Gibbs Distribution Based Antenna Splitting and User Scheduling in Full Duplex Massive MIMO Systems

TL;DR

This paper introduces a Gibbs distribution-based algorithm for joint antenna splitting and user scheduling in full duplex massive MIMO systems, significantly improving spectral efficiency while reducing computational complexity.

Contribution

It proposes a novel stochastic gradient descent method converting the problem into a KL divergence minimization, enabling efficient joint optimization in massive MIMO systems.

Findings

Spectral efficiency improved with the proposed algorithm.

Algorithm's SE curves match exhaustive search results.

Reduced computational complexity compared to exhaustive search.

Abstract

A Gibbs distribution based combinatorial optimization algorithm for joint antenna splitting and user scheduling problem in full duplex massive multiple-input multiple-output (MIMO) system is proposed in this paper. The optimal solution of this problem can be determined by exhaustive search. However, the complexity of this approach becomes prohibitive in practice when the sample space is large, which is usually the case in massive MIMO systems. Our algorithm overcomes this drawback by converting the original problem into a Kullback-Leibler (KL) divergence minimization problem, and solving it through a related dynamical system via a stochastic gradient descent method. Using this approach, we improve the spectral efficiency (SE) of the system by performing joint antenna splitting and user scheduling. Additionally, numerical results show that the SE curves obtained with our proposed algorithm overlap with the curves achieved by exhaustive search for user scheduling.