Pub Date : 2026-01-22DOI: 10.1109/TCCN.2026.3657053
Guangjie Han;Yaxin Hu;Yu He;Tongwei Zhang;Feiyan Li
Autonomous underwater vehicle (AUV) swarms are increasingly vital for large-scale underwater data collection. However, they are vulnerable to both external and internal attacks, including identity spoofing and selfish behaviors. To address these attacks, this paper proposes a novel trust evaluation mechanism, named PRLTE, which integrates Physical Layer Authentication (PLA) with Reinforcement Learning (RL). The mechanism comprises three core components: 1) trust calculation. Sink nodes collect multi-source trust evidence, including communication trust and energy trust. Furthermore, “work trust” evaluating data quality and quantity is introduced to mitigate the issue of insufficient historical trust evidence; 2) identity assessment. PLA under the Bellhop channel model is performed to authenticate agent identities and derive “identity trust;” and 3) trust evaluation. An RL-based trust evaluation mechanism is deployed to adaptively optimize trust component weights for agents based on identity trust. Simulation results demonstrate that PRLTE outperforms existing mechanisms in detecting malicious agents, with superior performance across both dense and sparse deployment scenarios.
{"title":"Multi-Source Trust Evaluation Using Physical Layer Authentication and Reinforcement Learning for Distributed AUV Swarms in Underwater Data Collection","authors":"Guangjie Han;Yaxin Hu;Yu He;Tongwei Zhang;Feiyan Li","doi":"10.1109/TCCN.2026.3657053","DOIUrl":"10.1109/TCCN.2026.3657053","url":null,"abstract":"Autonomous underwater vehicle (AUV) swarms are increasingly vital for large-scale underwater data collection. However, they are vulnerable to both external and internal attacks, including identity spoofing and selfish behaviors. To address these attacks, this paper proposes a novel trust evaluation mechanism, named PRLTE, which integrates Physical Layer Authentication (PLA) with Reinforcement Learning (RL). The mechanism comprises three core components: 1) trust calculation. Sink nodes collect multi-source trust evidence, including communication trust and energy trust. Furthermore, “work trust” evaluating data quality and quantity is introduced to mitigate the issue of insufficient historical trust evidence; 2) identity assessment. PLA under the Bellhop channel model is performed to authenticate agent identities and derive “identity trust;” and 3) trust evaluation. An RL-based trust evaluation mechanism is deployed to adaptively optimize trust component weights for agents based on identity trust. Simulation results demonstrate that PRLTE outperforms existing mechanisms in detecting malicious agents, with superior performance across both dense and sparse deployment scenarios.","PeriodicalId":13069,"journal":{"name":"IEEE Transactions on Cognitive Communications and Networking","volume":"12 ","pages":"5537-5551"},"PeriodicalIF":7.0,"publicationDate":"2026-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146042795","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-22DOI: 10.1109/tccn.2026.3657104
Pooria Seyed Eftetahi, Lin Cai, Amir Sepahi
{"title":"Fair Beam Scheduling in LEO Satellite Networks with Reinforcement Learning","authors":"Pooria Seyed Eftetahi, Lin Cai, Amir Sepahi","doi":"10.1109/tccn.2026.3657104","DOIUrl":"https://doi.org/10.1109/tccn.2026.3657104","url":null,"abstract":"","PeriodicalId":13069,"journal":{"name":"IEEE Transactions on Cognitive Communications and Networking","volume":"87 1","pages":""},"PeriodicalIF":8.6,"publicationDate":"2026-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146042788","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}
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.
{"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}
Pub Date : 2026-01-22DOI: 10.1109/tccn.2026.3657147
Bo Chen, Jianping An, Bowen Gui, Liang Zeng
{"title":"High Quality and Secure Speech Transmission at Low Bitrate via Semantic-Acoustic Hybrid Coding for Low-Altitude Intelligent Systems","authors":"Bo Chen, Jianping An, Bowen Gui, Liang Zeng","doi":"10.1109/tccn.2026.3657147","DOIUrl":"https://doi.org/10.1109/tccn.2026.3657147","url":null,"abstract":"","PeriodicalId":13069,"journal":{"name":"IEEE Transactions on Cognitive Communications and Networking","volume":"50 1","pages":""},"PeriodicalIF":8.6,"publicationDate":"2026-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146042786","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-20DOI: 10.1109/TCCN.2026.3656389
Yuhan Ai;Qimei Chen;Dingzhu Wen;Mehdi Bennis
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%.
{"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}
Pub Date : 2025-12-24DOI: 10.1109/TCCN.2025.3647822
Odysseas G. Karagiannidis;Victoria E. Galanopoulou;Panagiotis D. Diamantoulakis;Zhiguo Ding;Octavia A. Dobre
The evolution of wireless communication systems requires flexible, energy-efficient, and cost-effective antenna technologies. Pinching antennas (PAs), which can dynamically control electromagnetic wave propagation through binary activation states, have recently emerged as a promising candidate. In this work, we investigate the problem of optimally selecting a subset of fixed-position PAs to activate in a waveguide, when the aim is to maximize the communication rate at a user terminal. Due to the complex interplay between antenna activation, waveguide-induced phase shifts, and power division, this problem is formulated as a combinatorial fractional 0-1 quadratic program. To efficiently solve this challenging problem, we use neural network architectures of varying complexity to learn activation policies directly from data, leveraging spatial features and signal structure. Furthermore, we incorporate user location uncertainty into our training and evaluation pipeline to simulate realistic deployment conditions. Simulation results demonstrate the effectiveness and robustness of the proposed models.
{"title":"Deep Learning Optimization of Two-State Pinching Antennas Systems","authors":"Odysseas G. Karagiannidis;Victoria E. Galanopoulou;Panagiotis D. Diamantoulakis;Zhiguo Ding;Octavia A. Dobre","doi":"10.1109/TCCN.2025.3647822","DOIUrl":"10.1109/TCCN.2025.3647822","url":null,"abstract":"The evolution of wireless communication systems requires flexible, energy-efficient, and cost-effective antenna technologies. Pinching antennas (PAs), which can dynamically control electromagnetic wave propagation through binary activation states, have recently emerged as a promising candidate. In this work, we investigate the problem of optimally selecting a subset of fixed-position PAs to activate in a waveguide, when the aim is to maximize the communication rate at a user terminal. Due to the complex interplay between antenna activation, waveguide-induced phase shifts, and power division, this problem is formulated as a combinatorial fractional 0-1 quadratic program. To efficiently solve this challenging problem, we use neural network architectures of varying complexity to learn activation policies directly from data, leveraging spatial features and signal structure. Furthermore, we incorporate user location uncertainty into our training and evaluation pipeline to simulate realistic deployment conditions. Simulation results demonstrate the effectiveness and robustness of the proposed models.","PeriodicalId":13069,"journal":{"name":"IEEE Transactions on Cognitive Communications and Networking","volume":"12 ","pages":"4942-4956"},"PeriodicalIF":7.0,"publicationDate":"2025-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145823389","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-19DOI: 10.1109/TCCN.2025.3646157
Irshad A. Meer;Karl-Ludwig Besser;Mustafa Ozger;Dominic A. Schupke;H. Vincent Poor;Cicek Cavdar
Multi-connectivity involves dynamic cluster formation among distributed access points (APs) and coordinated resource allocation from these APs, highlighting the need for efficient mobility management strategies for users with multi-connectivity. In this paper, we propose a novel mobility management scheme for unmanned aerial vehicles (UAVs) that uses dynamic cluster reconfiguration with energy-efficient power allocation in a wireless interference network. Our objective encompasses meeting stringent reliability demands, minimizing joint power consumption, and reducing the frequency of cluster reconfiguration. To achieve these objectives, we propose a hierarchical multi-agent deep reinforcement learning (H-MADRL) framework, specifically tailored for dynamic clustering and power allocation. The edge cloud connected with a set of APs through low latency optical back-haul links hosts the high-level agent responsible for the optimal clustering policy, while low-level agents reside in the APs and are responsible for the power allocation policy. To further improve the learning efficiency, we propose a novel action-observation transition-driven learning algorithm that allows the low-level agents to use the action space from the high-level agent as part of the local observation space. This allows the lower-level agents to share partial information about the clustering policy and allocate the power more efficiently. The simulation results demonstrate that our proposed distributed algorithm achieves comparable performance to the centralized algorithm. Additionally, it offers better scalability, as the decision time for clustering and power allocation increases by only 10% when doubling the number of APs, compared to a 90% increase observed with the centralized approach.
{"title":"Hierarchical Multi-Agent DRL-Based Dynamic Cluster Reconfiguration for UAV Mobility Management","authors":"Irshad A. Meer;Karl-Ludwig Besser;Mustafa Ozger;Dominic A. Schupke;H. Vincent Poor;Cicek Cavdar","doi":"10.1109/TCCN.2025.3646157","DOIUrl":"10.1109/TCCN.2025.3646157","url":null,"abstract":"Multi-connectivity involves dynamic cluster formation among distributed access points (APs) and coordinated resource allocation from these APs, highlighting the need for efficient mobility management strategies for users with multi-connectivity. In this paper, we propose a novel mobility management scheme for unmanned aerial vehicles (UAVs) that uses dynamic cluster reconfiguration with energy-efficient power allocation in a wireless interference network. Our objective encompasses meeting stringent reliability demands, minimizing joint power consumption, and reducing the frequency of cluster reconfiguration. To achieve these objectives, we propose a hierarchical multi-agent deep reinforcement learning (H-MADRL) framework, specifically tailored for dynamic clustering and power allocation. The edge cloud connected with a set of APs through low latency optical back-haul links hosts the high-level agent responsible for the optimal clustering policy, while low-level agents reside in the APs and are responsible for the power allocation policy. To further improve the learning efficiency, we propose a novel action-observation transition-driven learning algorithm that allows the low-level agents to use the action space from the high-level agent as part of the local observation space. This allows the lower-level agents to share partial information about the clustering policy and allocate the power more efficiently. The simulation results demonstrate that our proposed distributed algorithm achieves comparable performance to the centralized algorithm. Additionally, it offers better scalability, as the decision time for clustering and power allocation increases by only 10% when doubling the number of APs, compared to a 90% increase observed with the centralized approach.","PeriodicalId":13069,"journal":{"name":"IEEE Transactions on Cognitive Communications and Networking","volume":"12 ","pages":"4957-4971"},"PeriodicalIF":7.0,"publicationDate":"2025-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11305124","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145785589","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Satellite-aided low-altitude UAV networks utilize satellites to support low-flying drones with communication and navigation for tasks like sensing, surveillance, and delivery. A critical challenge is offloading UAV communication traffic and migrate services to satellites during network congestion. The decentralized structure, changing link conditions, and limited local visibility make it hard to coordinate service migration and request routing in such dynamic environments. To address these challenges, we propose a semantic graph-based multi-agent reinforcement learning (MARL) framework for satellite-aided UAV networks. We formulate service migration and routing as a semantic graph optimization problem, with the objectives of reducing communication delay and increasing network throughput. The framework incorporates two key components: a cyclic message-passing model that enables nodes to infer global network states from limited local observations, and a discrete denoising diffusion model that generates realistic, and dynamic topologies. Our framework leverages semantic feature extraction to further enhance decision-making in routing and service placement. Extensive simulations show that our approach achieves significant reductions in average transmission delay and improvements in the network throughput.
{"title":"Satellite-Aided Low-Altitude UAV Service Migration With Semantic Extraction and Generated Graphs","authors":"Qiongqiong Jia;Jie Zheng;Ling Gao;Jinping Niu;Rui Cao;Jie Ren","doi":"10.1109/TCCN.2025.3645425","DOIUrl":"10.1109/TCCN.2025.3645425","url":null,"abstract":"Satellite-aided low-altitude UAV networks utilize satellites to support low-flying drones with communication and navigation for tasks like sensing, surveillance, and delivery. A critical challenge is offloading UAV communication traffic and migrate services to satellites during network congestion. The decentralized structure, changing link conditions, and limited local visibility make it hard to coordinate service migration and request routing in such dynamic environments. To address these challenges, we propose a semantic graph-based multi-agent reinforcement learning (MARL) framework for satellite-aided UAV networks. We formulate service migration and routing as a semantic graph optimization problem, with the objectives of reducing communication delay and increasing network throughput. The framework incorporates two key components: a cyclic message-passing model that enables nodes to infer global network states from limited local observations, and a discrete denoising diffusion model that generates realistic, and dynamic topologies. Our framework leverages semantic feature extraction to further enhance decision-making in routing and service placement. Extensive simulations show that our approach achieves significant reductions in average transmission delay and improvements in the network throughput.","PeriodicalId":13069,"journal":{"name":"IEEE Transactions on Cognitive Communications and Networking","volume":"12 ","pages":"5136-5147"},"PeriodicalIF":7.0,"publicationDate":"2025-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145778020","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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