这篇论文用KNN做RIS定位，无干扰时角度误差才0.37度，有干扰也能保持在1.4度，适合搞6G定位的人参考。
本文提出一种基于波束域机器学习的指纹定位框架，用于RIS辅助毫米波6G网络中的用户设备定位，无需信道状态信息。该方法将预设RIS反射状态下的SNR映射为用户方位角和距离，并扩展至存在跨链路干扰的SINR场景。28GHz仿真中，20x20 RIS下KNN算法在无干扰时角度MAE为0.37度、距离MAE为4厘米；有干扰时分别升至1.4度和7.6厘米。关键发现是干扰对角度估计的影响显著大于距离估计。
RIS-Aided mmWave Localization Under Cross-Link Interference via Beam-Domain ML Fingerprinting
Accurate user equipment (UE) localization is critical for beam management in reconfigurable intelligent surface (RIS)-assisted millimeter-wave (mmWave) based sixth-generation (6G) networks, especially if the direct base-station-UE links are unavailable. This paper proposes a beam-domain fingerprint framework that maps the received signal-to-noise ratio (SNR) across a small set of predefined RIS reflection states to the UE azimuth angle and range, without requiring channel state information (CSI). Crucially, we extend the framework to a realistic interference-impaired scenario in which a nearby cross-link interferer (CLI) corrupts the clean SNR fingerprint, yielding a signal-to-interference-plus-noise ratio (SINR) fingerprint; an interference-to-noise ratio (INR)-constrained calibration strategy keeps the interference level physically interpretable. Four machine-learning (ML) regressors are evaluated under both conditions. Simulation results at 28 GHz with a 20x20 RIS show that k-nearest neighbors (KNN) achieves the lowest angle MAE of 0.37 degrees and range MAE of 4 cm under clean conditions, rising to 1.4 degrees and 7.6 cm under interference. A key finding is that interference degrades angle estimation substantially more than range estimation across all models, a consequence of the asymmetric encoding of location information in the beam-domain fingerprint.