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arXiv 2405.08076eess.SPcs.SYeess.SY
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Show Me the Way: 借助支持UWB的RIS实现无线移动用户的实时跟踪

Show Me the Way: Real-Time Tracking of Wireless Mobile Users with UWB-Enabled RIS

Kevin Weinberger, Simon Tewes, Aydin Sezgin

AI总结:

针对RIS在移动用户场景下实时配置难、UWB定位波动导致波束失准的问题,提出波束分裂和UWB估计校正方案,显著提升移动用户跟踪性能。

摘要:

可重构智能表面(Reconfigurable Intelligent Surfaces, RIS)在6G无线网络中的集成,为通信环境提供了前所未有的控制能力。然而,在实际约束下确定最优配置仍是一项重大挑战,当用户处于移动状态、需要实时部署配置时,这一问题尤为突出。\n本研究利用超宽带(Ultra-Wideband, UWB)作为定位技术,在支持RIS的室内环境中捕获并分析用户的实时运动。基于获取的系统几何信息,采用基于模型的优化方法,实现RIS向用户的实时波束导向。但UWB模块的实际局限性会导致UWB估计值波动,使得RIS波束偶尔无法对准被跟踪的用户。本工作提出的方法旨在提升两个系统间的兼容性,为此提供了两项核心解决方案:用于获得更稳健RIS配置的波束分裂技术,以及用于减少UWB数据波动的UWB估计校正技术。\n通过在静止和移动场景下开展全面的理论与实验评估,所提技术的有效性得到验证。两种方法结合使用时,与传统方法相比,最坏情况下的跟踪性能显著提升了17.5dB。

英文摘要:

The integration of Reconfigurable Intelligent Surfaces (RIS) in 6G wireless networks offers unprecedented control over communication environments. However, identifying optimal configurations within practical constraints remains a significant challenge. This becomes especially pronounced, when the user is mobile and the configurations need to be deployed in real time. Leveraging Ultra-Wideband (UWB) as localization technique, we capture and analyze real-time movements of a user within the RIS-enabled indoor environment. Given this information about the system's geometry, a model-based optimization is utilized, which enables real-time beam steering of the RIS towards the user. However, practical limitations of UWB modules lead to fluctuating UWB estimates, causing the RIS beam to occasionally miss the tracked user. The methodologies proposed in this work aim to increase the compatibility between these two systems. To this end, we provide two key solutions: beam splitting for obtaining more robust RIS configurations and UWB estimation correction for reducing the variations in the UWB data. Through comprehensive theoretical and experimental evaluations in both stationary and mobile scenarios, the effectiveness of the proposed techniques is demonstrated. When combined, the proposed methods improve worst-case tracking performance by a significant 17.5dB compared to the conventional approach.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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