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

{"title":"A Simultaneous Decoding Approach to Joint State and Message Communications","authors":"Xinyang Li;Yiqi Chen;Vlad C. Andrei;Aladin Djuhera;Ullrich J. Mönich;Holger Boche","doi":"10.1109/JSAC.2025.3608138","DOIUrl":"10.1109/JSAC.2025.3608138","url":null,"abstract":"The capacity-distortion (C-D) trade-offs for joint state and message communications (JSMC) over single- and multi-user channels are investigated, where the transmitters have access to generalized state information and feedback while the receivers jointly decode the messages and estimate the channel state. A coding scheme is proposed based on backward simultaneous decoding of messages and compressed state descriptions without the need for the Wyner-Ziv random binning technique. For the point-to-point channel, the proposed scheme results in the optimal C-D function. For the state-dependent discrete memoryless degraded broadcast channel (SD-DMDBC), the successive refinement method is adopted for designing multi-stage state descriptions. With the simultaneous decoding approach, the derived achievable region is shown to be larger than the region obtained by the sequential decoding approach that is utilized in existing works. As for the state-dependent discrete memoryless multiple access channel (SD-DMMAC), in addition to the proposed method, Willem’s coding strategy is applied to enable partial collaboration between transmitters through the feedback links. Moreover, the state descriptions are shown to enhance both communication and state estimation performance. Examples are provided for the derived results to verify the analysis, either numerically or analytically. With particular focus, simple but representative integrated sensing and communications (ISAC) systems are also considered, and their fundamental performance limits are studied.","PeriodicalId":73294,"journal":{"name":"IEEE journal on selected areas in communications : a publication of the IEEE Communications Society","volume":"44 ","pages":"107-120"},"PeriodicalIF":17.2,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11155173","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145035820","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":"Integrated Sensing and Communications for Sparse Data: A Quality-Driven Privacy-Preserving Truth Discovery System","authors":"Jing Bai;Houbing Song;Anfeng Liu;Neal N. Xiong;Jie Wu","doi":"10.1109/JSAC.2025.3608133","DOIUrl":"10.1109/JSAC.2025.3608133","url":null,"abstract":"Integrated Sensing and Communications (ISAC) systems face challenges in balancing sparse human resource allocation with high-quality data acquisition, especially in scenarios involving highly mobile workers and uneven task coverage. While Sparse Mobile CrowdSensing (SMCS) offers a resource-efficient solution by inferring unobserved data from partial sensing tasks, two critical issues remain unaddressed: 1) existing data inference methods often assume collected data is trustworthy, neglecting the detrimental impact of malicious workers on data quality, and 2) the lack of privacy-preserving mechanisms in sparse ISAC may deter worker participation, compromising service quality. To address these, we propose a Quality-driven Privacy-preserving Truth Discovery scheme (QPTD), which optimizes sparse sensing and data quality with privacy constraints in ISAC systems. QPTD integrates a trust-aware Deep Reinforcement Learning (DRL) algorithm for dynamic cell selection, prioritizing cells with high-trust workers and minimizing recruitment costs, while a matrix encryption protocol ensures end-to-end privacy protection for both workers and tasks. By leveraging Matrix Factorization (MF) on encrypted sparse data aggregated from high-trust workers, QPTD infers unobserved tasks and safeguards sensitive information. Theoretical analysis proves the robustness of our privacy guarantees against adversarial attacks. Evaluations on real-world datasets demonstrate that QPTD improves ISAC system performance by increasing total data quality by 1.73%–142.07%, reducing estimation errors by 24.82%–75.32%, and cutting recruitment costs by up to 62.03% while ensuring service performance.","PeriodicalId":73294,"journal":{"name":"IEEE journal on selected areas in communications : a publication of the IEEE Communications Society","volume":"44 ","pages":"849-865"},"PeriodicalIF":17.2,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145035823","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":"Cooperative ISAC for Joint Localization and Velocity Estimation in Cell-Free MIMO Systems","authors":"Zihuan Wang;Vincent W.S. Wong;Robert Schober","doi":"10.1109/JSAC.2025.3613283","DOIUrl":"10.1109/JSAC.2025.3613283","url":null,"abstract":"In this paper, we explore a cooperative integrated sensing and communication (ISAC) framework that utilizes orthogonal frequency division multiplexing (OFDM) waveforms. Under the control of a central processing unit (CPU), multiple access points (APs) collaboratively perform multistatic sensing while providing communication service in a cell-free multiple-input multiple-output (MIMO) system. Achieving high sensing accuracy requires the collection of global sensing information at the CPU, which can lead to significant fronthaul signaling overhead due to the feedback of the sensing signals from each AP. To tackle this issue, we propose a collaborative processing scheme in which the APs locally compress and quantize the received sensing signals before forwarding them to the CPU. The CPU then aggregates the information from all APs to estimate the location and velocity of the targets. We develop a distributed vector-quantized variational autoencoder (D-VQVAE) to enable an end-to-end implementation of this scheme. D-VQVAE consists of distributed encoders at the APs to locally encode the received sensing signals, codebooks for quantizing the encoded results, and a decoder at the CPU for location and velocity estimation. It effectively reduces the amount of data transmitted from each AP to the CPU while maintaining a high sensing accuracy. We employ a collaborative learning-assisted scheme to train D-VQVAE in an end-to-end manner. Simulation results show that the proposed D-VQVAE network outperforms the baseline schemes in sensing accuracy and reduces fronthaul signaling overhead by 99% when compared with the centralized sensing approach.","PeriodicalId":73294,"journal":{"name":"IEEE journal on selected areas in communications : a publication of the IEEE Communications Society","volume":"44 ","pages":"642-658"},"PeriodicalIF":17.2,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145127602","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":"Performance of RIS-Aided Fluid Antenna-Enabled Multiuser NOMA Non-Terrestrial Networks","authors":"Soumen Mondal;Keshav Singh;Aryan Kaushik;Chih-Peng Li","doi":"10.1109/JSAC.2025.3615570","DOIUrl":"10.1109/JSAC.2025.3615570","url":null,"abstract":"This paper analyzes the outage probability and ergodic capacity of a reconfigurable intelligent surface (RIS)-assisted fluid antenna system (FAS)-enabled non-orthogonal multiple access (NOMA)-based satellite communication system, considering practical impairments such as imperfect successive interference cancellation (SIC) and channel state information (CSI). We model the satellite links with shadowed-rician distribution and the terrestrial links with Nakagami-m distribution while employing maximum ratio transmission (MRT) at the low Earth orbit (LEO) satellite. By leveraging the central limit theorem (CLT) and the moment method, the cascaded RIS-assisted channel is approximated as a Gamma distribution. Additionally, the clayton copula effectively models the correlation between FAS ports, enabling the derivation of approximate outage probability and ergodic capacity expressions for arbitrary fading distributions. The Gauss-Laguerre quadrature approximation is employed to efficiently solve complex integrals in the ergodic capacity analysis. Furthermore, asymptotic outage probability expressions in the high SNR regime are derived, revealing the diversity order and the impact of various system parameters on outage probability. Numerical results highlight the detrimental effects of these imperfections, underscoring their significance in system design. Performance improvements are demonstrated with an increasing number of RIS elements, transmit antennas, and FAS ports in RIS-assisted NOMA satellite communications. Furthermore, the impact of power allocation on outage probability is analyzed, and a trade-off between the number of FAS ports and the width of the fluid channel is established to achieve optimal performance.","PeriodicalId":73294,"journal":{"name":"IEEE journal on selected areas in communications : a publication of the IEEE Communications Society","volume":"44 ","pages":"1061-1076"},"PeriodicalIF":17.2,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145188388","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":"Intent-Driven Cognitive XR Networks: Multi-Agent Orchestration for Immersive Communication","authors":"Yakun Huang;Yaru Zhao;Zhenguo Chen;Hao Zhang;Jing Lv;Xiuquan Qiao","doi":"10.1109/JSAC.2025.3649618","DOIUrl":"10.1109/JSAC.2025.3649618","url":null,"abstract":"Extended Reality (XR) is emerging as a central use case in the 6G era, requiring intelligent, adaptive, and low-latency communication to support immersive user experiences. However, existing network architectures remain reactive and lack the capability to interpret or act upon users’ fine-grained multimodal intents, limiting their responsiveness and efficiency. To address this gap, this paper presents the Cognitive XR Network (CXN) framework, an intent-driven architecture that integrates perception, reasoning, and control through a hierarchy of Artificial Intelligence (AI) agents. CXN comprises three cooperating agents: the User Intent Agent, which infers and predicts user intents from multimodal sensory inputs; the Network Orchestration Agent, which performs global coordination through multi-agent reinforcement learning; and distributed Resource Management Agents which execute localized decisions under strategic guidance. Together, these agents form a closed cognitive loop that enables proactive, intent-aware orchestration across radio, compute, and caching domains. Simulations under realistic XR collaboration scenarios demonstrate that CXN sustains an intent satisfaction rate above 85% and an average interaction latency below 60 ms with 90 concurrent users, outperforming both reactive and centralized learning baselines.","PeriodicalId":73294,"journal":{"name":"IEEE journal on selected areas in communications : a publication of the IEEE Communications Society","volume":"44 ","pages":"3137-3151"},"PeriodicalIF":17.2,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145894711","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 Quantum-Optimized Training Framework for Radio Frequency Fingerprint Identification","authors":"To Truong An;Guolin Yin;Junqing Zhang;Yuan Ding;Trung Q. Duong;Simon L. Cotton","doi":"10.1109/JSAC.2026.3706263","DOIUrl":"10.1109/JSAC.2026.3706263","url":null,"abstract":"Radio frequency fingerprint identification (RFFI) offers a promising physical-layer method to authenticate devices based on unique hardware impairments. However, existing RFFI systems use deep learning (DL) models that are resource-intensive. Training is particularly demanding, requiring repeated updates to a large number of parameters. In this paper, we introduce a quantum-assisted training (QAST) framework to address training inefficiencies in RFFI. QAST integrates a quantum neural network (QNN) with a mapping network to generate parameters for a classical DL model. This indirect training strategy substantially reduces the number of trainable parameters and the overall memory requirements compared to direct training of the DL model. We achieve this by introducing a multimodal mapping network that effectively learns the QNN output. This network generates multiple classical parameters from a shared quantum representation, thereby reducing qubit requirements and lowering the risk of barren-plateau-related trainability degradation. We also propose a new embedding method that reduces the size of the embedding matrix and yields a 15% to 30% reduction in training time. The tailored QAST framework trains the RFFI model with only 10% of the original number of parameters while maintaining comparable accuracy, thereby substantially reducing memory and computational overhead and enabling efficient training or retraining in resource-limited environments.","PeriodicalId":73294,"journal":{"name":"IEEE journal on selected areas in communications : a publication of the IEEE Communications Society","volume":"44 ","pages":"5327-5340"},"PeriodicalIF":17.2,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148314206","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":"On Mixing of Quantum Key Distribution and Post-Quantum Cryptographic Keys: Min-Entropy Bounds, Provisioning Policies, and Network-Oriented Trade-Offs","authors":"Miralem Mehic;Stefan Rass;Sergej Jakovlev;Marcin Niemiec;Peppino Fazio;Miroslav Voznak","doi":"10.1109/JSAC.2026.3722598","DOIUrl":"10.1109/JSAC.2026.3722598","url":null,"abstract":"Quantum key distribution and post-quantum cryptography represent the backbone for tackling security challenges in the quantum era. Although each of them has its own shortcomings, their practical implementation raises the question of their possible combination and synergy. In this context, the paper considers mixing established cryptographic keys at key management layer, using both approaches to optimize the available key resources in a quantum network, also balancing the provided security. We provide analytical model, security analysis, and optimal contribution of keys in the mixed cryptographic key. Extensive simulation results demonstrate the validity of this approach in point-to-point and network environments.","PeriodicalId":73294,"journal":{"name":"IEEE journal on selected areas in communications : a publication of the IEEE Communications Society","volume":"44 ","pages":"5669-5679"},"PeriodicalIF":17.2,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11651613","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148728220","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":"Holographic Beamforming for Integrated Sensing and Communication With Mutual Coupling Effects","authors":"Shuhao Zeng;Haobo Zhang;Boya Di;Hongliang Zhang;Zijian Shao;Zhu Han;H. Vincent Poor;Lingyang Song","doi":"10.1109/JSAC.2025.3612921","DOIUrl":"10.1109/JSAC.2025.3612921","url":null,"abstract":"Integrated sensing and communication (ISAC) is envisioned as a key technology in 6G networks, owing to its potential for high spectral and cost efficiency. As a promising solution for extremely large-scale arrays, reconfigurable holographic surfaces (RHSs) can be integrated with ISAC to form the holographic ISAC paradigm, where enlarged radiation apertures of RHSs can achieve significant beamforming gains, thereby improving both communication and sensing performance. In this paper, we investigate holographic beamforming designs for ISAC systems. However, unlike existing holographic beamforming schemes developed for RHS-aided communications, such designs require explicit consideration of mutual coupling effects within the RHS. This is because, unlike prior works only considering communication performance, ISAC systems incorporate sensing functionality, which is sensitive to sidelobe levels. Ignoring mutual coupling in holographic beamforming can lead to notable undesired sidelobes, thus degrading sensing performance. The consideration of mutual coupling introduces new challenges, i.e., it induces non-linearity in beamforming problems, rendering them inherently non-convex. To address this issue, we propose a tractable electromagnetic-compliant holographic ISAC model that characterizes mutual coupling in a closed form using coupled dipole approximations. We then develop an efficient holographic beamforming algorithm to suppress sidelobes and enhance ISAC performance. Numerical results validate the effectiveness of the proposed algorithm.","PeriodicalId":73294,"journal":{"name":"IEEE journal on selected areas in communications : a publication of the IEEE Communications Society","volume":"44 ","pages":"480-497"},"PeriodicalIF":17.2,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145116991","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":"Covert IRS-UAV Networks Empowered by Deep Reinforcement Learning","authors":"Esraa M. Ghourab;Omar Alhussein;De Mi;Qiang Ye;Sami Muhaidat","doi":"10.1109/JSAC.2025.3642218","DOIUrl":"10.1109/JSAC.2025.3642218","url":null,"abstract":"Covert wireless communication ensures both information confidentiality and transmission untraceability, which is increasingly vital for mission-critical extended reality (XR) services. While uncrewed aerial vehicles (UAVs) provide mobility and flexible coverage, and intelligent reflecting surfaces (IRSs) enable energy-efficient signal manipulation, their joint use for covert communications has not yet been sufficiently explored. This paper proposes a novel UAV-mounted IRS system for covert communications that passively reflects source signals toward a legitimate receiver while minimizing detection by an adversary warden. In contrast to previous work that treats trajectory design, beamforming, and power control in isolation, the proposed work develops a unified framework based on double deep Q-networks (DDQN) to jointly optimize the UAV trajectory, power allocation, and IRS phase shifts under covert constraints. We analytically derive the optimal detection threshold and the minimum detection error probability, which are dynamically integrated into the learning framework. The optimization problem is formulated as a constrained Markov decision process, which allows the agent to adaptively learn optimal policies in dynamic environments without relying on perfect channel knowledge. Simulation results demonstrate that the proposed framework significantly improves covert rate and energy efficiency compared with the iterative and random benchmark schemes, while also providing insights into the impact of system parameters on performance.","PeriodicalId":73294,"journal":{"name":"IEEE journal on selected areas in communications : a publication of the IEEE Communications Society","volume":"44 ","pages":"1813-1826"},"PeriodicalIF":17.2,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145717736","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":"LLM in V2I: A Data-Driven Predictive Beamforming Framework for Vehicle Tracking in Near-Field ISAC Systems","authors":"Hongjia Huang;Weijie Yuan;Chang Liu;Liang Liu;Fan Liu;Wei Xiang;Derrick Wing Kwan Ng","doi":"10.1109/JSAC.2025.3643394","DOIUrl":"10.1109/JSAC.2025.3643394","url":null,"abstract":"In this paper, we investigate the problem of predictive beamforming design for tracking vehicles in an integrated sensing and communication (ISAC)-based near-field vehicle-to-infrastructure (V2I) system. The waveform design in near-field scenarios requires the joint consideration of both range and angle dimensions, posing new challenges to conventional beamforming and tracking strategies. To address this issue, we propose a predictive beamforming framework leveraging a large language model (LLM)-based neural network (LNN), which exploits historical channel state information (CSI) to facilitate accurate future beamforming decisions. Cramér–Rao bounds (CRBs) for angle and distance estimation, along with the achievable sum-rate, are applied as key metrics to evaluate the sensing and communication performance of the V2I system, respectively. Capitalizing on the derived performance metrics, we formulate the optimization problems aiming either to maximize the sum-rate subject to CRB constraints or to minimize the CRB while ensuring a required communication rate, thereby accommodating different design requirements. Moreover, to effectively capture the stochastic nature of vehicle driving behavior, the performance metrics are further expressed in expectation form over the distribution of possible driving states. Consequently, a data-driven optimization approach based on the LNN is adopted to handle the resulting intractable analytical expressions, and the underlying LNN is trained with task-specific loss functions. During the training process, low-rank adaptation (LoRA) is incorporated to fine-tune the pre-trained LLM, which significantly reduces the number of trainable parameters. Simulation results demonstrate that the proposed framework accurately predicts future vehicle kinematic parameters and effectively optimizes the power allocation across transmit links. As a result, it achieves superior and robust performance in both communication and sensing tasks, highlighting its potential as a vital solution for next-generation near-field V2I systems.","PeriodicalId":73294,"journal":{"name":"IEEE journal on selected areas in communications : a publication of the IEEE Communications Society","volume":"44 ","pages":"2510-2527"},"PeriodicalIF":17.2,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145730644","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}