论文标题
通过直接计算和RIS辅助MMWave系统的深度学习和深度学习的渠道估计
Channel Estimation via Direct Calculation and Deep Learning for RIS-Aided mmWave Systems
论文作者
论文摘要
本文提出了一种新颖的可重构智能表面（RIS）结构，可实现RIS辅助毫米波（MMWave）系统的通道估计。更具体地说，提出了两种通道估计方法，即直接计算（DC）和深度学习方法（DL）方法，以熟练地将整个通道估计转换为两个任务：通道估计和少量活动元素的角度参数估计。特别是，直接计算方法通过相邻活动元件的通道估计值直接计算角度参数，并且基于IT，DL方法降低了角度偏移率并进一步提高了角度参数估计的准确性。与传统方法相比，提出的方案降低了RIS通道估计的复杂性，同时就最小的正方形误差，可实现的速率和中断概率优于梁训练方法。
This paper proposes a novel reconfigurable intelligent surface (RIS) architecture which enables channel estimation of RIS-assisted millimeter wave (mmWave) systems. More specifically, two channel estimation methods, namely, direct calculation (DC) and deep learning (DL) methods, are proposed to skillfully convert the overall channel estimation into two tasks: the channel estimation and the angle parameter estimation of a small number of active elements. In particular, the direct calculation method calculates the angle parameters directly through the channel estimates of adjacent active elements and, based on it, the DL method reduces the angle offset rate and further improves the accuracy of angle parameter estimation. Compared with the traditional methods, the proposed schemes reduce the complexity of the RIS channel estimation while outperforming the beam training method in terms of minimum square error, achievable rate, and outage probability.
