Pub Date : 2026-02-02DOI: 10.1109/tccn.2026.3660231
Zida Guo, Rong Chai, Ruijin Sun, Chengchao Liang, Qianbin Chen
{"title":"System Utility Function Optimization-based Flight Trajectory and Resource Allocation for UAV-Assisted Integrated Sensing and Communication Systems","authors":"Zida Guo, Rong Chai, Ruijin Sun, Chengchao Liang, Qianbin Chen","doi":"10.1109/tccn.2026.3660231","DOIUrl":"https://doi.org/10.1109/tccn.2026.3660231","url":null,"abstract":"","PeriodicalId":13069,"journal":{"name":"IEEE Transactions on Cognitive Communications and Networking","volume":"41 1","pages":""},"PeriodicalIF":8.6,"publicationDate":"2026-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146101487","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}
Ray-tracing (RT) channel simulation has been widely used for simulating and analyzing propagation of electromagnetic waves in complex environments. Accuracy of RT simulations depends on environment construction, including both scene structure and material classification. However, existing RT studies rely on manual material segmentation and presuppose idealized material parameters, which highly overlooks critical challenges of material recognition from real-world data. This prevents RT from being applied in complex scenarios and leads to inaccurate simulation results. To address this issue, we use point cloud measurements to capture real-world environment information and propose a dual-branch network based on PointNet model to automatically classify environmental materials by integrating point cloud data and LiDAR-derived feature parameters. The proposed network significantly enhances material classification accuracy within complex scenes, thereby delivering more precise and computationally efficient input data for RT simulations. Furthermore, we analyze influence of material recognition accuracy on simulation parameters, such as path loss and delay spread. The results demonstrate that the proposed network achieves high classification performance and meets accuracy requirements of RT, thereby contributing to more realistic and reliable predictions for wireless systems. This approach lays a crucial foundation for development of environment-aware models for 6G networks, enabling more effective simulation of outdoor communication environments.
{"title":"Point Cloud-Based Environmental Material Classification for Wireless Channel Ray-Tracing Simulations","authors":"Zhuoyin Li;Ruisi He;Mi Yang;Ziyi Qi;Zhong Zhang;Haoxiang Zhang;Jiahui Han;Bo Ai;Zhangdui Zhong","doi":"10.1109/TCCN.2026.3659825","DOIUrl":"10.1109/TCCN.2026.3659825","url":null,"abstract":"Ray-tracing (RT) channel simulation has been widely used for simulating and analyzing propagation of electromagnetic waves in complex environments. Accuracy of RT simulations depends on environment construction, including both scene structure and material classification. However, existing RT studies rely on manual material segmentation and presuppose idealized material parameters, which highly overlooks critical challenges of material recognition from real-world data. This prevents RT from being applied in complex scenarios and leads to inaccurate simulation results. To address this issue, we use point cloud measurements to capture real-world environment information and propose a dual-branch network based on PointNet model to automatically classify environmental materials by integrating point cloud data and LiDAR-derived feature parameters. The proposed network significantly enhances material classification accuracy within complex scenes, thereby delivering more precise and computationally efficient input data for RT simulations. Furthermore, we analyze influence of material recognition accuracy on simulation parameters, such as path loss and delay spread. The results demonstrate that the proposed network achieves high classification performance and meets accuracy requirements of RT, thereby contributing to more realistic and reliable predictions for wireless systems. This approach lays a crucial foundation for development of environment-aware models for 6G networks, enabling more effective simulation of outdoor communication environments.","PeriodicalId":13069,"journal":{"name":"IEEE Transactions on Cognitive Communications and Networking","volume":"12 ","pages":"5878-5890"},"PeriodicalIF":7.0,"publicationDate":"2026-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146089962","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 : 2026-01-30DOI: 10.1109/TCCN.2026.3659849
Minhyeok Jang;Jalel Ben-Othman;Hyunbum Kim
Object detection is a fundamental task in computer vision with broad applications in autonomous transportation driving, smart surveillance, and traffic monitoring. In the context of Computing Power Networks (CPNs), which interconnect cloud, edge, and terminal nodes to support distributed AI services, deploying efficient object detection models under constrained computational resources is a critical challenge, particularly at the edge and terminal layers. This study investigates backbone optimization for the YOLOv11M object detection framework to improve computational efficiency while maintaining detection performance. We propose two lightweight variants, YOLOv11M-MN and YOLOv11M-Shuffle, by replacing the original backbone with MobileNetV3-Small and ShuffleNetV2, respectively. All Edge AI-enabled models share an identical detection head and training pipeline to ensure fair and controlled comparisons. To reflect resource-limited CPN environments, all experiments are conducted under CPU-only settings with staged training budgets. Performance is evaluated using the COCO128 dataset in terms of FLOPs, parameter count, inference latency, and detection accuracy. Experimental results demonstrate that the proposed lightweight backbones significantly reduce computational overhead and inference time, while exhibiting different accuracy–efficiency trade-offs, highlighting their suitability for selective deployment across heterogeneous CPN layers.
{"title":"Edge AI-Enabled Backbone Optimization for Real-Time Object Detection in Computing Power Networks","authors":"Minhyeok Jang;Jalel Ben-Othman;Hyunbum Kim","doi":"10.1109/TCCN.2026.3659849","DOIUrl":"10.1109/TCCN.2026.3659849","url":null,"abstract":"Object detection is a fundamental task in computer vision with broad applications in autonomous transportation driving, smart surveillance, and traffic monitoring. In the context of Computing Power Networks (CPNs), which interconnect cloud, edge, and terminal nodes to support distributed AI services, deploying efficient object detection models under constrained computational resources is a critical challenge, particularly at the edge and terminal layers. This study investigates backbone optimization for the YOLOv11M object detection framework to improve computational efficiency while maintaining detection performance. We propose two lightweight variants, YOLOv11M-MN and YOLOv11M-Shuffle, by replacing the original backbone with MobileNetV3-Small and ShuffleNetV2, respectively. All Edge AI-enabled models share an identical detection head and training pipeline to ensure fair and controlled comparisons. To reflect resource-limited CPN environments, all experiments are conducted under CPU-only settings with staged training budgets. Performance is evaluated using the COCO128 dataset in terms of FLOPs, parameter count, inference latency, and detection accuracy. Experimental results demonstrate that the proposed lightweight backbones significantly reduce computational overhead and inference time, while exhibiting different accuracy–efficiency trade-offs, highlighting their suitability for selective deployment across heterogeneous CPN layers.","PeriodicalId":13069,"journal":{"name":"IEEE Transactions on Cognitive Communications and Networking","volume":"12 ","pages":"5891-5902"},"PeriodicalIF":7.0,"publicationDate":"2026-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146089963","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":"AGI-Inspired Digital Twin Framework for UAV-BS Deployment in Disaster Communication Recovery","authors":"Luyu Qi, Yulei Wu, Shuping Dang, Zhuhui Li, Dimitra Simeonidou","doi":"10.1109/tccn.2026.3658781","DOIUrl":"https://doi.org/10.1109/tccn.2026.3658781","url":null,"abstract":"","PeriodicalId":13069,"journal":{"name":"IEEE Transactions on Cognitive Communications and Networking","volume":"217 1","pages":""},"PeriodicalIF":8.6,"publicationDate":"2026-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146070220","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}
Underwater acoustic networks are evolving from static, manually-configured systems into cognitive, learning-enabled platforms that can perceive, reason, and adapt to harsh ocean dynamics in real-time. Accurate target tracking is a core service of these networks and underpins marine resource exploration, environmental monitoring, and maritime security. Existing reviews or surveys, however, rarely examine underwater acoustic target tracking through the lens of cognitive communications and networking, and often offer a narrow perspective on addressing the paradigm shifts driven by emerging technologies like deep learning. To fill this gap, this work presents a systematic survey of this field and introduces an innovative three-dimensional taxonomy framework based on the three levels of the cognitive underwater acoustic target tracking network: the target layer, the perception layer, and the processing layer. Within this framework, we comprehensively survey the literature over the period 2016-2025, spanning from the theoretical foundations to diverse algorithmic approaches in underwater acoustic target tracking. Particularly, we emphasize the transformative potential and recent advancements of machine learning techniques, including deep learning and reinforcement learning, in enhancing the performance and adaptability of cognitive underwater tracking systems. Finally, this survey concludes by identifying key challenges in the field and proposing future avenues based on emerging technologies such as data desensitization, embodied intelligence, and large models.
{"title":"Cognitive Underwater Acoustic Networking and Target Tracking: A Comprehensive Survey","authors":"Zhong Yang;Zhengqiu Zhu;Yong Zhao;Yonglin Tian;Changjun Fan;Runkang Guo;Wenhao Lu;Jingwei Ge;Bin Chen;Yin Zhang;Guohua Wu;Rui Wang;Guangquan Cheng;Jincai Huang;Zhong Liu;Jun Zhang;Imre J. Rudas","doi":"10.1109/TCCN.2026.3658820","DOIUrl":"10.1109/TCCN.2026.3658820","url":null,"abstract":"Underwater acoustic networks are evolving from static, manually-configured systems into cognitive, learning-enabled platforms that can perceive, reason, and adapt to harsh ocean dynamics in real-time. Accurate target tracking is a core service of these networks and underpins marine resource exploration, environmental monitoring, and maritime security. Existing reviews or surveys, however, rarely examine underwater acoustic target tracking through the lens of cognitive communications and networking, and often offer a narrow perspective on addressing the paradigm shifts driven by emerging technologies like deep learning. To fill this gap, this work presents a systematic survey of this field and introduces an innovative three-dimensional taxonomy framework based on the three levels of the cognitive underwater acoustic target tracking network: the target layer, the perception layer, and the processing layer. Within this framework, we comprehensively survey the literature over the period 2016-2025, spanning from the theoretical foundations to diverse algorithmic approaches in underwater acoustic target tracking. Particularly, we emphasize the transformative potential and recent advancements of machine learning techniques, including deep learning and reinforcement learning, in enhancing the performance and adaptability of cognitive underwater tracking systems. Finally, this survey concludes by identifying key challenges in the field and proposing future avenues based on emerging technologies such as data desensitization, embodied intelligence, and large models.","PeriodicalId":13069,"journal":{"name":"IEEE Transactions on Cognitive Communications and Networking","volume":"12 ","pages":"5913-5936"},"PeriodicalIF":7.0,"publicationDate":"2026-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146070223","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 : 2026-01-28DOI: 10.1109/tccn.2026.3658783
Simone Fiorellino, Claudio Battiloro, Emilio Calvanese Strinati, Paolo Di Lorenzo
{"title":"Frame-Based Zero-Shot Semantic Channel Equalization for AI-Native Communications","authors":"Simone Fiorellino, Claudio Battiloro, Emilio Calvanese Strinati, Paolo Di Lorenzo","doi":"10.1109/tccn.2026.3658783","DOIUrl":"https://doi.org/10.1109/tccn.2026.3658783","url":null,"abstract":"","PeriodicalId":13069,"journal":{"name":"IEEE Transactions on Cognitive Communications and Networking","volume":"117 1","pages":""},"PeriodicalIF":8.6,"publicationDate":"2026-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146070226","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 : 2026-01-28DOI: 10.1109/tccn.2026.3658772
Jie Zhang, Yuanyuan He, Xianjun Deng, Xinwei Yu, Shenghao Liu, En Wang
{"title":"SuperFL: Bridging LDP with Byzantine Robustness in Federated Learning on Non-IID Data for Low-Altitude Networks","authors":"Jie Zhang, Yuanyuan He, Xianjun Deng, Xinwei Yu, Shenghao Liu, En Wang","doi":"10.1109/tccn.2026.3658772","DOIUrl":"https://doi.org/10.1109/tccn.2026.3658772","url":null,"abstract":"","PeriodicalId":13069,"journal":{"name":"IEEE Transactions on Cognitive Communications and Networking","volume":"1 1","pages":""},"PeriodicalIF":8.6,"publicationDate":"2026-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146070283","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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