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

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 multipleoutput (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. Index TermsMultiple-input-multiple-output (MIMO), one-bit analog-to-digital converter (ADC), reinforcement learning, robust data detection, likelihood function learning.

Low-Resolution Quantization in Phase Modulated Systems: Optimum Detectors and Error Rate Analysis

“…In [32], the authors focus on the development of soft-output detection methods for low-precision ADCs and propose another near-optimal detection method for a coded mmWave MIMO system. More recently, there has been progress in machine learning based approaches as well [33]- [35]. In [33], [34] a reinforcement learning approach is used to design a robust likelihood function learning method for MIMO systems with one-bit ADCs.…”

“…More recently, there has been progress in machine learning based approaches as well [33]- [35]. In [33], [34] a reinforcement learning approach is used to design a robust likelihood function learning method for MIMO systems with one-bit ADCs. For a similar system, a semi-supervised learning detector is proposed in [35] which is further improved to an online-learning detector.…”

“…. Rayleigh Fading: For special case of Rayleigh fading, which is obtained by setting m = 1, the expression in (32) can be re-expressed as (33).…”

“…By using the Taylor series expansion for high SNR values, we can re-express the average SEP expressions for QPSK modulation under Rayleigh fading given in (33) as (34).…”

Supervised-Learning for Multi-Hop MU-MIMO Communications With One-Bit Transceivers

“…. , T t do {Training for parameter learning} 2: Therefore, for the implementation, the number of co-scheduled uplink users should be chosen to meet the constraint of pilot overheads or a semi-supervised-learning and reinforcement-learning methods can be used as in [29] and [30]. In addition, the complexity of the A-ML detector can be further reduced using one-bit sphere decoding in [15].…”