6G毫米波航空网络视觉与管理的AI融合

IF 3.6 3区 计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS
Mohammad Shah Alamgir;Brian Kelley
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引用次数: 0

摘要

本文旨在通过融合实时视觉数据和智能波束选择,在动态城市环境中增强6G空中通信的波束管理,在城市环境中,建筑物和移动引起的阻塞经常阻碍视线(LoS)连接。我们提出了一种人工智能驱动的视觉和波束选择融合(AI-FVBS)框架,该框架集成了基于yolov8的目标检测、可重构智能表面(RIS)和上下文多武装匪(CMAB)学习,以优化高空平台(HAPS)和无人驾驶飞行器(uav)等空中平台的波束选择。空中平台提高了6G无线网络吞吐量,减少了阻塞,提高了LoS传输。与传统的基于通道状态信息(CSI)的方法不同,该系统利用实时视觉环境来动态平衡梁的探索和开发,增强了在密集的城市环境中频繁阻塞的鲁棒性。视觉辅助RIS在失去直接能见度的情况下进一步扩展了通信范围。安装在建筑物上的视觉辅助RIS通过智能反射信号来增强信号的传播和质量,并在空中平台无法直接检测移动物联网系统时辅助波束选择。使用航空数据集(包括DOTA)进行的实验验证表明,车辆检测精度为89%,HAPS的频谱效率为28 bps/Hz,无人机的频谱效率为35 bps/Hz。这些研究结果证实,与传统的依赖csi的方案相比,AI-FVBS在吞吐量和定位精度方面有了显著提高,从而使其成为6G空中网络的高效、可扩展的解决方案。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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.
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来源期刊
IEEE Access
IEEE Access COMPUTER 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.
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