases. Simulations show that FFMA with SCL achieves about 0.7 dB gain at <inline-formula> <tex-math>$mathrm {BER}=10^{-5}$ </tex-math></inline-formula> with 7 users over Polar Spreading. As the user load increases, Polar Spreading is about 2 dB worse than uncoded BPSK at <inline-formula> <tex-math>$mathrm {BER}=10^{-5}$ </tex-math></inline-formula> with 18 users, while FFMA with Top<inline-formula> <tex-math>$L$ </tex-math></inline-formula>-BMD remains about 2.3 dB better than uncoded BPSK with 31 users. The proposed decoding algorithms also have lower complexity than the Polar Spreading system.","PeriodicalId":13041,"journal":{"name":"IEEE Transactions on Communications","volume":"74 ","pages":"4400-4414"},"PeriodicalIF":8.3,"publicationDate":"2026-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146070711","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-28DOI: 10.1109/tcomm.2026.3658382
Smriti Uniyal, Nhan Thanh Nguyen, Guddu Kumar, Marco Di Renzo, Markku Juntti
{"title":"Outage, Symbol Error Probability, and Rate of RIS-Assisted MIMO Systems with Phase Errors","authors":"Smriti Uniyal, Nhan Thanh Nguyen, Guddu Kumar, Marco Di Renzo, Markku Juntti","doi":"10.1109/tcomm.2026.3658382","DOIUrl":"https://doi.org/10.1109/tcomm.2026.3658382","url":null,"abstract":"","PeriodicalId":13041,"journal":{"name":"IEEE Transactions on Communications","volume":"75 1","pages":""},"PeriodicalIF":8.3,"publicationDate":"2026-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146070323","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-28DOI: 10.1109/TCOMM.2026.3658369
Jingyuan Liu;Zheng Chang;Ying-Chang Liang
Recently, over-the-air federated learning (OTA-FL) has attracted significant attention for its ability to enhance communication efficiency. However, the performance of OTA-FL is constrained by the complex interplay among straggler effects, data heterogeneity, and signal aggregation errors. To address these coupled challenges, we propose a joint device selection and transmit power optimization framework. First, we conduct a theoretical analysis to quantify the convergence upper bound of OTA-FL under partial device participation. Our analysis theoretically proves that both the selected device set and the signal aggregation errors significantly determine the convergence upper bound. Crucially, we identify a critical trade-off in system optimization: Simply prioritizing strong channels to minimize aggregation errors leads to model bias due to data heterogeneity; conversely, indiscriminately including stragglers forces the system to wait for the slowest device, significantly delaying model updates and reducing training efficiency. To resolve this dilemma, we introduce the age of information (AoI) as a regulation metric. Accordingly, we propose minimizing the expected weighted sum peak age of information (EWS-PAoI) to theoretically bound model deviation while maintaining training efficiency. To achieve this, we calculate device priorities for each communication round using Lyapunov optimization and select the highest-priority devices via a greedy algorithm. Subsequently, to minimize the aggregation error identified in our theoretical analysis, we formulate a transmit power and normalizing factor optimization problem. By leveraging Karush-Kuhn-Tucker (KKT) conditions, we derive a closed-form “clip-or-scale” solution for efficient power control. Experimental results on CIFAR-10 and CIFAR-100 datasets demonstrate that our method significantly outperforms various baselines by effectively reducing both the mean squared error (MSE) and training latency while maintaining high test accuracy. Overall, the proposed framework achieves a superior balance among training efficiency, model accuracy, and user fairness.
{"title":"Age-Based Device Selection and Transmit Power Optimization in Over-the-Air Federated Learning","authors":"Jingyuan Liu;Zheng Chang;Ying-Chang Liang","doi":"10.1109/TCOMM.2026.3658369","DOIUrl":"10.1109/TCOMM.2026.3658369","url":null,"abstract":"Recently, over-the-air federated learning (OTA-FL) has attracted significant attention for its ability to enhance communication efficiency. However, the performance of OTA-FL is constrained by the complex interplay among straggler effects, data heterogeneity, and signal aggregation errors. To address these coupled challenges, we propose a joint device selection and transmit power optimization framework. First, we conduct a theoretical analysis to quantify the convergence upper bound of OTA-FL under partial device participation. Our analysis theoretically proves that both the selected device set and the signal aggregation errors significantly determine the convergence upper bound. Crucially, we identify a critical trade-off in system optimization: Simply prioritizing strong channels to minimize aggregation errors leads to model bias due to data heterogeneity; conversely, indiscriminately including stragglers forces the system to wait for the slowest device, significantly delaying model updates and reducing training efficiency. To resolve this dilemma, we introduce the age of information (AoI) as a regulation metric. Accordingly, we propose minimizing the expected weighted sum peak age of information (EWS-PAoI) to theoretically bound model deviation while maintaining training efficiency. To achieve this, we calculate device priorities for each communication round using Lyapunov optimization and select the highest-priority devices via a greedy algorithm. Subsequently, to minimize the aggregation error identified in our theoretical analysis, we formulate a transmit power and normalizing factor optimization problem. By leveraging Karush-Kuhn-Tucker (KKT) conditions, we derive a closed-form “clip-or-scale” solution for efficient power control. Experimental results on CIFAR-10 and CIFAR-100 datasets demonstrate that our method significantly outperforms various baselines by effectively reducing both the mean squared error (MSE) and training latency while maintaining high test accuracy. Overall, the proposed framework achieves a superior balance among training efficiency, model accuracy, and user fairness.","PeriodicalId":13041,"journal":{"name":"IEEE Transactions on Communications","volume":"74 ","pages":"4320-4335"},"PeriodicalIF":8.3,"publicationDate":"2026-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146070300","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper considers the cell-free integrated sensing and communication (CF-ISAC) networks utilizing reconfigurable intelligent surface (RIS)-mounted uncrewed aerial vehicles (UAVs). We aim to maximize the sum of weighted sum rate within the whole ISAC period by jointly optimizing access points (APs)’ transmit beamformings, RISs’ phase shifts, user-RIS association, and UAVs’ locations. To deal with a highly complex non-convex optimization problem, we propose an alternating optimization solution by decomposing the original problem into three subproblems. In particular, for optimizing APs’ transmit beamformings, RISs’ phase shifts, and user-RIS association, we convert the log-sum problem into a quadratically constrained quadratic programming problem using the Lagrangian dual principle and multi-ratio fractional programming. For optimizing UAVs’ locations, the successive convex approximation technique is used to transform it into a convex problem. Simulation results highlight the considerable performance advantage of the proposed network compared to benchmark schemes employing fixed RISs, without RIS-mounted UAVs (URISs), and collocated network with URISs.
{"title":"Weighted Sum Rate Maximization for RIS-Mounted UAV-Aided Cell-Free ISAC Systems","authors":"Shanza Shakoor;Nguyen-Son Vo;Quang Nhat Le;Berk Canberk;Chao-Kai Wen;Hyundong Shin;Trung Q. Duong","doi":"10.1109/TCOMM.2026.3658388","DOIUrl":"10.1109/TCOMM.2026.3658388","url":null,"abstract":"This paper considers the cell-free integrated sensing and communication (CF-ISAC) networks utilizing reconfigurable intelligent surface (RIS)-mounted uncrewed aerial vehicles (UAVs). We aim to maximize the sum of weighted sum rate within the whole ISAC period by jointly optimizing access points (APs)’ transmit beamformings, RISs’ phase shifts, user-RIS association, and UAVs’ locations. To deal with a highly complex non-convex optimization problem, we propose an alternating optimization solution by decomposing the original problem into three subproblems. In particular, for optimizing APs’ transmit beamformings, RISs’ phase shifts, and user-RIS association, we convert the log-sum problem into a quadratically constrained quadratic programming problem using the Lagrangian dual principle and multi-ratio fractional programming. For optimizing UAVs’ locations, the successive convex approximation technique is used to transform it into a convex problem. Simulation results highlight the considerable performance advantage of the proposed network compared to benchmark schemes employing fixed RISs, without RIS-mounted UAVs (URISs), and collocated network with URISs.","PeriodicalId":13041,"journal":{"name":"IEEE Transactions on Communications","volume":"74 ","pages":"4278-4290"},"PeriodicalIF":8.3,"publicationDate":"2026-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146070305","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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":"Multi-Frequency Resonating Based Magnetic Induction Underground Emergency Communications with Diverse Mediums","authors":"Jianyu Wang, Zhichao Li, Wenchi Cheng, Wei Zhang, Hailin Zhang","doi":"10.1109/tcomm.2026.3657446","DOIUrl":"https://doi.org/10.1109/tcomm.2026.3657446","url":null,"abstract":"","PeriodicalId":13041,"journal":{"name":"IEEE Transactions on Communications","volume":"7 1","pages":""},"PeriodicalIF":8.3,"publicationDate":"2026-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146070710","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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":"Movable Antenna Assisted Flexible Beamforming for Integrated Sensing and Communication in Vehicular Networks","authors":"Luyang Sun, Zhiqing Wei, Haotian Liu, Kan Yu, Zhendong Li, Zhiyong Feng","doi":"10.1109/tcomm.2026.3657454","DOIUrl":"https://doi.org/10.1109/tcomm.2026.3657454","url":null,"abstract":"","PeriodicalId":13041,"journal":{"name":"IEEE Transactions on Communications","volume":"7 1","pages":""},"PeriodicalIF":8.3,"publicationDate":"2026-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146070716","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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