Symbol-Level Precoding Design for Intelligent Reflecting Surface Assisted Multi-user MIMO Systems

TL;DR

This paper explores the design of symbol-level precoders for IRS-assisted multi-user MIMO systems, aiming to minimize error rates with low-resolution phase shifts using advanced optimization techniques.

Contribution

It introduces a novel precoder design framework for IRS-based low-resolution transmitters, including relaxation, quantization, and branch-and-bound methods for different resolutions.

Findings

Effective precoding algorithms for low-resolution IRS transmitters

Significant error rate reduction demonstrated in simulations

Proposed methods outperform existing approaches

Abstract

Intelligent reflecting surface (IRS) has emerged as a promising solution to enhance wireless information transmissions by adaptively controlling prorogation environment. Recently, the brand-new concept of utilizing IRS to implement a passive transmitter attracts researchers' attention since it potentially realizes low-complexity and hardware-efficient transmitters of multiple-input single/multiple-output (MISO/MIMO) systems. In this paper we investigate the problem of precoder design for a low-resolution IRS-based transmitter to implement multi-user MISO/MIMO wireless communications. Particularly, the IRS modulates information symbols by varying the phases of its reflecting elements and transmits them to $K$ single-antenna or multi-antenna users. We first aim to design the symbol-level precoder for IRS to realize the modulation and minimize the maximum symbol-error-rate (SER) of single-antenna receivers. In order to tackle this NP-hard problem, we first relax the low-resolution phase-shift constraint and solve this problem by Riemannian conjugate gradient (RCG) algorithm. Then, the low-resolution symbol-level precoding vector is obtained by direct quantization. Considering the large quantization error for 1-bit resolution case, the branch-and-bound method is utilized to solve the 1-bit resolution symbol-level precoding vector. For multi-antenna receivers, we propose to iteratively design the symbol-level precoder and combiner by decomposing the original large-scale optimization problem into several sub-problems. Simulation results validate the effectiveness of our proposed algorithms.