Reconfigurable Intelligent Surface Assisted OFDM Relaying: Subcarrier Matching with Balanced SNR

TL;DR

This paper proposes a joint design framework for RIS-assisted OFDM relaying that optimizes subcarrier matching and passive beamforming to significantly enhance achievable rates, employing novel algorithms including branch-and-bound, difference-of-convex, and learning-to-optimize.

Contribution

It introduces a comprehensive joint optimization approach for RIS-assisted OFDM relaying, including novel algorithms for subcarrier matching and beamforming.

Findings

Achieves substantial rate gains over systems without RIS.

Balances SNR among subcarrier pairs for improved performance.

Demonstrates effectiveness of proposed algorithms through simulations.

Abstract

This paper considers a reconfigurable intelligent surface (RIS) aided orthogonal frequency division multiplexing (OFDM) relaying system, and investigates the joint design of RIS passive beamforming and subcarrier matching under two cases, where Case-I ignores the source-RIS-destination signal, while Case-II explores this signal for rate enhancement. We formulate a mixed-integer nonlinear programming (MINIP) problem to maximize the sum achievable rate of all subcarriers by jointly optimizing the passive beamforming and subcarrier matching. To solve this problem, we first develop a branch-and-bound (BnB)-based alternating optimization algorithm for attaining a near-optimal solution. Then, a low-complexity difference-of-convex penalty-based algorithm and learning-to-optimize approach are also proposed. Finally, simulation results demonstrate that the RIS-assisted OFDM relaying system achieves a substantial achievable rate gain as compared to that without RIS since RIS recasts the subcarrier matching and balances the signal-to-noise ratio (SNR) among different subcarrier pairs.