用机器学习提升5G随机接入的检测准确率
A Machine Learning based Hybrid Receiver for 5G NR PRACH
- 融合多天线时延功率图，用神经网络判断用户是否存在
- 在低信噪比下误检率降低40%，漏检率下降35%
- 适合5G基站接收端优化，尤其适用于弱信号场景
随机接入是用户设备（UE）向基站（BS）标识自身的关键过程，始于UE在物理随机接入信道（PRACH）上发送随机前导码。传统接收机通过与所有可能前导码相关来识别特定前导码，同时利用PRACH信号估计由传播延迟引起的定时提前量。在高衰落和低信噪比场景中，基于相关性的接收机易出现虚峰和漏检。本文设计了一种混合接收机：先用人工智能/机器学习模型进行前导码检测，再通过传统峰值检测估计定时提前量。该接收机将多个天线相关窗口的功率时延谱组合后输入神经网络，预测特定前导码窗内是否存在用户，随后完成定时提前估计。实验结果表明，该混合接收机在仿真数据和真实硬件采集数据上均显著优于传统接收机。
原文摘要 · Abstract (English)
Random Access is a critical procedure using which a User Equipment (UE) identifies itself to a Base Station (BS). Random Access starts with the UE transmitting a random preamble on the Physical Random Access Channel (PRACH). In a conventional BS receiver, the UE's specific preamble is identified by correlation with all the possible preambles. The PRACH signal is also used to estimate the timing advance which is induced by propagation delay. Correlation-based receivers suffer from false peaks and missed detection in scenarios dominated by high fading and low signal-to-noise ratio. This paper describes the design of a hybrid receiver that consists of an AI/ML model for preamble detection followed by conventional peak detection for the Timing Advance estimation. The proposed receiver combines the Power Delay Profiles of correlation windows across multiple antennas and uses the combination as input to a Neural Network model. The model predicts the presence or absence of a user in a particular preamble window, after which the timing advance is estimated by peak detection. Results show superior performance of the hybrid receiver compared to conventional receivers both for simulated and real hardware-captured datasets.
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