Robust data detection for MIMO systems with one-bit ADCs: A reinforcement learning approach

Yo Seb Jeon, Namyoon Lee, H. Vincent Poor

Research output: Contribution to journalArticlepeer-review

46 Scopus citations


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.

Original languageEnglish (US)
Article number8924916
Pages (from-to)1663-1676
Number of pages14
JournalIEEE Transactions on Wireless Communications
Issue number3
StatePublished - Mar 2020
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Electrical and Electronic Engineering
  • Applied Mathematics


  • Multiple-input-multiple-output (MIMO)
  • likelihood function learning
  • one-bit analog-to-digital converter (ADC)
  • reinforcement learning
  • robust data detection


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