基于波束域机器学习指纹的、存在跨链路干扰的智能反射面辅助毫米波定位
RIS-Aided mmWave Localization Under Cross-Link Interference via Beam-Domain ML Fingerprinting
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中文总结 AI 辅助
本文针对RIS辅助毫米波网络的UE定位问题，提出波束域ML指纹框架，结合INR约束校准策略，经仿真验证KNN在干扰场景下仍有良好定位性能，且干扰对角度估计影响更大。
中文摘要 AI 辅助
在可重构智能反射面（RIS）辅助的基于毫米波（mmWave）的第六代（6G）网络中，精准的用户设备（UE）定位对波束管理至关重要，尤其是当基站与UE的直接链路不可用时。本文提出一种波束域指纹框架，该框架将少量预定义RIS反射状态下的接收信噪比（SNR）映射到UE的方位角和距离，无需信道状态信息（CSI）。关键在于，我们将该框架扩展到实际的受干扰场景：附近的跨链路干扰源（CLI）会破坏干净的SNR指纹，产生信干噪比（SINR）指纹；一种受干扰噪声比（INR）约束的校准策略使干扰水平保持物理可解释性。在两种场景下评估了四种机器学习（ML）回归器：28 GHz下、20×20 RIS的仿真结果显示，k近邻（KNN）在干净场景下实现最低的角度平均绝对误差（MAE）0.37度、距离MAE 4厘米，受干扰后分别升至1.4度、7.6厘米。关键发现是，在所有模型中，干扰对角度估计的损害远大于距离估计，这是波束域指纹中位置信息非对称编码的结果。
英文摘要
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.
发表机构
- Rajshahi University of Engineering & Technology (RUET)（拉杰沙希工程技术大学）
机构由 AI 辅助整理，请以论文原文为准。
