Joint Symbol-Level Precoding and Reflecting Designs for IRS-Enhanced MU-MISO Systems

TL;DR

This paper proposes a joint design of symbol-level precoding and IRS reflection in MU-MISO systems, demonstrating significant performance improvements through an efficient iterative algorithm and advanced optimization techniques.

Contribution

It introduces a novel joint optimization framework for symbol-level precoding and IRS reflection, employing efficient algorithms for enhanced wireless communication performance.

Findings

IRS significantly improves system performance.

Proposed algorithms effectively optimize precoding and reflection.

Joint design outperforms separate optimization approaches.

Abstract

Intelligent reflecting surfaces (IRSs) have emerged as a revolutionary solution to enhance wireless communications by changing propagation environment in a cost-effective and hardware-efficient fashion. In addition, symbol-level precoding (SLP) has attracted considerable attention recently due to its advantages in converting multiuser interference (MUI) into useful signal energy. Therefore, it is of interest to investigate the employment of IRS in symbol-level precoding systems to exploit MUI in a more effective way by manipulating the multiuser channels. In this paper, we focus on joint symbol-level precoding and reflecting designs in IRS-enhanced multiuser multiple-input single-output (MU-MISO) systems. Both power minimization and quality-of-service (QoS) balancing problems are considered. In order to solve the joint optimization problems, we develop an efficient iterative algorithm to decompose them into separate symbol-level precoding and block-level reflecting design problems. An efficient gradient-projection-based algorithm is utilized to design the symbol-level precoding and a Riemannian conjugate gradient (RCG)-based algorithm is employed to solve the reflecting design problem. Simulation results demonstrate the significant performance improvement introduced by the IRS and illustrate the effectiveness of our proposed algorithms.