Deep Learning-based Time-varying Channel Estimation for RIS Assisted Communication

TL;DR

This paper introduces a deep learning approach utilizing neural ODEs for efficient time-varying channel estimation in RIS-assisted wireless communication, significantly reducing pilot overhead and improving channel extrapolation in high mobility scenarios.

Contribution

It proposes a novel neural ODE-based neural network architecture for joint time and antenna domain channel extrapolation in RIS systems, addressing high mobility challenges.

Findings

Effective channel extrapolation in high mobility scenarios

Reduced pilot overhead for RIS channel estimation

Improved accuracy over traditional methods

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 time-varying cascaded channels, which is a challenging task due to the massive number of passive RIS elements and the small channel coherence time. To reduce the pilot overhead, a deep learning-based channel extrapolation is implemented over both antenna and time domains. We divide the neural network into two parts, i.e., the time-domain and the antenna-domain extrapolation networks, where the neural ordinary differential equations (ODE) are utilized. In the former, ODE accurately describes the dynamics of the RIS channels and improves the recurrent neural network's performance of time series reconstruction. In the latter, ODE is resorted to modify the relations among different data layers in a feedforward neural network. We cascade the two networks and jointly train them. Simulation results show that the proposed scheme can effectively extrapolate the cascaded RIS channels in high mobility scenario.