Mismatched Data Detection in Massive MU-MIMO

TL;DR

This paper enhances massive MU-MIMO data detection by incorporating prior mismatch tuning into the LAMA algorithm, enabling simpler, efficient detectors that maintain near-optimal error rates, especially with hardware-friendly priors.

Contribution

It introduces a tuning stage into LAMA for prior mismatch, leading to low-complexity, near-optimal data detectors with practical hardware implementations.

Findings

Tuning priors in LAMA improves detection performance.

Hardware-friendly priors enable efficient near-optimal detection.

Performance analysis aligns with classical results for specific priors.

Abstract

We investigate mismatched data detection for massive multi-user (MU) multiple-input multiple-output (MIMO) wireless systems in which the prior distribution of the transmit signal used in the data detector differs from the true prior. In order to minimize the performance loss caused by the prior mismatch, we include a tuning stage into the recently proposed large-MIMO approximate message passing (LAMA) algorithm, which enables the development of data detectors with optimal as well as sub-optimal parameter tuning. We show that carefully-selected priors enable the design of simpler and computationally more efficient data detection algorithms compared to LAMA that uses the optimal prior, while achieving near-optimal error-rate performance. In particular, we demonstrate that a hardware-friendly approximation of the exact prior enables the design of low-complexity data detectors that achieve near individually-optimal performance. Furthermore, for Gaussian priors and uniform priors within a hypercube covering the quadrature amplitude modulation (QAM) constellation, our performance analysis recovers classical and recent results on linear and non-linear massive MU-MIMO data detection, respectively.