Superiorized Adaptive Projected Subgradient Method with Application to MIMO Detection

TL;DR

This paper introduces a superiorized adaptive projected subgradient method for MIMO detection, demonstrating improved performance over correlated channels with low complexity, without requiring matrix inverses.

Contribution

The paper develops a set-theoretic MIMO detection framework using a superiorized APSM, offering better accuracy in correlated channels while maintaining low computational complexity.

Findings

Achieves lower symbol error ratios than existing methods on correlated channels.

Does not require matrix inverses, keeping complexity low.

Proves APSM is bounded perturbation resilient.

Abstract

In this paper, we show that the adaptive projected subgradient method (APSM) is bounded perturbation resilient. To illustrate a potential application of this result, we propose a set-theoretic framework for MIMO detection, and we devise algorithms based on a superiorized APSM. Various low-complexity MIMO detection algorithms achieve excellent performance on i.i.d. Gaussian channels, but they typically incur high performance loss if realistic channel models (e.g., correlated channels) are considered. Compared to existing low-complexity iterative detectors such as individually optimal large-MIMO approximate message passing (IO-LAMA), the proposed algorithms can achieve considerably lower symbol error ratios over correlated channels. At the same time, the proposed methods do not require matrix inverses, and their complexity is similar to IO-LAMA.