Probabilistic MIMO Symbol Detection with Expectation Consistency Approximate Inference

TL;DR

This paper introduces a novel low-complexity probabilistic MIMO detection algorithm based on Expectation Consistency, which improves accuracy over existing methods and is validated through mutual information analysis and LDPC coding performance.

Contribution

It presents a new EC-based algorithm for MIMO detection that generalizes existing inference methods and offers a tradeoff between accuracy and computational speed.

Findings

EC detector significantly outperforms state-of-the-art methods

Complexity is cubic in the number of antennas

Enhanced performance with LDPC channel coding

Abstract

In this paper we explore low-complexity probabilistic algorithms for soft symbol detection in high-dimensional multiple-input multiple-output (MIMO) systems. We present a novel algorithm based on the Expectation Consistency (EC) framework, which describes the approximate inference problem as an optimization over a non-convex function. EC generalizes algorithms such as Belief Propagation and Expectation Propagation. For the MIMO symbol detection problem, we discuss feasible methods to find stationary points of the EC function and explore their tradeoffs between accuracy and speed of convergence. The accuracy is studied, first in terms of input-output mutual information and show that the proposed EC MIMO detector greatly improves state-of-the-art methods, with a complexity order cubic in the number of transmitting antennas. Second, these gains are corroborated by combining the probabilistic output of the EC detector with a low-density parity-check (LDPC) channel code.