Optimizing Downlink Resource Allocation in Multiuser MIMO Networks via Fractional Programming and the Hungarian Algorithm

TL;DR

This paper introduces a novel iterative beamforming and user scheduling method for multiuser MIMO networks that leverages fractional programming and the Hungarian algorithm, significantly improving utility and efficiency over existing schemes.

Contribution

The paper presents a new joint beamforming and scheduling approach using fractional programming and the Hungarian algorithm, enhancing performance and computational efficiency in multiuser MIMO networks.

Findings

Outperforms state-of-the-art schemes in sum-log-utility and sum-rate.

Achieves higher utility with reduced computational complexity.

Demonstrates significant improvements in simulation results.

Abstract

Optimizing the sum-log-utility for the downlink of multi-frequency band, multiuser, multiantenna networks requires joint solutions to the associated beamforming and user scheduling problems through the use of cloud radio access network (CRAN) architecture; optimizing such a network is, however, non-convex and NP-hard. In this paper, we present a novel iterative beamforming and scheduling strategy based on fractional programming and the Hungarian algorithm. The beamforming strategy allows us to iteratively maximize the chosen objective function in a fashion similar to block coordinate ascent. Furthermore, based on the crucial insight that, in the downlink, the interference pattern remains fixed for a given set of beamforming weights, we use the Hungarian algorithm as an efficient approach to optimally schedule users for the given set of beamforming weights. Specifically, this approach allows us to select the best subset of users (amongst the larger set of all available users). Our simulation results show that, in terms of average sum-log-utility, as well as sum-rate, the proposed scheme substantially outperforms both the state-of-the-art multicell weighted minimum mean-squared error (WMMSE) and greedy proportionally fair WMMSE schemes, as well as standard interior-point and sequential quadratic solvers. Importantly, our proposed scheme is also far more computationally efficient than the multicell WMMSE scheme.