A Machine Learning Approach to MIMO Communications

Abstract

Inspired by the phenomenon that the received signals naturally form clusters, we propose a novel machine learning framework to design multi-input multi-output (MIMO) communication systems. In the proposed framework, the MIMO detection problem is converted into a clustering problem, and known labels are transmitted to assist the receiver for labeling the clusters. A modulation-constrained Gaussian mixture model (MC-GMM) and the associated optimization algorithm are developed to reduce the number of parameters to be learnt in the clustering algorithm. Furthermore, we propose a method called label reconstruction to minimize the overhead of label transmission, and the design of the optimal labels is studied. Simulation results are presented to verify the effectiveness of the proposed label-assisted clustering (LAC) receiver in approaching the optimal maximum likelihood detection (MLD) with perfectly known channel knowledge for typical MIMO systems.