{"title":"Joint Active and Passive Beamforming for Multi-UE Communication and Extended Target Detection in IRS-Assisted ISAC Systems","authors":"Hanfu Zhang, Erwu Liu, Shizhuang Zhang, Shuqiang Xia, Wei Ni, Rui Wang, Zhe Xing, Dusit Niyato, Abbas Jamalipour","doi":"10.1109/twc.2026.3659648","DOIUrl":"https://doi.org/10.1109/twc.2026.3659648","url":null,"abstract":"","PeriodicalId":13431,"journal":{"name":"IEEE Transactions on Wireless Communications","volume":"91 1","pages":""},"PeriodicalIF":10.4,"publicationDate":"2026-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146134223","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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 : 2026-02-04DOI: 10.1109/twc.2026.3658821
K.K. Ashna, Jobin Francis
{"title":"IRS-assisted Communication in Correlated Rayleigh Fading Channels: Diversity and SEP Analyses","authors":"K.K. Ashna, Jobin Francis","doi":"10.1109/twc.2026.3658821","DOIUrl":"https://doi.org/10.1109/twc.2026.3658821","url":null,"abstract":"","PeriodicalId":13431,"journal":{"name":"IEEE Transactions on Wireless Communications","volume":"28 1","pages":""},"PeriodicalIF":10.4,"publicationDate":"2026-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146115850","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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 : 2026-02-04DOI: 10.1109/twc.2026.3658566
Sota Uchimura, Josep Miquel Jornet, Koji Ishibashi
{"title":"Optimal Wavefronts for Maximum Ratio Transmissions Under Path Blockage Effects","authors":"Sota Uchimura, Josep Miquel Jornet, Koji Ishibashi","doi":"10.1109/twc.2026.3658566","DOIUrl":"https://doi.org/10.1109/twc.2026.3658566","url":null,"abstract":"","PeriodicalId":13431,"journal":{"name":"IEEE Transactions on Wireless Communications","volume":"15 1","pages":""},"PeriodicalIF":10.4,"publicationDate":"2026-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146115847","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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 : 2026-02-04DOI: 10.1109/twc.2026.3658942
Michele Mirabella, Pasquale Di Viesti, Christos Masouros, Giorgio M. Vitetta
{"title":"Joint Range and Doppler Estimation Using Spectrally Efficient FDM","authors":"Michele Mirabella, Pasquale Di Viesti, Christos Masouros, Giorgio M. Vitetta","doi":"10.1109/twc.2026.3658942","DOIUrl":"https://doi.org/10.1109/twc.2026.3658942","url":null,"abstract":"","PeriodicalId":13431,"journal":{"name":"IEEE Transactions on Wireless Communications","volume":"57 1","pages":""},"PeriodicalIF":10.4,"publicationDate":"2026-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146115852","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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 : 2026-02-04DOI: 10.1109/TWC.2026.3658710
Qifei Wang;Zhen Gao;Shuo Sun;Zhijin Qin;Xiaodong Xu;Meixia Tao
As a promising 6G enabler beyond conventional bit-level transmission, semantic communication can considerably reduce required bandwidth resources, while its combination with multiple access requires further exploration. This paper proposes a knowledge distillation-driven and diffusion-enhanced (KDD) semantic non-orthogonal multiple access (NOMA), named KDD-SemNOMA, for multi-user uplink wireless image transmission. Specifically, to ensure robust feature transmission across diverse transmission conditions, we firstly develop a ConvNeXt-based deep joint source and channel coding architecture with enhanced adaptive feature module. This module incorporates signal-to-noise ratio and channel state information to dynamically adapt to additive white Gaussian noise and Rayleigh fading channels. Furthermore, to improve image restoration quality without inference overhead, we introduce a two-stage knowledge distillation strategy, i.e., a teacher model, trained on interference-free orthogonal transmission, guides a student model via feature affinity distillation and cross-head prediction distillation. Moreover, a diffusion model-based refinement stage leverages generative priors to transform initial SemNOMA outputs into high-fidelity images with enhanced perceptual quality. Extensive experiments on CIFAR-10 and FFHQ-256 datasets demonstrate superior performance over state-of-the-art methods, delivering satisfactory reconstruction performance even at extremely poor channel conditions. These results highlight the advantages in both pixel-level accuracy and perceptual metrics, effectively mitigating interference and enabling high-quality image recovery.
{"title":"Knowledge Distillation-Driven Semantic NOMA for Image Transmission With Diffusion Model","authors":"Qifei Wang;Zhen Gao;Shuo Sun;Zhijin Qin;Xiaodong Xu;Meixia Tao","doi":"10.1109/TWC.2026.3658710","DOIUrl":"10.1109/TWC.2026.3658710","url":null,"abstract":"As a promising 6G enabler beyond conventional bit-level transmission, semantic communication can considerably reduce required bandwidth resources, while its combination with multiple access requires further exploration. This paper proposes a knowledge distillation-driven and diffusion-enhanced (KDD) semantic non-orthogonal multiple access (NOMA), named KDD-SemNOMA, for multi-user uplink wireless image transmission. Specifically, to ensure robust feature transmission across diverse transmission conditions, we firstly develop a ConvNeXt-based deep joint source and channel coding architecture with enhanced adaptive feature module. This module incorporates signal-to-noise ratio and channel state information to dynamically adapt to additive white Gaussian noise and Rayleigh fading channels. Furthermore, to improve image restoration quality without inference overhead, we introduce a two-stage knowledge distillation strategy, i.e., a teacher model, trained on interference-free orthogonal transmission, guides a student model via feature affinity distillation and cross-head prediction distillation. Moreover, a diffusion model-based refinement stage leverages generative priors to transform initial SemNOMA outputs into high-fidelity images with enhanced perceptual quality. Extensive experiments on CIFAR-10 and FFHQ-256 datasets demonstrate superior performance over state-of-the-art methods, delivering satisfactory reconstruction performance even at extremely poor channel conditions. These results highlight the advantages in both pixel-level accuracy and perceptual metrics, effectively mitigating interference and enabling high-quality image recovery.","PeriodicalId":13431,"journal":{"name":"IEEE Transactions on Wireless Communications","volume":"25 ","pages":"11783-11798"},"PeriodicalIF":10.7,"publicationDate":"2026-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146115549","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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 : 2026-02-04DOI: 10.1109/TWC.2026.3658630
Xiaoyang He;Xiaoxia Huang;Manabu Tsukada
Timely channel conditions are essential for vehicles to determine which base station (BS) to connect to, but acquiring them in mmWave vehicular networks is costly. Without additional channel estimations, the proposed asynchronous contextual kernelized upper confidence bound (ACK-UCB) algorithm estimates the current instantaneous transmission rates based on the historical transmission rates and contexts, such as the vehicle’s historical locations, velocities, and numbers of concurrent transmissions at the BS. ACK-UCB captures the nonlinear relationship between context and transmission rate, mapping the context into a reproducing kernel Hilbert space (RKHS), where a linear relationship becomes observable. To enhance estimation accuracy, a novel kernel function incorporating mmWave signal propagation characteristics is introduced in RKHS, allowing for a more precise evaluation of context similarity in relation to transmission rates. Furthermore, ACK-UCB encourages vehicles to share only reward distribution features after sufficient explorations, accelerating the learning process while keeping communication costs manageable. Numerical results show that ACK-UCB achieves 99.5%–100.5% network throughput and reduces 89%–91% communication cost of a benchmark algorithm that directly shares all local historical contexts and transmission rates, demonstrating the sharing efficiency of the ACK-UCB algorithm.
{"title":"ACK-UCB: An Asynchronous Contextual Kernel-Based Bandit Approach for User Association in mmWave Vehicular Networks","authors":"Xiaoyang He;Xiaoxia Huang;Manabu Tsukada","doi":"10.1109/TWC.2026.3658630","DOIUrl":"10.1109/TWC.2026.3658630","url":null,"abstract":"Timely channel conditions are essential for vehicles to determine which base station (BS) to connect to, but acquiring them in mmWave vehicular networks is costly. Without additional channel estimations, the proposed asynchronous contextual kernelized upper confidence bound (ACK-UCB) algorithm estimates the current instantaneous transmission rates based on the historical transmission rates and contexts, such as the vehicle’s historical locations, velocities, and numbers of concurrent transmissions at the BS. ACK-UCB captures the nonlinear relationship between context and transmission rate, mapping the context into a reproducing kernel Hilbert space (RKHS), where a linear relationship becomes observable. To enhance estimation accuracy, a novel kernel function incorporating mmWave signal propagation characteristics is introduced in RKHS, allowing for a more precise evaluation of context similarity in relation to transmission rates. Furthermore, ACK-UCB encourages vehicles to share only reward distribution features after sufficient explorations, accelerating the learning process while keeping communication costs manageable. Numerical results show that ACK-UCB achieves 99.5%–100.5% network throughput and reduces 89%–91% communication cost of a benchmark algorithm that directly shares all local historical contexts and transmission rates, demonstrating the sharing efficiency of the ACK-UCB algorithm.","PeriodicalId":13431,"journal":{"name":"IEEE Transactions on Wireless Communications","volume":"25 ","pages":"11738-11751"},"PeriodicalIF":10.7,"publicationDate":"2026-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146115849","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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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