Pub Date : 2026-01-23DOI: 10.1109/lwc.2026.3656740
Aleksey S. Gvozdarev
{"title":"IRS Compensation of Hyper-Rayleigh Fading: How Many Elements Are Needed?","authors":"Aleksey S. Gvozdarev","doi":"10.1109/lwc.2026.3656740","DOIUrl":"https://doi.org/10.1109/lwc.2026.3656740","url":null,"abstract":"","PeriodicalId":13343,"journal":{"name":"IEEE Wireless Communications Letters","volume":"287 1","pages":""},"PeriodicalIF":6.3,"publicationDate":"2026-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146042864","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 : 2026-01-23DOI: 10.1109/LWC.2026.3657598
Ping Cao;Wei Wang;Yiliang Liu;Zou Su
Distributed spectrum allocation for large-scale UAV swarm remains a challenging issue, due to spectrum allocation collisions and the high communication overhead required to reach consensus. To address these challenges, we propose a lightweight consensus protocol for distributed collision-free spectrum allocation (LCCFSA), where UAV nodes in the swarm form a blockchain and spectrum allocation consensus is reached on the chain. Specifically, a fast low-complexity allocation scheme is developed for each UAV based on an interference graph, where each UAV adaptively adjusts its occupancy area to avoid mutual interference. To further reduce the consensus overhead, we design a lightweight consensus protocol with a transaction-based blockchain ledger and provide a formal security analysis of the proposed protocol. A prototype is built to validate the feasibility of the proposed scheme. Simulation results show that the average consensus latency can be reduced by more than 20% in scenarios with 100 consensus nodes.
{"title":"A Lightweight Consensus Protocol for Distributed Collision-Free Spectrum Allocation","authors":"Ping Cao;Wei Wang;Yiliang Liu;Zou Su","doi":"10.1109/LWC.2026.3657598","DOIUrl":"10.1109/LWC.2026.3657598","url":null,"abstract":"Distributed spectrum allocation for large-scale UAV swarm remains a challenging issue, due to spectrum allocation collisions and the high communication overhead required to reach consensus. To address these challenges, we propose a lightweight consensus protocol for distributed collision-free spectrum allocation (LCCFSA), where UAV nodes in the swarm form a blockchain and spectrum allocation consensus is reached on the chain. Specifically, a fast low-complexity allocation scheme is developed for each UAV based on an interference graph, where each UAV adaptively adjusts its occupancy area to avoid mutual interference. To further reduce the consensus overhead, we design a lightweight consensus protocol with a transaction-based blockchain ledger and provide a formal security analysis of the proposed protocol. A prototype is built to validate the feasibility of the proposed scheme. Simulation results show that the average consensus latency can be reduced by more than 20% in scenarios with 100 consensus nodes.","PeriodicalId":13343,"journal":{"name":"IEEE Wireless Communications Letters","volume":"15 ","pages":"1553-1557"},"PeriodicalIF":5.5,"publicationDate":"2026-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146042870","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 : 2026-01-23DOI: 10.1109/LWC.2026.3656548
Peng Wu;Xiaopeng Yuan;Yulin Hu;Anke Schmeink
In this letter, we investigate the timeliness of an unmanned aerial vehicle (UAV)-enabled covert and secure communication network, where a novel metric, i.e., covert and secure age of information (C&S AoI), is proposed to capture the relationship between the information freshness and communication security. We first analyze the covert and secure communication performance on account of location uncertainty of warden, which leads to characterization on the expression of C&S AoI. Then, we formulate a joint UAV position, transmit power and blocklength design problem to minimize the C&S AoI. To solve the complicated problem, an efficient iterative algorithm based on optimal solution analysis and a novel convex approximation is developed for obtaining a high-quality solution. Simulations demonstrate the superior performance of proposed algorithm in improving C&S AoI performance of delay sensitive networks compared with benchmark.
{"title":"Age of Information for UAV-Enabled Covert and Secure Communication","authors":"Peng Wu;Xiaopeng Yuan;Yulin Hu;Anke Schmeink","doi":"10.1109/LWC.2026.3656548","DOIUrl":"10.1109/LWC.2026.3656548","url":null,"abstract":"In this letter, we investigate the timeliness of an unmanned aerial vehicle (UAV)-enabled covert and secure communication network, where a novel metric, i.e., covert and secure age of information (C&S AoI), is proposed to capture the relationship between the information freshness and communication security. We first analyze the covert and secure communication performance on account of location uncertainty of warden, which leads to characterization on the expression of C&S AoI. Then, we formulate a joint UAV position, transmit power and blocklength design problem to minimize the C&S AoI. To solve the complicated problem, an efficient iterative algorithm based on optimal solution analysis and a novel convex approximation is developed for obtaining a high-quality solution. Simulations demonstrate the superior performance of proposed algorithm in improving C&S AoI performance of delay sensitive networks compared with benchmark.","PeriodicalId":13343,"journal":{"name":"IEEE Wireless Communications Letters","volume":"15 ","pages":"1524-1528"},"PeriodicalIF":5.5,"publicationDate":"2026-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146042863","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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/LWC.2026.3656210
Mingqi Han;Xinghua Sun;Huadong Li;Chenyuan Feng;Xijun Wang;Qiaofeng Xue;Tony Q. S. Quek
The exponential growth of heterogeneous mobile applications has raised challenges in current Radio Access Networks (RAN). While the network slicing technique offers virtualized networks with independent resources to satisfy diverse Quality of Service (QoS) requirements, persistent challenges remain in dynamic resource allocation and intent-driven slice configuration. For resource allocation, conventional Deep Reinforcement Learning (DRL)-based approaches encounter challenges in addressing the high-dimensional state-action space in large-scale networks with massive Base Stations (BS) and users. For slice configuration, current static intent-driven slice management frameworks suffer from inflexible architectures that fail to adaptively optimize network slicing configurations. In this letter, we propose a multi-Large Language Model (LLM) cooperation-based Joint Network Slicing and Resource Allocation (JNSRA) algorithm to jointly optimize the configurations of intent-driven network slices and resource allocation in large-scale multi-BS networks. In JNSRA, we propose to train Agent LLM through LLM alignment, which can enhance cooperation among slices and BS by regarding joint resource allocation actions as sequences. Moreover, we propose a multi-LLM joint optimization framework to jointly optimize Agent, leading to enhanced network slicing and resource allocation. Simulation results illustrate that JNSRA outperforms other DRL and heuristic approaches, and the proposed multi-LLM collaboration can further enhance the reward, satisfaction ratio and throughput.
{"title":"Multi-LLM Cooperation-Based Joint Intent-Driven Network Slicing and Resource Allocation","authors":"Mingqi Han;Xinghua Sun;Huadong Li;Chenyuan Feng;Xijun Wang;Qiaofeng Xue;Tony Q. S. Quek","doi":"10.1109/LWC.2026.3656210","DOIUrl":"10.1109/LWC.2026.3656210","url":null,"abstract":"The exponential growth of heterogeneous mobile applications has raised challenges in current Radio Access Networks (RAN). While the network slicing technique offers virtualized networks with independent resources to satisfy diverse Quality of Service (QoS) requirements, persistent challenges remain in dynamic resource allocation and intent-driven slice configuration. For resource allocation, conventional Deep Reinforcement Learning (DRL)-based approaches encounter challenges in addressing the high-dimensional state-action space in large-scale networks with massive Base Stations (BS) and users. For slice configuration, current static intent-driven slice management frameworks suffer from inflexible architectures that fail to adaptively optimize network slicing configurations. In this letter, we propose a multi-Large Language Model (LLM) cooperation-based Joint Network Slicing and Resource Allocation (JNSRA) algorithm to jointly optimize the configurations of intent-driven network slices and resource allocation in large-scale multi-BS networks. In JNSRA, we propose to train Agent LLM through LLM alignment, which can enhance cooperation among slices and BS by regarding joint resource allocation actions as sequences. Moreover, we propose a multi-LLM joint optimization framework to jointly optimize Agent, leading to enhanced network slicing and resource allocation. Simulation results illustrate that JNSRA outperforms other DRL and heuristic approaches, and the proposed multi-LLM collaboration can further enhance the reward, satisfaction ratio and throughput.","PeriodicalId":13343,"journal":{"name":"IEEE Wireless Communications Letters","volume":"15 ","pages":"1568-1572"},"PeriodicalIF":5.5,"publicationDate":"2026-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146042890","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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/lwc.2026.3656481
Alexander Vavoulas, Konstantinos K. Delibasis, Harilaos G. Sandalidis, George Nousias, Nicholas Vaiopoulos
{"title":"Efficient UAV Coverage in Large Convex Quadrilateral Areas With Elliptical Footprints","authors":"Alexander Vavoulas, Konstantinos K. Delibasis, Harilaos G. Sandalidis, George Nousias, Nicholas Vaiopoulos","doi":"10.1109/lwc.2026.3656481","DOIUrl":"https://doi.org/10.1109/lwc.2026.3656481","url":null,"abstract":"","PeriodicalId":13343,"journal":{"name":"IEEE Wireless Communications Letters","volume":"13 1","pages":""},"PeriodicalIF":6.3,"publicationDate":"2026-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146042884","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：