Efficient Deep Learning-based Cascaded Channel Feedback in RIS-Assisted Communications

TL;DR

This paper proposes a deep learning framework for efficient cascaded channel feedback in RIS-assisted communications, significantly reducing feedback overhead by capturing and compressing channel variations.

Contribution

It introduces a novel deep learning-based method that captures intrinsic channel variations and compresses feedback, improving efficiency in RIS-assisted systems.

Findings

Reduces feedback overhead by capturing channel variations

Uses autoencoder-based deep compression for compact feedback

Numerical results show significant reduction in feedback and computational load

Abstract

In the realm of reconfigurable intelligent surface (RIS)-assisted communication systems, the connection between a base station (BS) and user equipment (UE) is formed by a cascaded channel, merging the BS-RIS and RIS-UE channels. Due to the fixed positioning of the BS and RIS and the mobility of UE, these two channels generally exhibit different time-varying characteristics, which are challenging to identify and exploit for feedback overhead reduction, given the separate channel estimation difficulty. To address this challenge, this letter introduces an innovative deep learning-based framework tailored for cascaded channel feedback, ingeniously capturing the intrinsic time variation in the cascaded channel. When an entire cascaded channel has been sent to the BS, this framework advocates the feedback of an efficient representation of this variation within a subsequent period through an extraction-compression scheme. This scheme involves RIS unit-grained channel variation extraction, followed by autoencoder-based deep compression to enhance compactness. Numerical simulations confirm that this feedback framework significantly reduces both the feedback and computational burdens.