Distributed Machine Learning Based Downlink Channel Estimation for RIS Assisted Wireless Communications

The downlink channel estimation requires a huge pilot overhead in the reconfigurable intelligent surface (RIS) assisted communication system. By exploiting the powerful learning ability of the neural network, the machine learning (ML) technique can be used to estimate the high-dimensional channel from a few received pilot signals at the user. However, since the training dataset collected by the single user only contains the information of part of the channel scenarios of a cell, the neural network trained by the single user is not able to work when the user moves from one channel scenario to another. To solve this challenge, we propose to leverage the distributed machine learning (DML) technique to enable the reliable downlink channel estimation. Specifically, we firstly build a downlink channel estimation neural network shared by all users, which can be collaboratively trained by the BS and the users with the help of the DML technique. Then, we further propose a hierarchical neural network architecture to improve the channel estimation accuracy, which can extract different channel features for different channel scenarios. Simulation results show that compared with the neural network trained by the single user, the proposed DML based neural networks can achieve better estimation performance with the reduced pilot overhead for all users from different scenarios.

I. Introduction

Recently, reconfigurable intelligent surface (RIS) is proposed as a potential technology for future 6G wireless communications [1]. Specifically, RIS is a surface composed of a large number of controllable elements made of low-cost passive components, where each RIS element can adjust the incident signal independently [2]. With the help of the RIS, the communication environment can be controlled intelligently by optimizing the coefficients of RIS elements to satisfy different communication requirements [3]. Thanks to its low-cost and configurability, RIS has been used to assist many communication applications. For example, by reasonably deploying RIS in the cell, RIS is able to overcome the blockage of the line of sight (LoS) path, particularly for millimeter-wave (mmWave) and terahertz (THz) communications [4], [5].