{"title":"6G毫米波航空网络视觉与管理的AI融合","authors":"Mohammad Shah Alamgir;Brian Kelley","doi":"10.1109/ACCESS.2025.3604288","DOIUrl":null,"url":null,"abstract":"This paper aims to enhance beam management for 6G aerial communications by fusing real-time vision data with intelligent beam selection in dynamic urban environments where buildings and mobility-induced blockages frequently obstruct line-of-sight (LoS) connectivity. We propose an artificial intelligence-driven fusion of vision and beam selection (AI-FVBS) framework that integrates YOLOv8-based object detection, reconfigurable intelligent surfaces (RIS), and contextual multiarmed bandit (CMAB) learning to optimize beam selection for aerial platforms such as high-altitude platform stations (HAPS) and uncrewed aerial vehicles (UAVs). Aerial platforms improve 6G wireless network throughput, reduces blockages, and improve LoS transmission. Unlike conventional channel state information (CSI)-based methods, the proposed system leverages real-time visual context to dynamically balance beam exploration and exploitation, enhancing robustness in dense urban environments with frequent blockages. Vision-assisted RIS further extends the communication range when direct visibility is lost. Mounted vision-assisted RIS on buildings enhance signal propagation and quality by intelligently reflecting signals, and assist in beam selection when aerial platforms cannot directly detect mobile IoT systems. Experimental validation using aerial datasets, including DOTA, demonstrates a vehicle detection precision of 89%, and spectral efficiencies of 28 bps/Hz for HAPS and 35 bps/Hz for UAVs. These findings confirm significant gains in throughput and localization accuracy over traditional CSI-dependent schemes, establishing AI-FVBS as an efficient and scalable solution for 6G aerial networks.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"152729-152747"},"PeriodicalIF":3.6000,"publicationDate":"2025-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11145068","citationCount":"0","resultStr":"{\"title\":\"AI Fusion of Vision and Management for 6G Millimeter-Wave Aerial Networks\",\"authors\":\"Mohammad Shah Alamgir;Brian Kelley\",\"doi\":\"10.1109/ACCESS.2025.3604288\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper aims to enhance beam management for 6G aerial communications by fusing real-time vision data with intelligent beam selection in dynamic urban environments where buildings and mobility-induced blockages frequently obstruct line-of-sight (LoS) connectivity. We propose an artificial intelligence-driven fusion of vision and beam selection (AI-FVBS) framework that integrates YOLOv8-based object detection, reconfigurable intelligent surfaces (RIS), and contextual multiarmed bandit (CMAB) learning to optimize beam selection for aerial platforms such as high-altitude platform stations (HAPS) and uncrewed aerial vehicles (UAVs). Aerial platforms improve 6G wireless network throughput, reduces blockages, and improve LoS transmission. Unlike conventional channel state information (CSI)-based methods, the proposed system leverages real-time visual context to dynamically balance beam exploration and exploitation, enhancing robustness in dense urban environments with frequent blockages. Vision-assisted RIS further extends the communication range when direct visibility is lost. Mounted vision-assisted RIS on buildings enhance signal propagation and quality by intelligently reflecting signals, and assist in beam selection when aerial platforms cannot directly detect mobile IoT systems. Experimental validation using aerial datasets, including DOTA, demonstrates a vehicle detection precision of 89%, and spectral efficiencies of 28 bps/Hz for HAPS and 35 bps/Hz for UAVs. These findings confirm significant gains in throughput and localization accuracy over traditional CSI-dependent schemes, establishing AI-FVBS as an efficient and scalable solution for 6G aerial networks.\",\"PeriodicalId\":13079,\"journal\":{\"name\":\"IEEE Access\",\"volume\":\"13 \",\"pages\":\"152729-152747\"},\"PeriodicalIF\":3.6000,\"publicationDate\":\"2025-08-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11145068\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Access\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/11145068/\",\"RegionNum\":3,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"COMPUTER SCIENCE, INFORMATION SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Access","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/11145068/","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
AI Fusion of Vision and Management for 6G Millimeter-Wave Aerial Networks
This paper aims to enhance beam management for 6G aerial communications by fusing real-time vision data with intelligent beam selection in dynamic urban environments where buildings and mobility-induced blockages frequently obstruct line-of-sight (LoS) connectivity. We propose an artificial intelligence-driven fusion of vision and beam selection (AI-FVBS) framework that integrates YOLOv8-based object detection, reconfigurable intelligent surfaces (RIS), and contextual multiarmed bandit (CMAB) learning to optimize beam selection for aerial platforms such as high-altitude platform stations (HAPS) and uncrewed aerial vehicles (UAVs). Aerial platforms improve 6G wireless network throughput, reduces blockages, and improve LoS transmission. Unlike conventional channel state information (CSI)-based methods, the proposed system leverages real-time visual context to dynamically balance beam exploration and exploitation, enhancing robustness in dense urban environments with frequent blockages. Vision-assisted RIS further extends the communication range when direct visibility is lost. Mounted vision-assisted RIS on buildings enhance signal propagation and quality by intelligently reflecting signals, and assist in beam selection when aerial platforms cannot directly detect mobile IoT systems. Experimental validation using aerial datasets, including DOTA, demonstrates a vehicle detection precision of 89%, and spectral efficiencies of 28 bps/Hz for HAPS and 35 bps/Hz for UAVs. These findings confirm significant gains in throughput and localization accuracy over traditional CSI-dependent schemes, establishing AI-FVBS as an efficient and scalable solution for 6G aerial networks.
IEEE AccessCOMPUTER SCIENCE, INFORMATION SYSTEMSENGIN-ENGINEERING, ELECTRICAL & ELECTRONIC
CiteScore
9.80
自引率
7.70%
发文量
6673
审稿时长
6 weeks
期刊介绍:
IEEE Access® is a multidisciplinary, open access (OA), applications-oriented, all-electronic archival journal that continuously presents the results of original research or development across all of IEEE''s fields of interest.
IEEE Access will publish articles that are of high interest to readers, original, technically correct, and clearly presented. Supported by author publication charges (APC), its hallmarks are a rapid peer review and publication process with open access to all readers. Unlike IEEE''s traditional Transactions or Journals, reviews are "binary", in that reviewers will either Accept or Reject an article in the form it is submitted in order to achieve rapid turnaround. Especially encouraged are submissions on:
Multidisciplinary topics, or applications-oriented articles and negative results that do not fit within the scope of IEEE''s traditional journals.
Practical articles discussing new experiments or measurement techniques, interesting solutions to engineering.
Development of new or improved fabrication or manufacturing techniques.
Reviews or survey articles of new or evolving fields oriented to assist others in understanding the new area.