Robust MIMO Detection With Imperfect CSI: A Neural Network Solution

TL;DR

This paper introduces neural network-based robust detection methods for MIMO systems with imperfect CSI, combining model-driven and data-driven approaches to improve detection accuracy under channel uncertainties.

Contribution

It proposes a novel unfolding-based neural network, RADMMNet, for robust MIMO detection, and develops a low-complexity version LCRADMMNet, along with a robust channel tracking method RDAKF.

Findings

Proposed networks outperform non-robust detectors in simulations.

The unfolding approach effectively combines model-driven and data-driven methods.

Robust detection significantly improves performance under CSI uncertainties.

Abstract

In this paper, we investigate the design of statistically robust detectors for multi-input multi-output (MIMO) systems subject to imperfect channel state information (CSI). A robust maximum likelihood (ML) detection problem is formulated by taking into consideration the CSI uncertainties caused by both the channel estimation error and the channel variation. To address the challenging discrete optimization problem, we propose an efficient alternating direction method of multipliers (ADMM)-based algorithm, which only requires calculating closed-form solutions in each iteration. Furthermore, a robust detection network RADMMNet is constructed by unfolding the ADMM iterations and employing both model-driven and data-driven philosophies. Moreover, in order to relieve the computational burden, a low-complexity ADMM-based robust detector is developed using the Gaussian approximation, and the corresponding deep unfolding network LCRADMMNet is further established. On the other hand, we also provide a novel robust data-aided Kalman filter (RDAKF)-based channel tracking method, which can effectively refine the CSI accuracy and improve the performance of the proposed robust detectors. Simulation results validate the significant performance advantages of the proposed robust detection networks over the non-robust detectors with different CSI acquisition methods.