Low-Complexity Algorithm for Worst-Case Utility Maximization in Multiuser MISO Downlink

TL;DR

This paper introduces a low-complexity iterative algorithm for worst-case utility maximization in multiuser MISO downlink systems, providing a practical solution with proven convergence and Pareto optimality.

Contribution

It proposes a novel low-complexity iterative algorithm for the NP-hard WCUM problem, improving computational efficiency and performance over existing methods.

Findings

The algorithm converges and is Pareto optimal.

Simulation results show significant performance gains.

The method reduces computational complexity compared to prior algorithms.

Abstract

This work considers worst-case utility maximization (WCUM) problem for a downlink wireless system where a multiantenna base station communicates with multiple single-antenna users. Specifically, we jointly design transmit covariance matrices for each user to robustly maximize the worst-case (i.e., minimum) system utility function under channel estimation errors bounded within a spherical region. This problem has been shown to be NP-hard, and so any algorithms for finding the optimal solution may suffer from prohibitively high complexity. In view of this, we seek an efficient and more accurate suboptimal solution for the WCUM problem. A low-complexity iterative WCUM algorithm is proposed for this nonconvex problem by solving two convex problems alternatively. We also show the convergence of the proposed algorithm, and prove its Pareto optimality to the WCUM problem. Some simulation results are presented to demonstrate its substantial performance gain and higher computational efficiency over existing algorithms.