Scalable Channel Estimation and Reflection Optimization for Reconfigurable Intelligent Surface-Enhanced OFDM Systems

TL;DR

This paper introduces a scalable, efficient framework for channel estimation and reflection optimization in RIS-enhanced OFDM systems, significantly reducing training overhead and complexity while improving rate performance.

Contribution

It presents a novel training-based scheme that simplifies RIS reflection optimization by pre-designing reflection vectors and estimating only the composite channel, adaptable to channel coherence time.

Findings

Reduces training overhead compared to traditional methods.

Achieves higher achievable rates with lower complexity.

Demonstrates robustness to channel estimation errors.

Abstract

This paper proposes a scalable channel estimation and reflection optimization framework for reconfigurable intelligent surface (RIS)-enhanced orthogonal frequency division multiplexing (OFDM) systems. Specifically, the proposed scheme firstly generates a training set of RIS reflection coefficient vectors offline. For each RIS reflection coefficient vector in the training set, the proposed scheme estimates only the end-to-end composite channel and then performs the transmit power allocation. As a result, the RIS reflection optimization is simplified by searching for the optimal reflection coefficient vector maximizing the achievable rate from the pre-designed training set. The proposed scheme is capable of flexibly adjusting the training overhead according to the given channel coherence time, which is in sharp contrast to the conventional counterparts. Moreover, we discuss the computational complexity of the proposed scheme and analyze the theoretical scaling law of the achievable rate versus the number of training slots. Finally, simulation results demonstrate that the proposed scheme is superior to existing approaches in terms of decreasing training overhead, reducing complexity as well as improving rate performance in the presence of channel estimation errors.