Double Intelligent Reflecting Surface-assisted Multi-User MIMO mmWave Systems with Hybrid Precoding

TL;DR

This paper explores how double intelligent reflecting surfaces (IRS) can enhance spectral efficiency in multi-user MIMO mmWave systems by jointly optimizing precoding and phase shifts using advanced optimization techniques.

Contribution

It introduces a joint optimization framework for digital precoding and IRS phase shifts in double-IRS-assisted MIMO systems, employing MM, BCD, QCQP, and RMO methods.

Findings

Double-IRS significantly improves spectral efficiency.

The proposed optimization achieves near-optimal performance.

Simulation confirms effectiveness of the joint design.

Abstract

This work investigates the effect of double intelligent reflecting surface (IRS) in improving the spectrum efficient of multi-user multiple-input multiple-output (MIMO) network operating in the millimeter wave (mmWave) band. Specifically, we aim to solve a weighted sum rate maximization problem by jointly optimizing the digital precoding at the transmitter and the analog phase shifters at the IRS, subject to the minimum achievable rate constraint. To facilitate the design of an efficient solution, we first reformulate the original problem into a tractable one by exploiting the majorization-minimization (MM) method. Then, a block coordinate descent (BCD) method is proposed to obtain a suboptimal solution, where the precoding matrices and the phase shifters are alternately optimized. Specifically, the digital precoding matrix design problem is solved by the quadratically constrained quadratic programming (QCQP), while the analog phase shift optimization is solved by the Riemannian manifold optimization (RMO). The convergence and computational complexity are analyzed. Finally, simulation results are provided to verify the performance of the proposed design, as well as the effectiveness of double-IRS in improving the spectral efficiency.