Optimal Data Detection in Large MIMO

TL;DR

This paper introduces LAMA, a computationally efficient data detection algorithm for large MIMO systems that achieves optimal error-rate performance under certain conditions, leveraging Bayesian AMP and state evolution analysis.

Contribution

The paper proposes LAMA, a novel large MIMO AMP-based detection algorithm that attains optimal error performance and offers practical low-complexity solutions.

Findings

LAMA achieves error-rate performance comparable to the IO detector under certain conditions.

Performance and complexity of LAMA are characterized via state evolution in large-system limits.

LAMA can approach theoretical performance limits in finite systems with low computational cost.

Abstract

Large multiple-input multiple-output (MIMO) appears in massive multi-user MIMO and randomly-spread code-division multiple access (CDMA)-based wireless systems. In order to cope with the excessively high complexity of optimal data detection in such systems, a variety of efficient yet sub-optimal algorithms have been proposed in the past. In this paper, we propose a data detection algorithm that is computationally efficient and optimal in a sense that it is able to achieve the same error-rate performance as the individually optimal (IO) data detector under certain assumptions on the MIMO system matrix and constellation alphabet. Our algorithm, which we refer to as LAMA (short for large MIMO AMP), builds on complex-valued Bayesian approximate message passing (AMP), which enables an exact analytical characterization of the performance and complexity in the large-system limit via the state-evolution framework. We derive optimality conditions for LAMA and investigate performance/complexity trade-offs. As a byproduct of our analysis, we recover classical results of IO data detection for randomly-spread CDMA. We furthermore provide practical ways for LAMA to approach the theoretical performance limits in realistic, finite-dimensional systems at low computational complexity.