Robust Data Detection for MIMO Systems with One-Bit ADCs: A Reinforcement Learning Approach

TL;DR

This paper introduces a reinforcement learning-based method to improve data detection in MIMO systems with one-bit ADCs by learning the likelihood function despite channel estimation errors.

Contribution

It proposes a novel likelihood function learning approach using reinforcement learning to address mismatch issues caused by one-bit ADCs in MIMO systems.

Findings

Significant performance improvements in data detection accuracy.

Effective compensation for likelihood function mismatch.

Robustness against channel estimation errors.

Abstract

The use of one-bit analog-to-digital converters (ADCs) at a receiver is a power-efficient solution for future wireless systems operating with a large signal bandwidth and/or a massive number of receive radio frequency chains. This solution, however, induces a high channel estimation error and therefore makes it difficult to perform the optimal data detection that requires perfect knowledge of likelihood functions at the receiver. In this paper, we propose a likelihood function learning method for multiple-input multiple-output (MIMO) systems with one-bit ADCs using a reinforcement learning approach. The key idea is to exploit input-output samples obtained from data detection, to compensate the mismatch in the likelihood function. The underlying difficulty of this idea is a label uncertainty in the samples caused by a data detection error. To resolve this problem, we define a Markov decision process (MDP) to maximize the accuracy of the likelihood function learned from the samples. We then develop a reinforcement learning algorithm that efficiently finds the optimal policy by approximating the transition function and the optimal state of the MDP. Simulation results demonstrate that the proposed method provides significant performance gains for the optimal data detection methods that suffer from the mismatch in the likelihood function.