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}
Pub Date : 2026-01-28DOI: 10.1109/tccn.2026.3658758
Jiao Chen, Haoyi Wang, Jianhua Tang, Junyi Wang
{"title":"AdaptFly: Prompt-Guided Adaptation of Foundation Models for Low-Altitude UAV Networks","authors":"Jiao Chen, Haoyi Wang, Jianhua Tang, Junyi Wang","doi":"10.1109/tccn.2026.3658758","DOIUrl":"https://doi.org/10.1109/tccn.2026.3658758","url":null,"abstract":"","PeriodicalId":13069,"journal":{"name":"IEEE Transactions on Cognitive Communications and Networking","volume":"75 1","pages":""},"PeriodicalIF":8.6,"publicationDate":"2026-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146070224","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.2026.3658750
Yao Wang;Chungang Yang;Qiao Li;Pu Wang;Sai Zou;Bodong Shang;Shoufeng Wang
As differentiated services emerge, intent-driven management and orchestration in the 6G terrestrial networks will face two key challenges. On the one hand, the intent mapping gap, limited orchestration flexibility, and lack of policy abstraction constrain intent refinement across the Business Support System, Operation Support System, and Network Operation Provider layers. On the other hand, multi-domain orchestration among the radio access, transport, and core networks remains hard due to limited global awareness, resource conflicts, and inconsistent policy models. In this paper, we present LAMIO-6G, a large AI model-empowered framework for cross-layer intent management and multi-domain policy orchestration, which autonomously generates policies at different levels of abstraction from intents. The LAMIO-6G incorporates two key models: ($i$ ) a unified network policy model and (ii) a monitor-analyze-plan-execute-knowledge feedback closed-loop model. To address limited intent generality and scenario generalization in the Business Support System layer, we then introduce intent decomposition techniques via generic large AI models using low-rank adaptation-based fine-tuning, design of intent decomposition prompts, and few-shot learning-assisted intent decomposition. Within the Operation Support System layer, we design an intent reasoning and optimization scheme through collaboration between domain-specific large AI models guided by long-short chain-of-thought techniques and lightweight proximal policy optimization model. Finally, we present a proof-of-concept implementation of a wireless energy-saving intent. Simulation results demonstrate that the DeepSeek-R1-14B model achieves 17% to 30% gains over all baseline schemes in fine-tuned metrics, intent decomposition and reasoning accuracy, and intent optimization performance.
{"title":"LAMIO-6G: Large AI Model-Empowered Cross-Layer Intent Management and Multi-Domain Policy Orchestration in 6G Terrestrial Networks","authors":"Yao Wang;Chungang Yang;Qiao Li;Pu Wang;Sai Zou;Bodong Shang;Shoufeng Wang","doi":"10.1109/TCCN.2026.3658750","DOIUrl":"10.1109/TCCN.2026.3658750","url":null,"abstract":"As differentiated services emerge, intent-driven management and orchestration in the 6G terrestrial networks will face two key challenges. On the one hand, the intent mapping gap, limited orchestration flexibility, and lack of policy abstraction constrain intent refinement across the Business Support System, Operation Support System, and Network Operation Provider layers. On the other hand, multi-domain orchestration among the radio access, transport, and core networks remains hard due to limited global awareness, resource conflicts, and inconsistent policy models. In this paper, we present LAMIO-6G, a large AI model-empowered framework for cross-layer intent management and multi-domain policy orchestration, which autonomously generates policies at different levels of abstraction from intents. The LAMIO-6G incorporates two key models: (<inline-formula> <tex-math>$i$ </tex-math></inline-formula>) a unified network policy model and (ii) a monitor-analyze-plan-execute-knowledge feedback closed-loop model. To address limited intent generality and scenario generalization in the Business Support System layer, we then introduce intent decomposition techniques via generic large AI models using low-rank adaptation-based fine-tuning, design of intent decomposition prompts, and few-shot learning-assisted intent decomposition. Within the Operation Support System layer, we design an intent reasoning and optimization scheme through collaboration between domain-specific large AI models guided by long-short chain-of-thought techniques and lightweight proximal policy optimization model. Finally, we present a proof-of-concept implementation of a wireless energy-saving intent. Simulation results demonstrate that the DeepSeek-R1-14B model achieves 17% to 30% gains over all baseline schemes in fine-tuned metrics, intent decomposition and reasoning accuracy, and intent optimization performance.","PeriodicalId":13069,"journal":{"name":"IEEE Transactions on Cognitive Communications and Networking","volume":"12 ","pages":"6221-6236"},"PeriodicalIF":7.0,"publicationDate":"2026-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146070281","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}
Integrated sensing and communications (ISAC), recognized as a key technology of the sixth-generation (6G) communication system, simultaneously attends to dual functionalities of communication and sensing. This paper introduces a reconfigurable intelligent surface (RIS)-assisted two-stage ISAC system. The system utilizes uplink pilots to achieve sensing-assisted communication and implements predictive optimal beamforming to maximize the multi-slot system sum-rate. Conventional beamforming algorithms predominantly rely on perfect or estimated channel state information (CSI), which is idealistic or requires a large pilot overhead, making it unaffordable in mobile user scenarios. To address this challenge, this paper proposes a fusion framework named TD3-SATGCN, which integrates deep reinforcement learning (DRL) with an attention-based spatial-temporal graph convolution network (SATGCN) for non-convex joint beamforming. The proposed framework implicitly captures the spatial features from sensed user trajectories and pilots, which are further mapped into beamforming solutions in multi-slots without explicit CSI estimation. Furthermore, the poor generalization of artificial intelligence (AI)-based algorithms has hindered their deployment in communication systems. This article converts communication systems into graph topologies, harnessing the permutation/equivariance properties of GCNs to enhance generalizability. Simulation results under various scenarios indicate that the TD3-SATGCN reduces pilot overhead by 25% and achieves up to a 13.52% improvement in the system sum-rate compared to benchmarks without CSI.
{"title":"Attention-Based Spatial-Temporal GCN for Sensing-Aided Beam Prediction in RIS-Assisted ISAC Systems","authors":"Jianzheng Li;Weijiang Wang;Rongkun Jiang;Xinyi Wang;Zesong Fei;Shiwei Ren","doi":"10.1109/TCCN.2026.3658751","DOIUrl":"10.1109/TCCN.2026.3658751","url":null,"abstract":"Integrated sensing and communications (ISAC), recognized as a key technology of the sixth-generation (6G) communication system, simultaneously attends to dual functionalities of communication and sensing. This paper introduces a reconfigurable intelligent surface (RIS)-assisted two-stage ISAC system. The system utilizes uplink pilots to achieve sensing-assisted communication and implements predictive optimal beamforming to maximize the multi-slot system sum-rate. Conventional beamforming algorithms predominantly rely on perfect or estimated channel state information (CSI), which is idealistic or requires a large pilot overhead, making it unaffordable in mobile user scenarios. To address this challenge, this paper proposes a fusion framework named TD3-SATGCN, which integrates deep reinforcement learning (DRL) with an attention-based spatial-temporal graph convolution network (SATGCN) for non-convex joint beamforming. The proposed framework implicitly captures the spatial features from sensed user trajectories and pilots, which are further mapped into beamforming solutions in multi-slots without explicit CSI estimation. Furthermore, the poor generalization of artificial intelligence (AI)-based algorithms has hindered their deployment in communication systems. This article converts communication systems into graph topologies, harnessing the permutation/equivariance properties of GCNs to enhance generalizability. Simulation results under various scenarios indicate that the TD3-SATGCN reduces pilot overhead by 25% and achieves up to a 13.52% improvement in the system sum-rate compared to benchmarks without CSI.","PeriodicalId":13069,"journal":{"name":"IEEE Transactions on Cognitive Communications and Networking","volume":"12 ","pages":"6119-6134"},"PeriodicalIF":7.0,"publicationDate":"2026-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146070221","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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