Mimo Detection by Variational Posterior Inference

Abstract

In this paper we examine the application of variational inference (VI) to MIMO detection. Our study is motivated by the recent interest in applying machine learning concepts to signal processing. VI is an approach for providing friendly approximations of certain intractable posterior probabilities in statistics, and it has been popularly used in machine learning. In MIMO detection we also have a similar problem; specifically, we want to evaluate the posterior symbol probabilities for detection, but they are computationally too expensive to evaluate when the problem size and/or the constellation size are large. By approximating the discrete symbol prior by a continuous Gaussian mixture model, we show how the notion of VI can be used to derive an iterative MIMO detector. Interestingly, the detector resembles the MMSE detector in structure. The performance of the proposed detector is demonstrated by simulations.