An Efficient Max-Min Fair Resource Optimization Algorithm for Rate-Splitting Multiple Access

TL;DR

This paper introduces a novel extragradient-fractional programming algorithm for efficiently solving the max-min fairness problem in rate-splitting multiple access, achieving high performance with reduced computational complexity.

Contribution

It develops a low-complexity, scalable optimization algorithm for MMF in RSMA, including handling imperfect channel information, outperforming existing methods in speed and fairness.

Findings

Achieves MMF rates comparable to SCA with less than 10% of the CPU time.

Introduces a low-dimensional EG-FP algorithm with complexity independent of transmit antennas.

Demonstrates robustness under imperfect channel state information.

Abstract

The max-min fairness (MMF) problem in rate-splitting multiple access (RSMA) is known to be challenging due to its non-convex and non-smooth nature, as well as the coupled beamforming and common rate variables. Conventional algorithms to address this problem often incur high computational complexity or degraded MMF rate performance. To address these challenges, in this work, we propose a novel optimization algorithm named extragradient-fractional programming (EG-FP) to address the MMF problem of downlink RSMA. The proposed algorithm first leverages FP to transform the original problem into a block-wise convex problem. For the subproblem of precoding block, we show that its Lagrangian dual is equivalent to a variational inequality problem, which is then solved using an extragradient-based algorithm. Additionally, we discover the optimal beamforming structure of the problem and based on which, we introduce a low-dimensional EG-FP algorithm with computational complexity independent of the number of transmit antennas. This feature is especially beneficial in scenarios with a large number of transmit antennas. The proposed algorithms are then extended to handle imperfect channel state information at the transmitter (CSIT). Numerical results demonstrate that the MMF rate achieved by our proposed algorithms closely matches that of the conventional successive convex approximation (SCA) algorithm and significantly outperforms other baseline schemes. Remarkably, the average CPU time of the proposed algorithms is less than 10\% of the runtime required by the SCA algorithm, showing the efficiency and scalability of the proposed algorithms.