Deep Learning Based RIS Channel Extrapolation with Element-grouping

TL;DR

This paper introduces a deep learning framework for RIS channel extrapolation that reduces pilot overhead by element-grouping and interference mitigation, enabling more accurate channel estimation in wireless networks.

Contribution

It proposes a novel deep learning-based method with two cascaded networks for interference elimination and channel extrapolation in RIS systems, improving estimation accuracy.

Findings

Significant gain over conventional element-grouping methods

Effective interference mitigation through deep learning networks

Enhanced channel estimation accuracy

Abstract

Reconfigurable intelligent surface (RIS) is considered as a revolutionary technology for future wireless communication networks. In this letter, we consider the acquisition of the cascaded channels, which is a challenging task due to the massive number of passive RIS elements. To reduce the pilot overhead, we adopt the element-grouping strategy, where each element in one group shares the same reflection coefficient and is assumed to have the same channel condition. We analyze the channel interference caused by the element-grouping strategy and further design two deep learning based networks. The first one aims to refine the partial channels by eliminating the interference, while the second one tries to extrapolate the full channels from the refined partial channels. We cascade the two networks and jointly train them. Simulation results show that the proposed scheme provides significant gain compared to the conventional element-grouping method without interference elimination.