Transforming RIS-Assisted Passive Beamforming from Tedious to Simple: A Relaxation Algorithm for Rician Channel

TL;DR

This paper introduces a relaxation algorithm for RIS-assisted passive beamforming that simplifies the optimization process, achieves near-optimal SNR, and significantly reduces computational time in Rician channels.

Contribution

It presents a novel relaxation algorithm for passive beamforming that is simpler and faster while maintaining high SNR performance in RIS-assisted systems.

Findings

RA achieves over 98% of the upper bound SNR

RA requires less than 1% of the time of manifold optimization

RA outperforms semidefinite relaxation in computational efficiency

Abstract

This paper investigates the problem of maximizing the signal-to-noise ratio (SNR) in reconfigurable intelligent surface (RIS)-assisted MISO communication systems. The problem will be reformulated as a complex quadratic form problem with unit circle constraints. We proved that the SNR maximizing problem has a closed-form global optimal solution when it is a rank-one problem, whereas the former researchers regarded it as an optimization problem. Moreover, We propose a relaxation algorithm (RA) that relaxes the constraints to that of Rayleigh's quotient problem and then projects the solution back, where the SNR obtained by RA achieves much the same SNR as the upper bound but with significantly low time consumption. Then we asymptotically analyze its performance when the transmitter antennas n_t and the number of units of RIS N grow large together, with N/n_t -> c. Finally, our numerical simulations show that RA achieves over 98% of the performance of the upper bound and takes below 1% time consumption of manifold optimization (MO) and 0.1% of semidefinite relaxation (SDR).