Intelligent Reflecting Surface Meets OFDM: Protocol Design and Rate Maximization

TLDR

Intelligent reflecting surfaces (IRS) promise spectrum and energy efficiency in wireless systems, yet prior work has focused on frequency‑flat channels and perfect CSI at the transmitter. This paper investigates IRS‑enhanced OFDM over frequency‑selective channels and introduces a practical transmission protocol that incorporates channel estimation. To reduce training overhead, the authors group adjacent IRS elements sharing a common reflection coefficient, estimate only the combined channel per group, and then jointly optimize transmit power and IRS coefficients via an alternating algorithm with a tailored initialization. Simulations demonstrate that the proposed design markedly boosts OFDM link rates compared to an IRS‑free system and reveal an optimal grouping size that balances training overhead against beamforming flexibility.

Abstract

Intelligent reflecting surface (IRS) is a promising new technology for achieving both spectrum and energy efficient wireless communication systems in the future. However, existing works on IRS mainly consider frequency-flat channels and assume perfect knowledge of channel state information (CSI) at the transmitter. Motivated by the above, in this paper we study an IRS-enhanced orthogonal frequency division multiplexing (OFDM) system under frequency-selective channels and propose a practical transmission protocol with channel estimation. First, to reduce the overhead in channel training as well as exploit the channel spatial correlation, we propose a novel IRS elements grouping method, where each group consists of a set of adjacent IRS elements that share a common reflection coefficient. Based on this method, we propose a practical transmission protocol where only the combined channel of each group needs to be estimated, thus substantially reducing the training overhead. Next, with any given grouping and estimated CSI, we formulate the problem to maximize the achievable rate by jointly optimizing the transmit power allocation and the IRS passive array reflection coefficients. Although the formulated problem is non-convex and thus difficult to solve, we propose an efficient algorithm to obtain a high-quality suboptimal solution for it, by alternately optimizing the power allocation and the passive array coefficients in an iterative manner, along with a customized method for the initialization. Simulation results show that the proposed design significantly improves the OFDM link rate performance as compared to the case without using IRS. Moreover, it is shown that there exists an optimal size for IRS elements grouping which achieves the maximum achievable rate due to the practical trade-off between the training overhead and IRS passive beamforming flexibility.