Self and turbo iterations for MIMO receivers and large-scale systems

TL;DR

This paper presents an improved expectation propagation-based turbo detector for large-scale MIMO systems, enhancing convergence, robustness, and performance over previous methods, especially with high-order modulations and imperfect channel information.

Contribution

It introduces a non-uniform prior for better convergence and reviews EP parameters to reduce complexity and improve stability in large-scale MIMO detection.

Findings

Enhanced detection performance compared to previous EP-based methods

Robustness against imperfect channel state information

Reduced computational complexity with improved convergence

Abstract

We investigate a turbo soft detector based on the expectation propagation (EP) algorithm for large-scale multiple-input multiple-output (MIMO) systems. Optimal detection in MIMO systems becomes computationally unfeasible for high-order modulations and/or large number of antennas. In this situation, the linear minimum mean square error (LMMSE) exhibits a low-complexity with a good performance, however far from optimal. To improve the performance, the EP algorithm can be used. In this paper, we review previous EP-based detectors and enhance their estimation in terms of complexity and performance. Specifically, we improve the convergence of the self-iterated EP stage by replacing the uniform prior by a non-uniform one, which better characterizes the information returned by the decoder once the turbo procedure starts. We also review the EP parameters to avoid instabilities when using high-order modulations and to reduce the computational complexity. Simulation results illustrate the robustness and enhanced performance of this novel detector in comparison with previous approaches found in the literature. Results also show that the proposed detector is robust in the presence of imperfect channel state information (CSI).