User Selection in Millimeter Wave Massive MIMO System using Convolutional Neural Networks

TL;DR

This paper introduces a low-complexity convolutional neural network framework for user selection in millimeter wave massive MIMO systems, achieving near-optimal performance with reduced computational cost and robustness to channel imperfections.

Contribution

It proposes a novel CNN-based user selection method that outperforms traditional algorithms in efficiency and effectiveness for mmWave massive MIMO systems.

Findings

CNN achieves near-optimal achievable rate compared to exhaustive search.

CNN outperforms evolutionary and greedy algorithms in rate and complexity.

The scheme is robust to channel imperfections.

Abstract

A hybrid architecture for millimeter wave (mmW) massive MIMO systems is considered practically implementable due to low power consumption and high energy efficiency. However, due to the limited number of RF chains, user selection becomes necessary for such architecture. Traditional user selection algorithms suffer from high computational complexity and, therefore, may not be scalable in 5G and beyond wireless mobile communications. To address this issue, in this letter we propose a low complexity CNN framework for user selection. The proposed CNN accepts as input the channel matrix and gives as output the selected users. Simulation results show that the proposed CNN performs close to optimal exhaustive search in terms of achievable rate, with negligible computational complexity. In addition, CNN based user selection outperforms the evolutionary algorithm and the greedy algorithm in terms of both achievable rate and computational complexity. Finally, simulation results also show that the proposed CNN based user selection scheme is robust to channel imperfections.