Massive MIMO Data Detection Using Matrix Inversion Method and Deep Neural Network

TL;DR: This paper proposes a hybrid data detection algorithm for massive MIMO systems, combining linear approximate matrix inversion with a deep neural network to achieve near-optimal performance with lower complexity, outperforming classical methods in various simulations.

Abstract: As the number of users within a cellular system increase, so does the need for a higher quality of service and performance. Massive multiple-input multiple-output (MIMO) is a staple technology in the implementation of fifth-generation (5G) cellular networks. The technology leverages multiple antennas at the base station and within user devices to increase spectral efficiency, link reliability, and range. With massive MIMO being a significant area of research, several data detection techniques exist. Classical methods leveraging linear detection algorithms and linear approximate matrix inversions exist. However, as of recently, detection algorithms utilizing deep learning (DL) have been proposed. The detection problem requires techniques that are both robust and provide near-optimal performance at the expense of minimal complexity within different channel scenarios. DL utilizes machine learning to train a detection algorithm providing comparable performance to classic techniques with the advantage of lower complexity. This paper offers a hybrid detection algorithm consisting of a linear approximate matrix inversion step followed by a DL algorithm. The approximated message vector calculated in the first step is the initial iterate fed to the DL algorithm, which continuously improves the detection accuracy. Various simulations are conducted, demonstrating the significant superiority of the proposed framework. The study is concluded with an analysis of the complexity of the hybrid algorithm in addition to a discussion of the model's performance.