面向RIS-mmWave系统的机器学习辅助高效波束训练及信道估计方法

面向B5G及6G无线通信系统的高速无线信息传输，本文研究了智能超表面辅助毫米波（RIS-mmWave）系统的高效能波束训练及信道估计方法。特别地，基于RIS-mmWave波束管理及有效信道获取的内生关联性，本文创新性地提出一种机器学习辅助的RIS-mmWave系统高效波束训练及信道估计方法。具体而言，在第一阶段，设计了一种新颖的半监督学习模型，实现位置信息辅助的在线快速波束训练，并且免估计地直接获取粗略角度域信息以驱动精细化信道估计；在第二阶段中，提出半监督学习辅助的压缩感知级联信道估计算法，利用半监督学习模型直接输出的粗略角度域信息驱动块正交匹配追踪算法进行信道估计。仿真结果表明，所提波束训练及信道估计方法在系统开销和信道估计误差等方面的性能均优于代表性参考方案。

Machine Learning Assisted Efficient Beam Training and Channel Estimation for RIS-mmWave Communications

? ?In this paper， we study high-efficiency beam training and channel estimation for the reconfigurable intelligent surface-assisted millimeter-wave （RIS-mmWave） system， which is the key to achieving ultra-high wireless information transmissions in B5G and 6G wireless networks. In particular， noting the intrinsic connection between RIS-mmWave beam management and effective channel acquisition， a novel machine learning-assisted efficient beam training and channel estimation method is proposed for RIS-mmWave systems. Specifically， in the first stage， a novel semi-supervised learning model is designed to achieve fast online beam training by exploiting users' location information， which directly obtains coarse angular domain information. In the second stage， a semi-supervised learning-assisted compressed sensing is proposed to estimate the RIS-dependent cascade channel， which uses the coarse angular domain information predicted by the learning model to drive the block orthogonal matching pursuit algorithm for channel estimation. Simulation results show that the performance of the proposed beam training and channel estimation method outperforms the reference scheme in terms of system overhead and channel estimation error.