Machine Learning-based Methods for Reconfigurable Antenna Mode Selection in MIMO Systems

TL;DR

This paper introduces machine learning techniques, including deep neural networks and multi-armed bandits, to optimize reconfigurable antenna mode selection in MIMO systems, aiming to enhance spectral efficiency while reducing complexity.

Contribution

It presents novel ML-based algorithms for static and dynamic RA mode selection, addressing the non-convex optimization challenge with reduced computational complexity.

Findings

ML methods outperform random selection

Deep learning achieves near-optimal static solutions

Multi-armed bandit reduces dynamic selection complexity

Abstract

MIMO technology has enabled spatial multiple access and has provided a higher system spectral efficiency (SE). However, this technology has some drawbacks, such as the high number of RF chains that increases complexity in the system. One of the solutions to this problem can be to employ reconfigurable antennas (RAs) that can support different radiation patterns during transmission to provide similar performance with fewer RF chains. In this regard, the system aims to maximize the SE with respect to optimum beamforming design and RA mode selection. Due to the non-convexity of this problem, we propose machine learning-based methods for RA antenna mode selection in both dynamic and static scenarios. In the static scenario, we present how to solve the RA mode selection problem, an integer optimization problem in nature, via deep convolutional neural networks (DCNN). A Multi-Armed-bandit (MAB) consisting of offline and online training is employed for the dynamic RA state selection. For the proposed MAB, the computational complexity of the optimization problem is reduced. Finally, the proposed methods in both dynamic and static scenarios are compared with exhaustive search and random selection methods.