Paper Info
Title
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 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 for 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 data detection methods that suffer from the mismatch in the likelihood function.
Year
DOI
Venue
2020
10.1109/TWC.2019.2956044
IEEE Transactions on Wireless Communications
Keywords
DocType
Volume
Channel estimation,MIMO communication,OFDM,Receivers,Wireless communication,Learning (artificial intelligence),Approximation algorithms
Journal
19
Issue
ISSN
Citations 
3
1536-1276
4
PageRank 
References 
Authors
0.40
0
3
Name
Order
Citations
PageRank
Yo-Seb Jeon1549.12
Namyoon Lee285762.30
H. V. Poor3254111951.66