Pub Date : 2026-01-28DOI: 10.1109/TCOMM.2026.3657394
Tong Wu;Cheng-Xiang Wang;Junling Li;Xiaoyu Chen;Chen Huang;Mingchuan Yao;El-Hadi M. Aggoune
To address sparse channel measurement data and inadequate predictive capabilities in conventional channel models, predictive channel modeling employs joint generative and predictive architectures to enhance robustness. In this paper, we propose an enhanced diffusion-driven predictive framework that integrates generative augmentation and prior-aware prediction into a unified learning pipeline. We first introduce a space-time-frequency (STF) coupled diffusion network based on transformers that generates synthetic channel data preserving critical channel statistical properties. Additionally, we compress measured channel state information into a low-dimensional manifold via a latent encoder and introduce an innovative composite training scheme that couples diffusion-driven prior generation with prediction, equipping the predictive module with rich latent features that lift its performance ceiling and markedly improve generalization across diverse scenarios. Extensive experiments confirm the superiority of our algorithm, and its performance is further validated using channel measurement data, thereby demonstrating its robustness for advanced wireless communications in real-world deployment scenarios.
{"title":"High-Accuracy Predictive Channel Modeling for 6G Wireless Communications With an Improved Diffusion-Driven Learning Framework","authors":"Tong Wu;Cheng-Xiang Wang;Junling Li;Xiaoyu Chen;Chen Huang;Mingchuan Yao;El-Hadi M. Aggoune","doi":"10.1109/TCOMM.2026.3657394","DOIUrl":"10.1109/TCOMM.2026.3657394","url":null,"abstract":"To address sparse channel measurement data and inadequate predictive capabilities in conventional channel models, predictive channel modeling employs joint generative and predictive architectures to enhance robustness. In this paper, we propose an enhanced diffusion-driven predictive framework that integrates generative augmentation and prior-aware prediction into a unified learning pipeline. We first introduce a space-time-frequency (STF) coupled diffusion network based on transformers that generates synthetic channel data preserving critical channel statistical properties. Additionally, we compress measured channel state information into a low-dimensional manifold via a latent encoder and introduce an innovative composite training scheme that couples diffusion-driven prior generation with prediction, equipping the predictive module with rich latent features that lift its performance ceiling and markedly improve generalization across diverse scenarios. Extensive experiments confirm the superiority of our algorithm, and its performance is further validated using channel measurement data, thereby demonstrating its robustness for advanced wireless communications in real-world deployment scenarios.","PeriodicalId":13041,"journal":{"name":"IEEE Transactions on Communications","volume":"74 ","pages":"4014-4029"},"PeriodicalIF":8.3,"publicationDate":"2026-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146070307","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.2026.3658396
Arnav Mukhopadhyay;Keshav Singh;Fan-Shuo Tseng;Kapal Dev;Cunhua Pan
Integrating large-scale antenna arrays is essential for overcoming capacity limitations in wireless communications. In this work, we examine a novel sixth-generation (6G) secure simultaneous wireless information and power transfer (SWIPT) system, where a transmitter equipped with an extremely large-scale antenna array (ELAA) operates in the near-field region. In our design, the transmitter concurrently delivers confidential data to information receivers and energy to zero-energy devices (ZEDs) via non-orthogonal multiple access (NOMA). A key innovation of our approach is the specialized near-field beamfocusing technique derived from a three-dimensional spherical channel model, which explicitly accounts for the unique propagation characteristics of near-field communications and distinguishes our method from traditional far-field designs. We formulate a non-convex optimization problem aimed at maximizing the secrecy rate while satisfying minimum quality-of-service and energy harvesting requirements. To solve this problem, we develop an iterative algorithm based on weighted sum-rate maximization and sequential convex approximations that effectively mitigate interference and enhance beamfocusing performance. Numerical simulations demonstrate that, with a 64-element uniform linear array and 40 dBm transmit power, our near-field NOMA system achieves an $mathbf {18.41 %}$ higher secrecy rate than near-field spatial division multiple access (SDMA) and a $mathbf {36.78}$ -fold improvement over near-field orthogonal multiple access (OMA), along with a $mathbf {6.39}$ dBm increase in harvested power relative to SDMA. These results underscore the critical role of specialized near-field design in next-generation 6G networks and its significant implications for industrial internet-of-things (IoT) and Industry 4.0 applications.
{"title":"Secured Near-Field NOMA for ZED IoT Networks With SWIPT and Extremely Large-Scale Antennas","authors":"Arnav Mukhopadhyay;Keshav Singh;Fan-Shuo Tseng;Kapal Dev;Cunhua Pan","doi":"10.1109/TCOMM.2026.3658396","DOIUrl":"10.1109/TCOMM.2026.3658396","url":null,"abstract":"Integrating large-scale antenna arrays is essential for overcoming capacity limitations in wireless communications. In this work, we examine a novel sixth-generation (6G) secure simultaneous wireless information and power transfer (SWIPT) system, where a transmitter equipped with an extremely large-scale antenna array (ELAA) operates in the near-field region. In our design, the transmitter concurrently delivers confidential data to information receivers and energy to zero-energy devices (ZEDs) via non-orthogonal multiple access (NOMA). A key innovation of our approach is the specialized near-field beamfocusing technique derived from a three-dimensional spherical channel model, which explicitly accounts for the unique propagation characteristics of near-field communications and distinguishes our method from traditional far-field designs. We formulate a non-convex optimization problem aimed at maximizing the secrecy rate while satisfying minimum quality-of-service and energy harvesting requirements. To solve this problem, we develop an iterative algorithm based on weighted sum-rate maximization and sequential convex approximations that effectively mitigate interference and enhance beamfocusing performance. Numerical simulations demonstrate that, with a 64-element uniform linear array and 40 dBm transmit power, our near-field NOMA system achieves an <inline-formula> <tex-math>$mathbf {18.41 %}$ </tex-math></inline-formula> higher secrecy rate than near-field spatial division multiple access (SDMA) and a <inline-formula> <tex-math>$mathbf {36.78}$ </tex-math></inline-formula>-fold improvement over near-field orthogonal multiple access (OMA), along with a <inline-formula> <tex-math>$mathbf {6.39}$ </tex-math></inline-formula> dBm increase in harvested power relative to SDMA. These results underscore the critical role of specialized near-field design in next-generation 6G networks and its significant implications for industrial internet-of-things (IoT) and Industry 4.0 applications.","PeriodicalId":13041,"journal":{"name":"IEEE Transactions on Communications","volume":"74 ","pages":"3919-3936"},"PeriodicalIF":8.3,"publicationDate":"2026-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146070318","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.2026.3658381
Pham Q. Viet, Daniel Romero
{"title":"Path Planning for Aerial Relays via Probabilistic Roadmaps","authors":"Pham Q. Viet, Daniel Romero","doi":"10.1109/tcomm.2026.3658381","DOIUrl":"https://doi.org/10.1109/tcomm.2026.3658381","url":null,"abstract":"","PeriodicalId":13041,"journal":{"name":"IEEE Transactions on Communications","volume":"42 1","pages":""},"PeriodicalIF":8.3,"publicationDate":"2026-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146070319","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.2026.3657445
Sidrah Javed, Yunfei Chen
{"title":"Airborne and Ground-based NIBs for On-Demand Coverage with User Disparity and SWIPT","authors":"Sidrah Javed, Yunfei Chen","doi":"10.1109/tcomm.2026.3657445","DOIUrl":"https://doi.org/10.1109/tcomm.2026.3657445","url":null,"abstract":"","PeriodicalId":13041,"journal":{"name":"IEEE Transactions on Communications","volume":"53 1","pages":""},"PeriodicalIF":8.3,"publicationDate":"2026-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146070313","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
{"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":"https://doi.org/10.1109/tcomm.2026.3658354","url":null,"abstract":"","PeriodicalId":13041,"journal":{"name":"IEEE Transactions on Communications","volume":"29 1","pages":""},"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.2026.3658615
Anna Valeria Guglielmi;Mattia Scarin Callegaro;Yaser Dorrazehi;Stefano Tomasin
We consider beyond-diagonal reconfigurable intelligent surfaces (BD-RISs) whose elements are connected in groups and aim at optimizing their configuration to maximize the achievable rate of the cascade channel. We propose two suboptimal approaches (i.e., semidefinite programming (SDP) and projected gradient ascent (PGA) solutions) to first find the BD-RIS configuration that maximizes the composite channel trace and then locally maximizes the achievable rate by a randomization approach. We impose a constraint on the choice of the coefficients to ensure that the BD-RIS is passive, i.e., it does not emit more power than that received. Still, our solution has a high communication overhead for a large number of connections among the BD-RIS elements. We then propose a dynamic mapping between the BD-RIS configuration and a small number of control variables. The mapping is provided by the encoder part of an autoencoder, trained to minimize a suitable loss function on the optimal configurations in the specific deployment. We also design the BD-RIS configuration directly in the latent space of the autoencoder, reducing the complexity. By simulations in a typical cellular communication scenario, we show that the group-connected BD-RIS can achieve up to 95% of the rate obtained for a fully-connected BD-RIS with two orders of magnitude lower complexity, while the autoencoder compression and configuration optimization in the latent space reduces the control rate by 90% with negligible rate loss.
{"title":"Optimization of Passive Beyond-Diagonal RIS via Relaxation, Randomization, and Autoencoding","authors":"Anna Valeria Guglielmi;Mattia Scarin Callegaro;Yaser Dorrazehi;Stefano Tomasin","doi":"10.1109/TCOMM.2026.3658615","DOIUrl":"10.1109/TCOMM.2026.3658615","url":null,"abstract":"We consider beyond-diagonal reconfigurable intelligent surfaces (BD-RISs) whose elements are connected in groups and aim at optimizing their configuration to maximize the achievable rate of the cascade channel. We propose two suboptimal approaches (i.e., semidefinite programming (SDP) and projected gradient ascent (PGA) solutions) to first find the BD-RIS configuration that maximizes the composite channel trace and then locally maximizes the achievable rate by a randomization approach. We impose a constraint on the choice of the coefficients to ensure that the BD-RIS is passive, i.e., it does not emit more power than that received. Still, our solution has a high communication overhead for a large number of connections among the BD-RIS elements. We then propose a dynamic mapping between the BD-RIS configuration and a small number of control variables. The mapping is provided by the encoder part of an autoencoder, trained to minimize a suitable loss function on the optimal configurations in the specific deployment. We also design the BD-RIS configuration directly in the latent space of the autoencoder, reducing the complexity. By simulations in a typical cellular communication scenario, we show that the group-connected BD-RIS can achieve up to 95% of the rate obtained for a fully-connected BD-RIS with two orders of magnitude lower complexity, while the autoencoder compression and configuration optimization in the latent space reduces the control rate by 90% with negligible rate loss.","PeriodicalId":13041,"journal":{"name":"IEEE Transactions on Communications","volume":"74 ","pages":"4172-4186"},"PeriodicalIF":8.3,"publicationDate":"2026-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11367070","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146070316","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-28DOI: 10.1109/TCOMM.2026.3658391
Jiaying Wang;Xiaoping Jin;Pei Han;Miaowen Wen;Yao Ge;Chongwen Huang;Yudong Yao
Spectral efficiency (SE) and energy efficiency (EE) are two major challenges faced by the sixth-generation wireless communication systems. In this paper, we propose a reconfigurable intelligent surfaces-assisted simultaneous wireless information and power transfer scheme based on fluid antenna port grouping index modulation (RIS-FA-PGIM). The flexible port switching capability of FA overcomes the spatial limitations of traditional antennas, significantly improving the SE. Furthermore, in order to improve the SE and EE of the system, this paper jointly optimizes the beamforming matrix at the base station and RIS. Due to the coupling relationship between variables, the optimization problem is non-convex and difficult to solve. In order to solve this problem, an alternating optimization algorithm is proposed, which gradually approaches the global optimal solution through an iterative optimization process. Simulation results show that the system not only achieves outstanding SE performance but also realizes low energy consumption, which verifies the effectiveness and superiority of the scheme.
{"title":"Beamforming Design for Fluid Antenna Port Grouping Index Modulation With RIS-Assisted SWIPT Systems","authors":"Jiaying Wang;Xiaoping Jin;Pei Han;Miaowen Wen;Yao Ge;Chongwen Huang;Yudong Yao","doi":"10.1109/TCOMM.2026.3658391","DOIUrl":"10.1109/TCOMM.2026.3658391","url":null,"abstract":"Spectral efficiency (SE) and energy efficiency (EE) are two major challenges faced by the sixth-generation wireless communication systems. In this paper, we propose a reconfigurable intelligent surfaces-assisted simultaneous wireless information and power transfer scheme based on fluid antenna port grouping index modulation (RIS-FA-PGIM). The flexible port switching capability of FA overcomes the spatial limitations of traditional antennas, significantly improving the SE. Furthermore, in order to improve the SE and EE of the system, this paper jointly optimizes the beamforming matrix at the base station and RIS. Due to the coupling relationship between variables, the optimization problem is non-convex and difficult to solve. In order to solve this problem, an alternating optimization algorithm is proposed, which gradually approaches the global optimal solution through an iterative optimization process. Simulation results show that the system not only achieves outstanding SE performance but also realizes low energy consumption, which verifies the effectiveness and superiority of the scheme.","PeriodicalId":13041,"journal":{"name":"IEEE Transactions on Communications","volume":"74 ","pages":"3904-3918"},"PeriodicalIF":8.3,"publicationDate":"2026-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146070321","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.2026.3657448
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}
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