IEEE Transactions on Cognitive Communications and Networking最新文献

{"title":"Channel-Adaptive Cross-Modal Generative Semantic Communication for Point Cloud Transmission","authors":"Wanting Yang;Zehui Xiong;Qianqian Yang;Ping Zhang;Mérouane Debbah;Rahim Tafazolli","doi":"10.1109/TCCN.2026.3657061","DOIUrl":"10.1109/TCCN.2026.3657061","url":null,"abstract":"With the rapid development of autonomous driving and extended reality, efficient transmission of point clouds (PCs) has become increasingly important. In this context, we propose a novel channel-adaptive cross-modal generative semantic communication (SemCom) for PC transmission, called GenSeC-PC. GenSeC-PC employs a semantic encoder that fuses images and point clouds, where images serve as non-transmitted side information. Meanwhile, the decoder is built upon the backbone of PointDif. Such a cross-modal design not only ensures high compression efficiency but also delivers superior reconstruction performance compared to PointDif. Moreover, to ensure robust transmission and reduce system complexity, we design a streamlined and asymmetric channel-adaptive joint semantic-channel coding architecture, where only the encoder needs the feedback of average signal-to-noise ratio (SNR) and available bandwidth. In addition, rectified denoising diffusion implicit models is employed to accelerate the decoding process to the millisecond level, enabling real-time PC communication. Unlike existing methods, GenSeC-PC leverages generative priors to ensure reliable reconstruction even from noisy or incomplete source PCs. More importantly, it supports fully analog transmission, improving compression efficiency by eliminating the need for error-free side information transmission common in prior SemCom approaches. Simulation results confirm the effectiveness of cross-modal semantic extraction and dual-metric guided fine-tuning, highlighting the framework’s robustness across diverse conditions—including low SNR, bandwidth limitations, varying numbers of 2D images, and previously unseen objects.","PeriodicalId":13069,"journal":{"name":"IEEE Transactions on Cognitive Communications and Networking","volume":"12 ","pages":"5983-5998"},"PeriodicalIF":7.0,"publicationDate":"2026-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146042792","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}

{"title":"Joint Resource Allocation for Underwater Acoustic Cooperative Communication Networks: A Hierarchical Combinatorial Bandit Approach","authors":"Song Han;Hongyun Zhang;Xinbin Li;Junzhi Yu;Zhixin Liu;Tongwei Zhang;Xin Zheng;Weigang Nie","doi":"10.1109/TCCN.2026.3656295","DOIUrl":"https://doi.org/10.1109/TCCN.2026.3656295","url":null,"abstract":"This study investigates a joint resource allocation problem for the unknown time-varying underwater acoustic cooperative communication networks. A low-computational-cost and high-efficiency joint strategy decision-making approach, enhanced in three aspects: improving the quantity and quality of learning information, and optimizing the search method for the complex coupled strategy space, is proposed. First, we formulate a combinatorial multi-armed bandit learning model, which offers the separate learning spaces for each sub-strategy. Thus, the player can effectively learn the high-dimensional entire joint strategy by searching the low-dimensional sub-strategy space with low computational cost to avoid the complex direct search in the high-dimensional space. As a result, the solving difficulty caused by the complex coupling of joint sub-strategies can be significantly mitigated. Second, a hierarchical combinatorial bandit learning structure is formulated to improve the quantity and quality of learning information. The estimation layer is constructed to denoise the observed rewards, thereby providing higher quality learning information. The prediction layer is constructed to predict the rewards of all sub-strategies, thereby enriching learning information. Furthermore, in the decision-making layer, we propose an extended upper confidence bound 1 index function to achieve the effective integration of the higher quality denoised learning information and richer predicted learning information to improve the online decision. Finally, the simulation results verify that the proposed innovations can significantly improve the online decision-making efficiency for complex coupled joint strategies and effectively enhance the performance of underwater acoustic cooperative communication networks.","PeriodicalId":13069,"journal":{"name":"IEEE Transactions on Cognitive Communications and Networking","volume":"12 ","pages":"6104-6118"},"PeriodicalIF":7.0,"publicationDate":"2026-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146223597","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}

{"title":"Cross-Modal Collaborative Diffusion Models for Distributed AI-Generated Content","authors":"Yuhan Ai;Qimei Chen;Dingzhu Wen;Mehdi Bennis","doi":"10.1109/TCCN.2026.3656389","DOIUrl":"https://doi.org/10.1109/TCCN.2026.3656389","url":null,"abstract":"Powered by Artificial Intelligence (AI), AI-Generated Content (AIGC) has recently emerged as a promising approach for synthesizing multimodal content, including text, images, and videos. Diffusion model is one of the predominant frameworks within AIGC for generating diverse and photorealistic images with high fidelity. However, deploying diffusion models on practical resource-constrained edge devices confronts critical challenges for their limited computational abilities and severe communication overhead, which spurs research interests in distributed AIGC. Existing works on distributed AIGC primarily focus on either increasing training cost to pursue higher generation quality or sacrificing performance to achieve communication efficiency, merely considering their integration impacts. To address these issues, we propose a novel Cross-Modal Collaborative Diffusion Model (Co-Diff) framework that achieves computation-and-communication efficient image synthesis without compromising robust generalization. The core innovation lies in our split diffusion architecture, which strategically offloads computation-intensive reverse denoising to the server and edge devices perform lightweight forward diffusion with text-guided attention. In addition, we design a learnable quantization module that encodes high-dimensional noise into compact codes to minimize communication overhead. We establish a theoretical convergence analysis for Co-Diff, deriving a closed-form expression that reveal a quantifiable trade-off between denoising accuracy and computational latency. Based on the theoretical findings, we formulate a joint communication-and-computation optimization problem, which obtains an analytical solution among diffusion-step scheduling, processing-frequency allocation, and bandwidth distribution. Extensive experiments validate our theoretical analysis, and demonstrate the effectiveness of the proposed design for computation-and-communication efficient Co-Diff. Compared with conventional distributed learning frameworks, the proposed Co-Diff increases communication-and-computation efficiency by 72.8%, as well as boosts image synthesis quality by 12.9%.","PeriodicalId":13069,"journal":{"name":"IEEE Transactions on Cognitive Communications and Networking","volume":"12 ","pages":"5552-5565"},"PeriodicalIF":7.0,"publicationDate":"2026-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146082000","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}

{"title":"Meta-Learning-Based STAR-RIS for Dynamic Multi-Mobile-User Downlink Communications Over 6G Mobile Wireless Networks","authors":"Yujie Su;Fei Wang;Xi Zhang","doi":"10.1109/TCCN.2026.3662327","DOIUrl":"https://doi.org/10.1109/TCCN.2026.3662327","url":null,"abstract":"Reconfigurable intelligen surface (RIS) has been widely envisioned as a key technique, which can enhance communication quality of service (QoS) for mobile users (MUs) by reconfiguring wireless propagation environments. Unlike traditional RISs that can only reflect signals, simultaneously transmitting and reflecting RIS (STAR-RIS) can extend half-space coverage to full-space coverage by simultaneously transmitting and reflecting incident signals. In this paper, we propose to develop the joint optimization for the transmission and reflection coefficients, i.e., phase-shifts and amplitudes, of STAR-RIS and the transmit beamforming of base station (BS) over STAR-RIS-aided downlink communications, where multiple MUs move in real time and can change their movement patterns dynamically. First, taking into account MUs’ mobility, we formulate a rate maximization problem to maximize the average transmission rate from BS to MUs, under the continuous phase-shifts and path-loss channel modes with perfect channel state information (CSI)/MUs-locations estimation. Second, since the meta-learning can rapidly adapt to dynamic network environments, we develop a scheme based on meta-learning integrated with the proximal policy optimization (PPO) algorithm to optimize STAR-RIS’s transmission and reflection coefficients and BS’s transmit beamforming. Third, we extend our work to the more realistic communication scenario under the discrete phase-shifts and Rician channel models with imperfect CSI/MUs-locations estimation. Finally, we validate and evaluate our developed schemes through extensive simulations, showing that our meta-learning-based schemes significantly outperform baseline schemes and can adapt to MUs’ new movement patterns rapidly.","PeriodicalId":13069,"journal":{"name":"IEEE Transactions on Cognitive Communications and Networking","volume":"12 ","pages":"7559-7573"},"PeriodicalIF":7.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147796093","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}

{"title":"A Hybrid Quantum Neural Network-Based Robust Resource Optimization for UAV Network Slicing","authors":"Guorong Zhou;Kai Liang;Liqiang Zhao;Gan Zheng;Jiandong Li;Kai-Kit Wong;Chan-Byoung Chae","doi":"10.1109/TCCN.2026.3689862","DOIUrl":"https://doi.org/10.1109/TCCN.2026.3689862","url":null,"abstract":"To enhance customized terrestrial communications and support diverse independent emergency services, uncrewed aerial vehicle (UAV) network slicing is regarded as a feasible solution. However, UAV network slicing suffers from UAV’s limited resources and imperfect channel state information (CSI) due to channel errors. In this paper, we propose a robust resource optimization framework with channel uncertainty for UAV network slicing, by introducing a hybrid quantum neural network (HQNN)-based multi-agent deep reinforcement learning (MADRL) algorithm. Specifically, to cope with limited UAV resources, inspired by Open Radio Access Network (RAN), we decompose users’ baseband processing functions and dynamically distribute them across different UAV sub-networks. Then, considering channel uncertainty, we formulate a quantile-based delay minimization problem under an outage probability constraint. This problem jointly optimizes function splitting (FS), user association, and UAV power allocation. To obtain robust resource optimization, we customize a HQNN-based multi-agent dueling double deep Q-network (HQMAD3QN). It perfectly integrates the mature techniques of MADRL for processing multiple elements and the advanced computational capabilities of HQNNs coupling the quantum neural network with the convolutional neural network. Notably, we utilize a Monte Carlo (MC)-based data augmentation module to address the difficult outage probability constraint. Numerical results show that the proposed method outperforms state-of-the-art approaches in terms of service delay, robustness, constraint violation, and computational complexity.","PeriodicalId":13069,"journal":{"name":"IEEE Transactions on Cognitive Communications and Networking","volume":"12 ","pages":"7862-7877"},"PeriodicalIF":7.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147828722","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}

{"title":"Toward AI-Native Ultra-Dense Networks: Cell Association via Multi-View Sensing","authors":"Khoa Anh Ngo;Seungnyun Kim;Jihoon Moon;Byungju Lee;Jiao Wu;Byonghyo Shim","doi":"10.1109/TCCN.2026.3685403","DOIUrl":"10.1109/TCCN.2026.3685403","url":null,"abstract":"Millimeter wave (mmWave)/terahertz (THz) ultra-dense networks (UDNs) are anticipated to meet the technical performance requirements (TPRs) of immersive 6G applications. By deploying a large number of small base stations (SBSs), UDN can mitigate the severe attenuation in mmWave and THz frequency bands. To fully enjoy the benefits of mmWave/THz UDNs, ensuring line-of-sight (LoS) communication between SBSs and user equipments (UEs) is essential. However, identifying LoS conditions and determining proper cell associations require a contextual understanding of the surrounding environments, which conventional wireless measurement-based approaches may not readily provide. In this paper, we propose a novel cell association scheme for 6G mmWave/THz UDN based on vision sensing and computer vision (CV) technologies. The key idea of the proposed technique is to directly extract the positions and LoS conditions of UEs using multi-view RGB images. As the proposed technique operates without relying on wireless measurements, it eliminates the need for pilot transmissions and channel feedback, thereby reducing signaling overhead and latency significantly. Furthermore, by capturing UEs from multiple viewpoints, we can effectively detect them even in the presence of occlusions. From extensive numerical evaluations on realistic UDN scenarios, we show that the proposed scheme achieves <inline-formula> <tex-math>$30{,}%$ </tex-math></inline-formula> throughput gain over conventional cell association methods.","PeriodicalId":13069,"journal":{"name":"IEEE Transactions on Cognitive Communications and Networking","volume":"12 ","pages":"7845-7861"},"PeriodicalIF":7.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147725739","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}

{"title":"Generative AI-Empowered Signal Processing for Collaborative Embodied Agents: A Survey on Agentic Security","authors":"Gaofeng Pan;Yi Tao;Ziyi Yang;Junnan Li;Yuanyuan Peng;Xuemin Bao;Yiyang Zhang;Zizheng Hua;Shuai Wang;Rui Zhang;Changhao Du","doi":"10.1109/TCCN.2026.3676045","DOIUrl":"10.1109/TCCN.2026.3676045","url":null,"abstract":"The transition from the Internet of Things (IoT) to the Internet of Agents (IoA) necessitates a fundamental paradigm shift in physical layer security, moving from static, rule-based defenses toward autonomous, agentic resilience. However, current embodied nodes often lack the cognitive capability required to handle adaptive, intelligent threats. We propose employing Generative AI (GAI) as the cognitive engine—rather than merely a signal processing tool—to empower agents to construct an internal world model of the physical environment. In this work, we present a holistic agentic security framework that instills autonomy across two key dimensions. First, Endogenous Self-Evolution allows agents to utilize GAI for predictive environmental modeling and autonomous resource decision-making, thereby self-calibrating against hardware impairments. Second, Exogenous Active Defense implements a generative observe-orient-decide-act (OODA) loop. This loop enables agents to autonomously execute intelligent threat perception, confrontation planning, and a strategic duality of behavioral concealment and identity camouflage. Finally, we discuss the path toward general-purpose security agents via neuro-symbolic architectures. This survey bridges the gap between signal generation and agentic reasoning, defining a new era where security becomes an innate, evolving property of collaborative embodied intelligence.","PeriodicalId":13069,"journal":{"name":"IEEE Transactions on Cognitive Communications and Networking","volume":"12 ","pages":"6741-6759"},"PeriodicalIF":7.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147492840","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}

{"title":"Joint Spatio-Temporal Federated Learning for Cellular Traffic Prediction","authors":"Chenhan Zhai;Hui Liu;Ertong Shang;Lizhe Zhang;Zeyu Qiu;Junzhao Du","doi":"10.1109/TCCN.2026.3694873","DOIUrl":"https://doi.org/10.1109/TCCN.2026.3694873","url":null,"abstract":"Cellular traffic prediction is essential for the energy-efficient and intelligent management of base stations (BSs). In this context, Federated Learning allows BSs to collaboratively train a shared model without transmitting raw data, thus significantly reducing communication overhead and enhancing privacy protection. Despite these advantages, most existing methods struggle to alleviate the coupled spatio-temporal heterogeneity of cellular traffic data across BSs, resulting in suboptimal global prediction performance. The main reason is that they decouple spatio-temporal relationships by modeling temporal and spatial properties independently, with limited consideration of their dependencies in cellular traffic. To overcome these limitations, we propose FedJST, a joint spatio-temporal federated cellular traffic prediction method that effectively integrates the temporal and spatial properties of cellular traffic from different BSs in two key aspects. Firstly, to capture spatio-temporal dependencies, we propose a multi-scale feature guidance strategy. This strategy leverages global multi-scale feature to incorporate shared spatio-temporal information, encouraging local models to capture complex spatio-temporal interactions. Secondly, we propose a spatio-temporal property-aware BS selection strategy that leverages a lightweight augmented dataset to prioritize representative BSs for training, enabling effective learning of diverse spatio-temporal patterns. We conduct extensive experiments across six cases on two real-world datasets, demonstrating that FedJST achieves average improvements of 5.95%, 4.43%, and 2.99% in RMSE, MAE, and <inline-formula> <tex-math>$R^{2}$ </tex-math></inline-formula> metrics, respectively.","PeriodicalId":13069,"journal":{"name":"IEEE Transactions on Cognitive Communications and Networking","volume":"12 ","pages":"8609-8624"},"PeriodicalIF":7.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148236081","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}

{"title":"Full-Duplex ISAC-Enabled D2D Underlaid Cellular Networks: Joint Transceiver Beamforming and Power Allocation","authors":"Tao Jiang;Ming Jin;Qinghua Guo;Yinhong Liu;Yaming Li;Junteng Yao","doi":"10.1109/TCCN.2025.3552627","DOIUrl":"10.1109/TCCN.2025.3552627","url":null,"abstract":"Integrating device-to-device (D2D) communication into cellular networks can significantly reduce the transmission burden on base stations (BSs). Besides, integrated sensing and communication (ISAC) is envisioned as a key feature in future wireless networks. In this work, we consider a full-duplex ISAC-based D2D underlaid system, and propose a joint beamforming and power allocation scheme to improve the performance of the coexisting ISAC and D2D networks. To enhance spectral efficiency, a sum rate maximization problem is formulated for the full-duplex ISAC-based D2D underlaid system, which is non-convex. To solve the non-convex optimization problem, we propose a successive convex approximation (SCA)-based iterative algorithm and prove its convergence. Numerical results are provided to validate the effectiveness of the proposed scheme with the iterative algorithm, demonstrating that the proposed scheme outperforms state-of-the-art ones in both communication and sensing performance.","PeriodicalId":13069,"journal":{"name":"IEEE Transactions on Cognitive Communications and Networking","volume":"12 ","pages":"1-13"},"PeriodicalIF":8.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143661499","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}

{"title":"Near-Field Channel Estimation for XL-MIMO: A Deep Generative Model Guided by Side Information","authors":"Zhenzhou Jin;Li You;Derrick Wing Kwan Ng;Xiang-Gen Xia;Xiqi Gao","doi":"10.1109/TCCN.2025.3566047","DOIUrl":"10.1109/TCCN.2025.3566047","url":null,"abstract":"This paper investigates the near-field (NF) channel estimation (CE) for extremely large-scale multiple-input multiple-output (XL-MIMO) systems. Considering the pronounced NF effects in XL-MIMO communications, we first establish a joint angle-distance (AD) domain-based spherical-wavefront physical channel model that captures the inherent sparsity of XL-MIMO channels. Leveraging the channel’s sparsity in the joint AD domain, the CE is approached as a task of reconstructing sparse signals. Anchored in this framework, we first propose a compressed sensing algorithm to acquire a preliminary channel estimate. Harnessing the powerful implicit prior learning capability of generative artificial intelligence (GenAI), we further propose a GenAI-based approach to refine the estimated channel. Specifically, we introduce the preliminary estimated channel as side information, and derive the evidence lower bound (ELBO) of the log-marginal distribution of the target NF channel conditioned on the preliminary estimated channel, which serves as the optimization objective for the proposed generative diffusion model (GDM). Additionally, we introduce a more generalized version of the GDM, the non-Markovian GDM (NM-GDM), to accelerate the sampling process, achieving an approximately tenfold enhancement in sampling efficiency. Experimental results indicate that the proposed approach is capable of offering substantial performance gain in CE compared to existing benchmark schemes within NF XL-MIMO systems. Furthermore, our approach exhibits enhanced generalization capabilities in both the NF or far-field (FF) regions.","PeriodicalId":13069,"journal":{"name":"IEEE Transactions on Cognitive Communications and Networking","volume":"12 ","pages":"628-643"},"PeriodicalIF":8.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143898256","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}