IEEE journal on selected areas in communications : a publication of the IEEE Communications Society最新文献

{"title":"From Large AI Models to Agentic AI: A Tutorial on Future Intelligent Communications","authors":"Feibo Jiang;Cunhua Pan;Kezhi Wang;Pietro Michiardi;Octavia A. Dobre;Merouane Debbah","doi":"10.1109/JSAC.2026.3660010","DOIUrl":"10.1109/JSAC.2026.3660010","url":null,"abstract":"With the advent of 6G communications, intelligent communication systems face multiple challenges, including constrained perception and response capabilities, limited scalability, and low adaptability in dynamic environments. To address these challenges, this tutorial provides a systematic and comprehensive introduction to the principles, design, and applications of Large Artificial Intelligence Models (LAMs) and Agentic AI technologies in intelligent communication systems, aiming to offer researchers an integrated overview of cutting-edge methodologies and practical insights. First, the tutorial outlines the background of 6G communications and reviews the technological evolution from LAMs to Agentic AI. It then systematically examines the key components required for constructing LAMs, classifies various types of LAMs, and analyzes their applicability in communication. A LAM-centric design paradigm tailored for communication systems is subsequently proposed, encompassing dataset construction, internal learning, and external learning approaches. Building upon this foundation, the tutorial develops an LAM-based Agentic AI system for intelligent communications, elaborating on its core components—including agents, world models, planners, knowledge bases, tools, and memory modules—as well as their interaction mechanisms. Finally, it provides an in-depth review of representative applications of LAMs and Agentic AI in communication scenarios, and summarizes the current research challenges and future directions, with the goal of fostering the development of efficient, secure, and sustainable next-generation intelligent communication systems.","PeriodicalId":73294,"journal":{"name":"IEEE journal on selected areas in communications : a publication of the IEEE Communications Society","volume":"44 ","pages":"3507-3540"},"PeriodicalIF":17.2,"publicationDate":"2026-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146101480","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Learn-to-Share: A Decentralized Multi-Agent Spectrum Sharing Framework for Heterogeneous Networks in the 6G Era","authors":"Yang Tao;Jian-Cheng He;Zi-Jian Liu;Shaoshi Yang","doi":"10.1109/JSAC.2026.3659811","DOIUrl":"10.1109/JSAC.2026.3659811","url":null,"abstract":"Future wireless networks will be artificial intelligence (AI) native and highly heterogeneous, where 5G/6G, next generation Wi-Fi, low-altitude drone networks, and satellite networks, have to coexist in the crowded gigahertz and tens of gigahertz frequency bands. This introduces grave challenges in radio resource sharing due to fundamental technical specification discrepancies and complex time-varying interference patterns. As a remedy, we propose a learn-to-share (L2S) framework—a distributed multi-agent paradigm for spectrum sharing among multiple radio access technologies (RATs) that will coexist in the 6G era. In this framework, user terminals (UTs) that are mutually interfering with each other, regardless of which RAT they use, can operate as intelligent agents capable of autonomously making decisions on spectrum access through local sensing and learning, without centralized coordination. We formulate this spectrum sharing problem as a decentralized partially observable Markov decision process (Dec-POMDP) and design a spectral-temporal attention and recurrence (STAR) algorithm as the cognitive engine. STAR decomposes individual agent decisions into three tightly coupled subtasks: channel selection via Transformer networks that capture spatial interference correlations, threshold adaptation through bidirectional long short-term memory (LSTM) networks that model temporal channel dynamics, and waiting time scheduling using predictive LSTM planners that intelligently avoid collisions. Through extensive OMNeT++-based simulations, we demonstrate that STAR significantly outperforms traditional approaches: achieving 73.5% improvement in access success rate, 30.6% enhancement in fairness (Jain’s index), and 88.1% increase in system throughput compared with the conventional listen-before-talk (LBT) mechanism, while requiring 50.6% fewer convergence episodes than the deep recurrent Q-network (DRQN) baseline. Moreover, compared with the mixing Q-network (QMIX), a representative centralized training with decentralized execution (CTDE) baseline, our algorithm reduces the computing power needed per UT by 21.9% while achieving comparable access success rate and system throughput as a fully decentralized multi-agent framework.","PeriodicalId":73294,"journal":{"name":"IEEE journal on selected areas in communications : a publication of the IEEE Communications Society","volume":"44 ","pages":"3490-3506"},"PeriodicalIF":17.2,"publicationDate":"2026-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146089958","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"IEEE Communications Society Information","authors":"","doi":"10.1109/JSAC.2025.3636429","DOIUrl":"https://doi.org/10.1109/JSAC.2025.3636429","url":null,"abstract":"","PeriodicalId":73294,"journal":{"name":"IEEE journal on selected areas in communications : a publication of the IEEE Communications Society","volume":"43 12","pages":"C3-C3"},"PeriodicalIF":17.2,"publicationDate":"2026-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11366032","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146049300","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Distributionally Robust Wireless Semantic Communication With Large AI Models","authors":"Long Tan Le;Senura Hansaja Wanasekara;Zerun Niu;Nguyen H. Tran;Phuong Luu. Vo;Walid Saad;Dusit Niyato;Zhu Han;Choong Seon Hong;H. Vincent Poor","doi":"10.1109/JSAC.2026.3656885","DOIUrl":"10.1109/JSAC.2026.3656885","url":null,"abstract":"Semantic communication (SemCom) has emerged as a promising paradigm for 6G wireless systems by transmitting task-relevant information rather than raw bits, yet existing approaches remain vulnerable to dual sources of uncertainty: semantic misinterpretation arising from imperfect feature extraction and transmission-level perturbations from channel noise. Current deep learning based SemCom systems typically employ domain-specific architectures that lack robustness guarantees and fail to generalize across diverse noise conditions, adversarial attacks, and out-of-distribution data. In this paper, a novel and generalized semantic communication framework called <inline-formula> <tex-math>$textsf {WaSeCom}$ </tex-math></inline-formula> is proposed to systematically address uncertainty and enhance robustness. In particular, Wasserstein distributionally robust optimization is employed to provide resilience against semantic misinterpretation and channel perturbations. A rigorous theoretical analysis is performed to establish the robust generalization guarantees of the proposed framework. Experimental results on image and text transmission demonstrate that <inline-formula> <tex-math>$textsf {WaSeCom}$ </tex-math></inline-formula> achieves improved robustness under noise and adversarial perturbations. These results highlight its effectiveness in preserving semantic fidelity across varying wireless conditions.","PeriodicalId":73294,"journal":{"name":"IEEE journal on selected areas in communications : a publication of the IEEE Communications Society","volume":"44 ","pages":"3473-3489"},"PeriodicalIF":17.2,"publicationDate":"2026-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146043167","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Large Multimodal Model-Based Environment-Aware Beam Management","authors":"Seungnyun Kim;Subham Saha;Seokhyun Jeong;Byonghyo Shim;Moe Z. Win","doi":"10.1109/JSAC.2025.3611419","DOIUrl":"10.1109/JSAC.2025.3611419","url":null,"abstract":"Beam management is an essential operation of next-generation (xG) wireless networks to compensate severe signal attenuation and ensure reliable communications over millimeter wave (mmWave) and terahertz (THz) bands. The primary objective of beam management is to determine beam directions that are properly aligned with the signal propagation paths. Conventional beam management techniques typically rely on geometric channel parameters such as angles, delays, and path gains. However, due to their lack of contextual awareness of the surrounding environment, these techniques fall short in handling the piecewise continuous changes in the geometric channel parameters caused by sudden obstructions or variations in scatterers. In this paper, we propose a novel beam management framework that utilizes environmental information extracted from sensor data (e.g., images) and pilot measurements to optimize beam directions. The main idea of the proposed scheme is to utilize visual channel parameters, including the positions of user equipment (UE), reflection points, and scatterers, which provide a direct visualization of the propagation environment. By tracking the visual channel parameters, dynamic changes in scatterers and propagation paths can be monitored over time. To analyze multimodal data and track these parameters, we exploit large multimodal model (LMM), a generative artificial intelligence (AI) model specialized in extracting correlated features across multimodal data and generating subsequent data. Simulation results show that the proposed scheme can accurately track the visual channel parameters and enhance data rate.","PeriodicalId":73294,"journal":{"name":"IEEE journal on selected areas in communications : a publication of the IEEE Communications Society","volume":"44 ","pages":"991-1007"},"PeriodicalIF":17.2,"publicationDate":"2026-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146043166","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integrated Sensing and Communication (ISAC) Channel Model Toward 3GPP 6G Standardization: Modeling, Validation, and Application","authors":"Wei Hong;Zhenyu Zhang;Wei Xi;Yunfeng Liu;Yingyang Li;Qiheng Huang;Qun Zhao;Juejia Zhou;Jianhua Zhang;Yong Li","doi":"10.1109/JSAC.2026.3656257","DOIUrl":"10.1109/JSAC.2026.3656257","url":null,"abstract":"Integrated sensing and communication (ISAC) systems have been envisaged as a pivotal technology and prominent usage scenario for the sixth generation (6G) wireless communication systems, delivering dual-functional hardware infrastructure and superior spectral efficiency. To facilitate performance evaluation for ISAC commercialization, the third generation partnership project (3GPP) has initiated the standardization of ISAC deployment scenarios and ISAC channel modeling in the fifth generation (5G) new radio (NR) Release-19 (Rel-19). In this paper, three fundamental contributions are proposed to advance ISAC channel modeling towards 3GPP 6G standardization under the framework of the 5G counterpart. First, we establish a generalized radar cross section (RCS) modeling framework that precisely characterizes target scattering properties for ISAC channel power parameterization. Second, an elaborated unified spatial consistency modeling is developed to maintain geometric coherence under mobility of sensing nodes, thereby enabling persistent tracking capability through enhanced spatial coherence. Third, as an application of ISAC channel modeling, we investigate a sensing transceiver pair selection algorithm that maximizes reference signal received power via full coupling loss analysis derived from channel characteristics. Numerical results illustrate detailed RCS profiling of the Xiaomi SU7 Max vehicle validated through ray-tracing simulations for both monostatic and bistatic sensing modes. The proposed spatial correlation modeling for residual fluctuation RCS component effectively mitigates artificial Doppler spectrum broadening induced by geometric discontinuities. Furthermore, the proposed sensing transceiver pair selection algorithm based on full coupling loss exhibits significant performance gains over benchmark method.","PeriodicalId":73294,"journal":{"name":"IEEE journal on selected areas in communications : a publication of the IEEE Communications Society","volume":"44 ","pages":"3459-3472"},"PeriodicalIF":17.2,"publicationDate":"2026-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146042798","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Information-Theoretic Decentralized Secure Aggregation With Passive Collusion Resilience","authors":"Xiang Zhang;Zhou Li;Shuangyang Li;Kai Wan;Derrick Wing Kwan Ng;Giuseppe Caire","doi":"10.1109/JSAC.2026.3678509","DOIUrl":"10.1109/JSAC.2026.3678509","url":null,"abstract":"In decentralized federated learning (FL), multiple clients collaboratively learn a shared machine learning (ML) model by leveraging their privately held datasets distributed across the network, through interactive exchange of intermediate model updates. To ensure data security, cryptographic techniques are commonly employed to protect model updates during aggregation. Despite growing interest in secure aggregation, existing works predominantly focus on protocol design and computational guarantees, with limited understanding of the fundamental information-theoretic limits of such systems. Moreover, optimal bounds on communication and key usage remain unknown in decentralized settings, where no central aggregator is available. Motivated by these gaps, we study the problem of decentralized secure aggregation (DSA) from an information-theoretic perspective. Specifically, we consider a network of <inline-formula> <tex-math>$K$ </tex-math></inline-formula> fully-connected users, each holding a private input—an abstraction of local training data—who aim to securely compute the sum of all inputs. The security constraint requires that no user learns anything beyond the input sum, even when colluding with up to <inline-formula> <tex-math>$T$ </tex-math></inline-formula> other users. We characterize the optimal rate region, which specifies the minimum achievable communication and secret key rates for DSA. In particular, we show that to securely compute one symbol of the desired input sum, each user must: 1) transmit at least one symbol to others; 2) hold at least one symbol of secret key; and 3) all users must collectively hold no fewer than <inline-formula> <tex-math>$K - 1$ </tex-math></inline-formula> independent key symbols. Our results establish the fundamental performance limits of DSA, providing insights for the design of provably secure and communication-efficient protocols in decentralized learning.","PeriodicalId":73294,"journal":{"name":"IEEE journal on selected areas in communications : a publication of the IEEE Communications Society","volume":"44 ","pages":"4414-4428"},"PeriodicalIF":17.2,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147524172","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Fine-Grained 3D Radio Map Construction Paradigm With Ultra-Low Sampling Rates by Large Generative Models","authors":"Zhiyuan Liu;Qingyu Liu;Shuhang Zhang;Hongliang Zhang;Lingyang Song","doi":"10.1109/JSAC.2026.3677149","DOIUrl":"10.1109/JSAC.2026.3677149","url":null,"abstract":"A radio map captures the spatial distribution of wireless channel parameters, such as the strength of the signal received, across a geographic area. The problem of fine-grained three-dimensional (3D) radio map construction involves inferring a high-resolution radio map for the two-dimensional (2D) area at an arbitrary target height within a 3D region of interest, using radio samples collected by sensors sparsely distributed in that 3D region. Solutions to the problem are crucial for efficient spectrum management in 3D spaces, particularly for drones in the rapidly developing low-altitude economy. However, this problem is challenging due to ultra-sparse sampling, where the number of collected radio samples is far fewer than the desired resolution of the radio map to be estimated. In this paper, we design RadioLAM, a fine-grained 3D radio map construction paradigm built on generative Large Artificial Intelligence Models (LAMs). RadioLAM employs the creative power and the strong generalization capability of LAM to address the ultra-sparse sampling challenge. It consists of three key blocks: 1) an augmentation block, using the radio propagation model to project the radio samples collected at different heights to the 2D area at the target height; 2) a generation block, leveraging a diffusion-based LAM under an Mixture of Experts (MoE) architecture to generate a candidate set of fine-grained radio maps for the target 2D area; and 3) an election block, utilizing the radio propagation model as a guide to find the best map from the candidate set. Extensive simulations show that RadioLAM is able to solve the fine-grained 3D radio map construction problem efficiently from an ultra-low sampling rate of 0.1%, and significantly outperforms state-of-the-art (SOTA). Furthermore, real-world experiments confirm that RadioLAM achieves superior performance compared to SOTA.","PeriodicalId":73294,"journal":{"name":"IEEE journal on selected areas in communications : a publication of the IEEE Communications Society","volume":"44 ","pages":"4397-4413"},"PeriodicalIF":17.2,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147506598","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Design and Implementation of Photovoltaic Array for Integration of Visible Light Communication and Wireless Power Transfer Systems","authors":"Huilin Ni;Jizhou Zhuang;Lipeng Qiu;Xiaofeng Li;Dong Ma;Shaolong Wu","doi":"10.1109/JSAC.2026.3699495","DOIUrl":"https://doi.org/10.1109/JSAC.2026.3699495","url":null,"abstract":"The integration of visible light communication (VLC) and wireless power transfer (WPT) offers a sustainable platform for powering Internet of Things (IoT) devices while enabling simultaneous data transmission. However, conventional receivers based on a single photovoltaic (PV) cell are constrained by the inherent trade-off between bandwidth and output power. To overcome this limitation, this paper proposes a PV array-based solution and designs its circuit topology, achieving both high output power and extended bandwidth. An equivalent circuit model of the PV array is established to facilitate theoretical analysis, and the transfer function is derived to evaluate system performance. Furthermore, the influences of different array configurations and peripheral circuits are investigated, leading to the adoption of an optimized bridged-T-coil structure. Numerical simulations and comparative experiments on 4-cell array configuration demonstrate that the proposed bridged-T-coil topology achieves a <inline-formula> <tex-math>$3.13times $ </tex-math></inline-formula> bandwidth enhancement and nearly quadrupled output power compared to a single PV cell. Furthermore, when evaluated against the average bandwidth-power product (BW <inline-formula> <tex-math>$times P_{max }$ </tex-math></inline-formula>) of the five other internally developed array architectures for a fair comparison, the bridged-T-coil array yields a value of 93.81, exceeding their mean performance by approximately 20.7% on average. To the best of our knowledge, this work presents the first systematic design and investigation into the circuit topology and performance of a PV array receiver for VLC-WPT systems, and provides a practical receiver solution for low-power IoT nodes requiring integrated energy and communication capabilities.","PeriodicalId":73294,"journal":{"name":"IEEE journal on selected areas in communications : a publication of the IEEE Communications Society","volume":"44 ","pages":"4772-4786"},"PeriodicalIF":17.2,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148236152","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Social Utility Maximization via Entanglement Connection Provisioning in Quantum Networks","authors":"Xiaoyu Wang;Yangming Zhao;Hongli Xu;Chen Tian;Kun Yang;Chunming Qiao","doi":"10.1109/JSAC.2026.3695437","DOIUrl":"https://doi.org/10.1109/JSAC.2026.3695437","url":null,"abstract":"From the perspective of user experience, when optimizing resource provisioning in networks, we have to maximize social utility, which is an abstraction of what users can obtain from the service provided by a network. In quantum networks, unlike their counterparts, circuit-switched classical networks, (i) the utility obtained by a demand is not always concave for the number of Entanglement Connections (ECs) we provision to it; and (ii) each demand requires a different amount of quantum resources over each link along the path to establish an EC. As a result, the Social Utility Maximization (SUM) problem is more challenging than in classic circuit-switched networks. In this paper, we propose an approach also called SUM to maximize social utility in quantum networks by provisioning an appropriate number of ECs (and corresponding resources) to demands. We first formulate the SUM problem and analyze it based on Lagrangian relaxation and duality techniques. Accordingly, we derive the optimal EC provisioning scheme for a given Lagrangian multiplier, depending on whether the utility function of each demand is convex, concave, or sigmoid-like. After that, a primal-dual iteration algorithm is proposed to determine the optimal EC provisioning scheme to maximize social utility. We conduct extensive simulations to demonstrate that SUM outperforms the state-of-the-art approach to maximizing quantum network throughput, i.e., EFiRAP, by up to 58.4%.","PeriodicalId":73294,"journal":{"name":"IEEE journal on selected areas in communications : a publication of the IEEE Communications Society","volume":"44 ","pages":"4718-4732"},"PeriodicalIF":17.2,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148171701","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}