IEEE Open Journal of the Communications Society最新文献

{"title":"Resource Optimization for Semantic Communication in 6G Networks: A Survey","authors":"Sana Sharif;Faria Khandaker;Muhammad Naeem;Waleed Ejaz","doi":"10.1109/OJCOMS.2026.3664496","DOIUrl":"https://doi.org/10.1109/OJCOMS.2026.3664496","url":null,"abstract":"Semantic communication (SemCom) is an emerging technology for sixth-generation (6G) networks that focuses on the transmission of meaning and task-relevant information, rather than raw data. In contrast, traditional communication (TradCom) transmits complete data streams without considering contextual or semantic importance, which often leads to redundancy and inefficient resource utilization. In this survey, we provide a comprehensive analysis of resource optimization for SemCom networks, focusing on mathematical models, optimization techniques, and performance metrics such as data rate, energy efficiency, and delay. We investigate intelligent frameworks and emerging solutions that align with the quality of service (QoS) demands of 6G networks, offering a unified perspective on using SemCom for scalable, efficient, and robust communication systems. We also highlight major challenges, outline future research directions, and present potential solutions for SemCom-powered wireless networks. We provide a discussion on fundamental concepts to provide readers with the essential background needed to understand the technical advancements in the field. We provide a thorough analysis of existing literature to identify key research gaps and highlighted valuable lessons to guide future directions. Overall, this survey serves as a foundational reference for resource optimization in SemCom networks, laying the groundwork for intelligent and context-aware communication systems capable of meeting the stringent performance demands of 6G networks.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"7 ","pages":"1602-1642"},"PeriodicalIF":6.3,"publicationDate":"2026-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11395983","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146223770","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":"Secure Semantic Communication With Non-Targeted and Target-Oriented Stealthy Semantic Protection","authors":"Haiyang Cao;Tong-Xing Zheng;Wenjie Wang;Cong Li;Yiliang Liu;Zhou Su;Jia Shi;Zan Li","doi":"10.1109/OJCOMS.2026.3664498","DOIUrl":"https://doi.org/10.1109/OJCOMS.2026.3664498","url":null,"abstract":"Semantic communication (SC), as an emerging communication paradigm, has garnered increasing attention due to its unique advantages in robustness and bandwidth savings in extreme environments, but the open nature of wireless channels and the reliance on deep learning expose SC systems to serious security threats. Existing defenses primarily focus on degrading eavesdropper reconstruction but neglect stealthiness. In this paper, we propose a lightweight stealth-aware secure SC (LS-SSC) framework for resource-constrained image transmission under eavesdropping threats. Built upon a channel-spatial attention-enhanced reversible residual network (RevNet), LS-SSC utilizes the forward process to inject stealthy non-targeted adversarial attacks (NTAA) and controllable target-oriented adversarial attacks (TOAA) into the transmitted semantic features, effectively misleading eavesdroppers’ downstream AI tasks. Conversely, the legitimate receiver leverages the invertibility of RevNet to eliminate these perturbations and recover the original semantic information. Furthermore, to balance the conflicting objectives of adversarial intensity and visual stealthiness, we propose a piecewise approximation dynamic-weighting strategy guided by validation indicators. We conduct extensive simulations for LS-SSC under NTAA and TOAA. Experimental results demonstrate that LS-SSC reduces parameters by 71% and FLOPs by 41% compared to baselines, while achieving superior attack efficacy, robust stealthiness, and reliable legitimate reconstruction.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"7 ","pages":"1586-1601"},"PeriodicalIF":6.3,"publicationDate":"2026-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11396024","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146223803","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":"Secure Airspace Based on Virtual Trust Bubbles: Blockchain-Assisted V2V Negotiation and Quantum Optimization for Collision-Free UAV Coordination in Zero-Trust Environments","authors":"Fariborz Rasoulie","doi":"10.1109/OJCOMS.2026.3663051","DOIUrl":"https://doi.org/10.1109/OJCOMS.2026.3663051","url":null,"abstract":"The proliferation of uncrewed aerial vehicles (UAVs) in urban and shared airspaces necessitates reliable, secure, and intelligent coordination mechanisms to prevent mid-air collisions. Conventional centralized air-traffic management systems struggle to ensure scalability and trust in decentralized multi-drone ecosystems. This paper proposes a novel Blockchain- and Quantum-Assisted Vehicle-to-Vehicle (BQ-V2V) negotiation framework that enables autonomous UAVs to cooperatively negotiate right-of-way and plan collision-free trajectories in a zero-trust environment. The proposed architecture integrates five functional layers—Perception, Communication, Blockchain-based Trust and Consensus, Quantum Approximate Optimization Algorithm (QAOA)-based Decision, and Control and Execution—to establish a complete perception-to-action pipeline. Each UAV employs V2V communication to share its intent and flight parameters, while a lightweight blockchain protocol ensures non-repudiation, tamper-resistance, and trust among agents without requiring a centralized authority. Right-of-way conflicts are modeled as a combinatorial optimization problem mapped to a quantum Hamiltonian, and QAOA is used to derive near-optimal cooperative decisions in real time. The resulting control policies are applied through nonlinear trajectory-tracking controllers that guarantee Lyapunov stability and collision-free convergence. Simulation results demonstrate that the proposed BQ-V2V framework achieves 34.6% faster negotiation convergence, 27% reduction in communication overhead, and significant trust resilience under adversarial or communication-compromised conditions compared with conventional multi-agent and blockchain-only baselines. This work establishes a foundational step toward scalable, decentralized, and quantum-aware airspace management for next-generation UAV swarms. The proposed BQ-V2V framework was implemented and validated through a co-simulation environment combining MATLAB/Simulink with PX4 SITL and Gazebo, enabling high-fidelity modeling of UAV dynamics, multi-agent coordination, and real-time communication behaviors under realistic sensor noise, latency, and environmental disturbances.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"7 ","pages":"1537-1557"},"PeriodicalIF":6.3,"publicationDate":"2026-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11386969","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146223655","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":"Performance Analysis of a Nonlinear Energy-Harvesting Cooperative Spectrum-Sharing Network With Statistical Channel Knowledge","authors":"Jamil Farhat;Srijibendu Bagchi;Glauber Brante;Victoria Dala Pegorara Souto;Richard Demo Souza","doi":"10.1109/OJCOMS.2026.3662570","DOIUrl":"https://doi.org/10.1109/OJCOMS.2026.3662570","url":null,"abstract":"This paper investigates the performance of an energy-harvesting cooperative spectrum-sharing radio network. The proposed system model includes a pair of primary nodes and a relay that harvests energy from a multi-antenna power beacon, which also operates as a gateway for the relay. The energy beamforming design at the power beacon is based only on statistical channel state information. A realistic nonlinear rectenna model is adopted at the relay to capture both sensitivity and saturation effects, as well as capacitor charging dynamics. Analytical expressions are derived for the outage probabilities of the primary and secondary communication links under Rician fading, and asymptotic analyses are presented for both high- and low-signal-to-noise ratio regimes. The results reveal the presence of outage floors due to energy saturation, as well as reliability trade-offs governed by the energy allocation factor at the relay, nodes distance, and array size. The study provides useful design insights for energy-efficient spectrum-sharing IoT systems envisioned for next generation communication systems.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"7 ","pages":"1477-1490"},"PeriodicalIF":6.3,"publicationDate":"2026-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11381418","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146223762","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":"Zero-Touch Cross-Domain Orchestration in the 6G Compute Continuum: A Framework Evaluation in Real-Life Scenarios","authors":"Raúl Cuervo Bello;Gabriele Scivoletto;Giada Landi;Razvan Mihai;Christian Catalin Petrache;Miguel Camelo;Johann Marquez-Barja;Nina Slamnik-Kriještorac","doi":"10.1109/OJCOMS.2026.3662044","DOIUrl":"https://doi.org/10.1109/OJCOMS.2026.3662044","url":null,"abstract":"The distribution of services across heterogeneous edge and cloud infrastructures in Beyond 5G (B5G) and Sixth-Generation (6G) networks increases operational complexity, while existing MANagement and Orchestration (MANO) solutions remain single-domain and lack unified telemetry or autonomous cross-domain decision mechanisms. Prior work proposes cross-domain orchestration concepts, but most validations rely on simulation and overlook interoperability issues, non-stationary latency, and inconsistent Key Performance Indicator (KPI) models in real Network Function Virtualization Infrastructure (NFVI) deployments. This paper introduces a modular, technology-agnostic Zero-touch Network and Service Management (ZSM) framework that provides unified NFVI abstraction and a multi-criteria decision engine that jointly considers latency, compute load, and energy consumption for autonomous service placement, scaling, and dynamic resource provisioning. Evaluation in a real-world deployment across geographically distributed testbeds demonstrates that our ZSM framework mitigates latency spikes by up to 96%, reduces overloaded-node latency by 36%, and maintains stable performance under variable load. These results confirm the practicality and effectiveness of zero-touch, multi-domain orchestration for future 6G compute-continuum environments.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"7 ","pages":"1376-1395"},"PeriodicalIF":6.3,"publicationDate":"2026-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11373539","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146175638","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":"IQFM—A Wireless Foundation Model for I/Q Streams in AI-Native 6G","authors":"Omar Mashaal;Hatem Abou-Zeid","doi":"10.1109/OJCOMS.2026.3661435","DOIUrl":"https://doi.org/10.1109/OJCOMS.2026.3661435","url":null,"abstract":"Foundation models have shown remarkable potential in natural language processing and computer vision, yet remain in their infancy in wireless communications. While a few efforts have explored image-based modalities such as channel state information (CSI) and frequency spectrograms, foundation models operating directly on raw in-phase/quadrature (IQ) signals remain largely unexplored. This paper presents IQFM, the first foundation model for wireless communications built on raw multi-antenna IQ data. IQFM supports diverse tasks including modulation classification, angle-of-arrival (AoA), beam prediction, and RF fingerprinting, without handcrafted features or heavy preprocessing. We introduce a task-aware augmentation strategy that categorizes transformations into core augmentations, such as cyclic time shifting, and task-specific augmentations, enabling structured representation learning within a contrastive self-supervised framework. Using this approach, a lightweight encoder pre-trained on over-the-air multi-antenna IQ data achieves 99.67% accuracy on modulation classification and 65.45% on AoA classification with only one labeled sample per class through linear probing, far outperforming supervised baselines. With minimal Low-Rank Adaptation (LoRA) updates, IQFM generalizes to out-of-distribution tasks, showing cross-dataset generalization on modulation classification and strong performance on unseen tasks such as beam prediction and RF fingerprinting. These results establish raw IQ-based foundation models as efficient, reusable encoders for multi-task learning in AI-native 6G systems.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"7 ","pages":"1426-1441"},"PeriodicalIF":6.3,"publicationDate":"2026-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11372716","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146175635","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":"AoI-Aware Flow Allocation in IoT Systems Using Matching Theory for Edge Computing","authors":"Benedetta Picano;Romano Fantacci","doi":"10.1109/OJCOMS.2026.3661290","DOIUrl":"https://doi.org/10.1109/OJCOMS.2026.3661290","url":null,"abstract":"The Internet of Things (IoT) enables intelligent monitoring and control through large-scale data sensing and real-time processing, leading to increasing computational demands. In this context, information freshness, commonly quantified by the Age of Information (AoI), plays a crucial role in evaluating system performance. To efficiently support data processing while reducing latency and network congestion, IoT monitoring systems increasingly rely on edge computing architectures, where computational resources are deployed close to end devices. This paper addresses the problem of computational workload distribution among edge nodes and proposes a flow allocation technique based on matching theory. The proposed approach enhances system performance by serving more devices with fewer edge nodes, achieving an approximate <inline-formula> <tex-math>$45~%$ </tex-math></inline-formula> gain over alternative solutions in terms of peak AoI.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"7 ","pages":"1558-1567"},"PeriodicalIF":6.3,"publicationDate":"2026-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11373248","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146223757","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":"Green ISAC: A Design Framework for Resilient TDD Communication and High-Resolution Sensing in Asymmetric Systems","authors":"Nusaibah A. Alshorman;Sonia Aïssa;Hüseyin Arslan","doi":"10.1109/OJCOMS.2026.3661562","DOIUrl":"https://doi.org/10.1109/OJCOMS.2026.3661562","url":null,"abstract":"The design of future 6G networks is driven by the dual requirements of supporting new integrated sensing and communication (ISAC) functionalities while drastically improving energy efficiency. A common strategy for this is spatial power saving, where the radio frequency chains are dynamically scaled based on network load. Conventionally, this is performed by switching off antennas in a uniform manner, resulting in a smaller, compact sub-array. While this method saves power and preserves the channel reciprocity for time division duplex (TDD), the resulting small aperture array suffers from poor sensing resolution. In contrast, this paper proposes a novel adaptive antenna selection framework that intelligently configures the active elements during low-load mode to form a genetically optimized sparse array. A multi-objective co-design methodology is developed to find the Pareto front of optimal layouts that balance two conflicting goals: enhancing sensing accuracy by synthesizing a large, hole-free virtual sum co-array, and ensuring resilient TDD communication by mitigating channel estimation errors. This framework enables two operational modes: a power-saving asymmetric mode for ISAC, and a high-performance fully sparse mode for dedicated sensing. Simulation results demonstrate that the intelligent sparse array selection significantly outperforms the conventional uniform deactivation strategy, providing a clear pathway to adaptive systems that achieve substantial energy savings without compromising on high-performance sensing and communication.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"7 ","pages":"1523-1536"},"PeriodicalIF":6.3,"publicationDate":"2026-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11373176","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146223718","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":"Enhancing Physical Layer Security in Cell-Free Networks Through IRS-Assisted Meta-Reinforcement Learning","authors":"Sivasankar. S;Markkandan. S","doi":"10.1109/OJCOMS.2026.3661530","DOIUrl":"https://doi.org/10.1109/OJCOMS.2026.3661530","url":null,"abstract":"This paper provides a framework to enhance physical layer security (PLS) in cell-free massive multiple-input multiple-output systems (CF-mMIMO). This system combines multiple intelligent reflecting surfaces (IRSs) with a type of machine learning called meta-reinforcement learning (Meta-RL). The proposed method addresses the primary challenge of protecting distributed wireless transmissions from sophisticated eavesdropping, taking into account channel state information (CSI), hardware constraints, and quantized phase control. This research presents a joint optimization method achieved through the exploitation of active beamforming at distributed access points (APs) and passive beamforming at multiple IRS panels via a combined Meta-RL and Q-learning algorithm. The system adapts to changing channel conditions, estimation inaccuracies, and eavesdropping risks while maintaining computational efficiency with a decoupled two-timescale optimization structure. The system model explicitly incorporates CSI estimation errors, channel degradation over time, multiple IRS coordination, and authentic hardware limitations. Extensive simulations show that the suggested approach surpasses conventional benchmarks in terms of secrecy rate, energy efficiency, and speed of convergence. In particular, it achieves up to 45% higher secrecy rates and 38% better energy efficiency compared to the most effective current approaches. It remains reliable even under substantial CSI imperfections (error variance up to 0.5), discrete phase quantization (3 to 5 bits), and multi-IRS coordination overhead. Two-timescale stochastic approximation is used to provide theoretical convergence guarantees, with practical feasibility supported by a detailed analysis of complexity.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"7 ","pages":"1453-1476"},"PeriodicalIF":6.3,"publicationDate":"2026-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11373237","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146223761","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":"Generative AI-Enhanced Robust Semantic Communication Architecture for AAV Image Transmission","authors":"Canpu Liu;Li Zhou;Xinfeng Deng;Yichi Zhang;Nan Li;Jun Xiong;Boon-Chong Seet","doi":"10.1109/OJCOMS.2026.3660029","DOIUrl":"https://doi.org/10.1109/OJCOMS.2026.3660029","url":null,"abstract":"Autonomous aerial vehicle (AAV) wireless image transmission has gained widespread application across various fields due to its flexibility, yet it faces critical challenges such as resource constraints and degradation of reconstruction quality caused by harsh channel conditions. To address these issues, we designed a lightweight semantic communication backbone network that substantially reduces the computational and storage overhead of AAVs through codebook assistance and efficient encoder-decoder design. On this basis, to tackle severe image degradation under adverse channel conditions, we introduced a generative artificial intelligence-based (GAI) enhancement module. Specifically, we developed a semantic refinement network (SRN) that employs an innovative signal-to-noise ratio (SNR) adaptive feature-wise linear modulation (FiLM) layer to dynamically adjust its refinement strategy based on real-time channel quality, fundamentally transforming the image reconstruction paradigm from traditional signal recovery to conditional content generation. Extensive experimental results demonstrate that our proposed framework significantly outperforms the current state-of-the-art method under extreme channel conditions, highlighting its great potential for achieving robust AAV image transmission in challenging operational environments.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"7 ","pages":"1442-1452"},"PeriodicalIF":6.3,"publicationDate":"2026-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11370183","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146223749","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}