Pub Date : 2026-01-22DOI: 10.1109/tccn.2026.3657116
Jialin Zhuang, Lanhua Li, Yusi Long, Bo Gu, Changyan Yi, Shimin Gong
{"title":"Lagrangian-Augmented Learning for Stochastic Age of Accurate Semantic Information Minimization in Mobile Edge Computing Systems","authors":"Jialin Zhuang, Lanhua Li, Yusi Long, Bo Gu, Changyan Yi, Shimin Gong","doi":"10.1109/tccn.2026.3657116","DOIUrl":"https://doi.org/10.1109/tccn.2026.3657116","url":null,"abstract":"","PeriodicalId":13069,"journal":{"name":"IEEE Transactions on Cognitive Communications and Networking","volume":"44 1","pages":""},"PeriodicalIF":8.6,"publicationDate":"2026-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146042797","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.2026.3657053
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}
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