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arXiv 2406.07463eess.SP
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动态富散射环境中的可重构智能表面:基于BiLSTM的精准用户定位优化方法

Reconfigurable Intelligent Surfaces in Dynamic Rich Scattering Environments: BiLSTM-Based Optimization for Accurate User Localization

Anum Umer, Ivo Müürsepp, Muhammad Mahtab Alam

AI总结:

针对动态富散射环境下RIS最优配置难确定、定位精度受限的问题,提出基于波动物理仿真训练的biLSTM网络捕捉信道与RIS配置关系的方法,可自适应优化配置以显著提升UE定位精度。

摘要:

将可重构智能表面(RIS)集成到无线环境中可实现信道可编程性与传播信道的动态控制,预计将在第六代(6G)网络中发挥关键作用。大多数RIS相关研究聚焦于更简单的准自由空间条件,这类场景下无线信道通常采用解析建模。但许多实际定位场景发生在具有富散射特性且随时间变化的环境中,这些动态复杂的条件给确定最优RIS配置以最大化定位精度带来了巨大挑战。本文提出了应对这一挑战的方案:引入一种利用双向长短期记忆(biLSTM)网络的新方法,该网络由能精确反映波动物理特性的模拟器训练,用于捕捉动态富散射条件下无线信道与RIS配置之间的关系。我们采用该方法优化RIS配置以提升用户设备(UE)定位性能,定位精度以均方误差(MSE)衡量。通过大量仿真实验验证,我们的方法可自适应调整RIS配置,在这类动态变化的富散射环境中显著提升定位精度。

英文摘要:

The integration of reconfigurable intelligent surfaces (RIS) in wireless environments offers channel programmability and dynamic control over propagation channels, which is expected to play a crucial role in sixth generation (6G) networks. The majority of RIS-related research has focused on simpler, quasi-free-space conditions, where wireless channels are typically modeled analytically. However, many practical localization scenarios unfold in environments characterized by rich scattering that also change over time. These dynamic and complex conditions pose significant challenges in determining the optimal RIS configuration to maximize localization accuracy. In this paper, we present our approach to overcoming this challenge. This paper introduces a novel approach that leverages a bidirectional long-short term memory (biLSTM) network, trained with a simulator that accurately reflects wave physics, to capture the relationship between wireless channels and the RIS configuration under dynamic, rich-scattering conditions. We use this approach to optimize RIS configurations for enhanced user equipment (UE) localization, measured by mean squared error (MSE). Through extensive simulations, we demonstrate that our approach adapts RIS configurations to significantly improve localization accuracy in such dynamically changing rich scattering environments.

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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