Efficient QAM Signal Detector for Massive MIMO Systems via PS-ADMM Approach

TL;DR

This paper introduces a novel PS-ADMM-based detector for massive MIMO systems that efficiently solves a non-convex optimization problem for QAM detection, demonstrating improved performance and convergence properties.

Contribution

The paper develops a new PS-ADMM algorithm tailored for QAM detection in massive MIMO, transforming the problem into a non-convex sharing model and proving its convergence.

Findings

Effective detection performance demonstrated in simulations

Parallel analytical solutions for variables

Convergence under mild conditions

Abstract

In this paper, we design an efficient quadrature amplitude modulation (QAM) signal detector for massive multiple-input multiple-output (MIMO) communication systems via the penalty-sharing alternating direction method of multipliers (PS-ADMM). Its main content is as follows: first, we formulate QAM-MIMO detection as a maximum-likelihood optimization problem with bound relaxation constraints. Decomposing QAM signals into a sum of multiple binary variables and exploiting introduced binary variables as penalty functions, we transform the detection optimization model to a non-convex sharing problem; second, a customized ADMM algorithm is presented to solve the formulated non-convex optimization problem. In the implementation, all variables can be solved analytically and in parallel; third, it is proved that the proposed PS-ADMM algorithm converges under mild conditions. Simulation results demonstrate the effectiveness of the proposed approach.