Particle Swarm Optimization for Weighted Sum Rate Maximization in MIMO Broadcast Channels

TL;DR

This paper presents a particle swarm optimization-based method to maximize the weighted sum rate in MIMO broadcast channels by jointly optimizing precoding and decoding matrices under power constraints.

Contribution

It introduces a novel application of PSO for transceiver design in MIMO broadcast channels, addressing the nonconvex optimization challenge.

Findings

The proposed PSO algorithm effectively converges to high-quality solutions.

The method achieves higher weighted sum rate compared to baseline approaches.

Computational complexity is manageable for practical system sizes.

Abstract

In this paper, we investigate the downlink multiple-input-multipleoutput (MIMO) broadcast channels in which a base transceiver station (BTS) broadcasts multiple data streams to K MIMO mobile stations (MSs) simultaneously. In order to maximize the weighted sum-rate (WSR) of the system subject to the transmitted power constraint, the design problem is to find the pre-coding matrices at BTS and the decoding matrices at MSs. However, such a design problem is typically a nonlinear and nonconvex optimization and, thus, it is quite hard to obtain the analytical solutions. To tackle with the mathematical difficulties, we propose an efficient stochastic optimization algorithm to optimize the transceiver matrices. Specifically, we utilize the linear minimum mean square error (MMSE) Wiener filters at MSs. Then, we introduce the constrained particle swarm optimization (PSO) algorithm to jointly optimize the precoding and decoding matrices. Numerical experiments are exhibited to validate the effectiveness of the proposed algorithm in terms of convergence, computational complexity and total WSR.