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arXiv 2510.23908eess.SPcs.ET
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RIS辅助无线系统中机器学习驱动的用户定位

Machine Learning-Driven User Localization in RIS-Assisted Wireless Systems

M. T. Hassan, D. Zelenchuk, M. A. B. Abbasi

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AI总结：

针对RIS辅助6G毫米波系统波束搜索空间大、全向扫描效率低的问题，提出基于机器学习的用户定位框架，通过多回归模型利用分扇区接收功率实时预测用户角度位置，DT模型精度最高，可减少波束探测、加快对齐、降低时延。

摘要：

第六代移动通信（6G）以在毫米波频段实现超可靠低时延（URLLC）的吉比特级连接为目标，该频段的定向信道需要精确的波束对齐。可重构智能表面（RIS）能够重塑电波传播并扩展覆盖范围，但它也扩大了基站的波束搜索空间，导致全向扫描因控制开销和时延问题效率低下。我们提出了一种适用于27 GHz频段RIS辅助通信的、基于机器学习（ML）的用户定位框架。一块20×20的RIS反射来自连接核心网的基站的信号，并在0-90度的仰角平面内扫描波束，该平面被划分为四个角度扇区。我们通过记录不同用户位置下的接收信号功率（Pr，单位为dBm）构建了数据集，并训练了多种回归模型，包括决策树（DT）、支持向量回归器（SVR）、k近邻（KNN）、XGBoost、梯度提升和随机森林。在运行阶段，同一平面内的未知用户测量四个接收功率值（每个扇区一个）并将其上报给预训练的RIS控制器，控制器会实时预测用户的角度位置。使用平均绝对误差（MAE）、均方根误差（RMSE）和决定系数（R²）进行的评估显示该框架具有高精度。DT模型的MAE为4.8度，R²=0.96，而其他模型的R²在70%到86%之间。预测的方向图（包括52至55度之间的主瓣对齐情况）与真实值高度吻合。该框架减少了波束探测次数，实现了更快的对齐，并降低了RIS辅助6G网络的时延。

英文摘要：

The sixth generation (6G) targets ultra reliable, low latency (URLLC) gigabit connectivity in mmWave bands, where directional channels require precise beam alignment. Reconfigurable intelligent surfaces (RIS) reshape wave propagation and extend coverage, but they enlarge the beam search space at the base station, making exhaustive sweeps inefficient due to control overhead and latency. We propose an ML based user localization framework for RIS assisted communication at 27 GHz. A 20x20 RIS reflects signals from a core network connected base station and sweeps beams across the 0-90 degree elevation plane, divided into four angular sectors. We build a dataset by recording received signal power (Pr in dBm) across user locations and train multiple regressors, including decision tree (DT), support vector regressor (SVR), k nearest neighbor (KNN), XGBoost, gradient boosting, and random forest. In operation, an unknown user in the same plane measures four received power values (one per sector) and reports them to the pretrained RIS controller, which predicts the user's angular position in real time. Evaluation using mean absolute error (MAE), root mean squared error (RMSE), and R squared (R2) shows high accuracy. The DT model achieves an MAE of 4.8 degrees with R2 = 0.96, while other models reach 70 to 86 percent. Predicted radiation patterns, including main lobe alignment between 52 and 55 degrees, closely track ground truth. The framework reduces beam probing, enables faster alignment, and lowers latency for RIS assisted 6G networks.

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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