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, Tong Li, Chungang Yang, Qiao Li, Pu Wang, Sai Zou, Bodong Shang, Shoufeng Wang, Sule Wang
{"title":"LAMIO-6G: Large AI Model-Empowered Cross-Layer Intent Management and Multi-Domain Policy Orchestration in 6G Terrestrial Networks","authors":"Yao Wang, Tong Li, Chungang Yang, Qiao Li, Pu Wang, Sai Zou, Bodong Shang, Shoufeng Wang, Sule Wang","doi":"10.1109/tccn.2026.3658750","DOIUrl":"https://doi.org/10.1109/tccn.2026.3658750","url":null,"abstract":"","PeriodicalId":13069,"journal":{"name":"IEEE Transactions on Cognitive Communications and Networking","volume":"40 1","pages":""},"PeriodicalIF":8.6,"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}
{"title":"Edge General Intelligence Through World Models, Large Language Models, and Agentic AI: Fundamentals, Solutions, and Challenges","authors":"Changyuan Zhao, Guangyuan Liu, Ruichen Zhang, Yinqiu Liu, Jiacheng Wang, Jiawen Kang, Dusit Niyato, Zan Li, Xuemin Shen, Zhu Han, Sumei Sun, Chau Yuen, Dong In Kim","doi":"10.1109/tccn.2026.3658762","DOIUrl":"https://doi.org/10.1109/tccn.2026.3658762","url":null,"abstract":"","PeriodicalId":13069,"journal":{"name":"IEEE Transactions on Cognitive Communications and Networking","volume":"42 1","pages":""},"PeriodicalIF":8.6,"publicationDate":"2026-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146070222","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.2026.3658760
Kai Zhong;Jinfeng Hu;Dongxu An;Huan Wan;Yiran Zhang;Xin Tai;Yongfeng Zuo;Ye Yuan;Cunhua Pan;Kah Chan Teh;Xianxiang Yu;Huiyong Li;Guolong Cui
Unimodular waveform design is a key technology in cognitive Multiple Input Multiple Output (MIMO) radar systems. Existing research mainly includes two categories: unimodular continuous/binary waveform design for detection, and unimodular continuous waveform design for Direction of Arrival (DOA) estimation without spectral constraints. Different from existing methods, our focus lies in investigating unimodular binary waveform design for DOA estimation within spectrally crowded environments. This problem is formulated as minimizing the mean square error (MSE) for DOA estimation, subject to the constraints of binary waveform and multiple spectral constraints. Due to the spectral constraint and nonconvex nature of the binary waveform constraint, the problem is NP-hard and challenging to solve directly. Fortunately, we observe that the problem can be decomposed into multiple more tractable subproblems by introducing auxiliary variables. Leveraging this characteristic, we propose an efficient Problem Decomposition-based Sequential Optimization (PDSO) method to tackle this problem. The method introduces two auxiliary variables to decompose the problem into two subproblems: one of which can be solved in closed-form, while the other is efficiently addressed by the Binary Alternating Directions Method of Multipliers (B-ADMM) algorithm. Compared to the existing methods, the proposed approach demonstrates superior performance in terms of computational cost, DOA resolution, and suppression of spectral interference.
{"title":"Binary Waveform Design for Spectrally-Compatible Cognitive MIMO Radar DOA Estimation","authors":"Kai Zhong;Jinfeng Hu;Dongxu An;Huan Wan;Yiran Zhang;Xin Tai;Yongfeng Zuo;Ye Yuan;Cunhua Pan;Kah Chan Teh;Xianxiang Yu;Huiyong Li;Guolong Cui","doi":"10.1109/TCCN.2026.3658760","DOIUrl":"10.1109/TCCN.2026.3658760","url":null,"abstract":"Unimodular waveform design is a key technology in cognitive Multiple Input Multiple Output (MIMO) radar systems. Existing research mainly includes two categories: unimodular continuous/binary waveform design for detection, and unimodular continuous waveform design for Direction of Arrival (DOA) estimation without spectral constraints. Different from existing methods, our focus lies in investigating unimodular binary waveform design for DOA estimation within spectrally crowded environments. This problem is formulated as minimizing the mean square error (MSE) for DOA estimation, subject to the constraints of binary waveform and multiple spectral constraints. Due to the spectral constraint and nonconvex nature of the binary waveform constraint, the problem is NP-hard and challenging to solve directly. Fortunately, we observe that the problem can be decomposed into multiple more tractable subproblems by introducing auxiliary variables. Leveraging this characteristic, we propose an efficient Problem Decomposition-based Sequential Optimization (PDSO) method to tackle this problem. The method introduces two auxiliary variables to decompose the problem into two subproblems: one of which can be solved in closed-form, while the other is efficiently addressed by the Binary Alternating Directions Method of Multipliers (B-ADMM) algorithm. Compared to the existing methods, the proposed approach demonstrates superior performance in terms of computational cost, DOA resolution, and suppression of spectral interference.","PeriodicalId":13069,"journal":{"name":"IEEE Transactions on Cognitive Communications and Networking","volume":"12 ","pages":"5937-5952"},"PeriodicalIF":7.0,"publicationDate":"2026-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146070256","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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