Joint Resource Optimization for IRS-Assisted mmWave MIMO under QoS Constraints

TL;DR

This paper proposes a joint resource optimization framework for IRS-assisted mmWave MIMO systems, enhancing system performance through dynamic power allocation and beam optimization under QoS constraints.

Contribution

It introduces a multi-blocks alternating optimization method with ADMM and gradient-projection techniques for joint resource and beam optimization in IRS-assisted mmWave MIMO systems.

Findings

Beam optimization is crucial at high SINR levels.

Joint dynamic resource allocation improves performance beyond hardware limits.

The proposed method achieves higher sum-rate in simulations.

Abstract

This letter focuses on the non-convex joint optimization with a dynamic resource of multi-user for an intelligent reflecting surface-enhanced mmWave system, where all users are concentrated on the unique cluster beam. Firstly, the objective function of the above non-linear problem is converted into a quadratic programming form under the quality of service constraints. Further, a multi-blocks alternating optimization framework with dynamic power allocation is proposed to obtain the maximum sum-rate, where the relaxed ADMM algorithm is adopted to tackle the optimal fulldigital precoder and the corresponding passive reflecting matrix is obtained by the gradient-projection. The numerical results verify that beam optimization should be emphasized in high SINR, but joint dynamic resource allocation can further improve system performance even if the hardware dimensions reaches the limit.