Learning Based Signal Detection for MIMO Systems with Unknown Noise Statistics

Ke He, Le He, Lisheng Fan, Yansha Deng, George K. Karagiannidis, Arumugam Nallanathan

Research output: Contribution to journalArticlepeer-review

43 Citations (Scopus)


This paper aims to devise a generalized maximum likelihood (ML) estimator to robustly detect signals with unknown noise statistics in multiple-input multiple-output (MIMO) systems. In practice, there is little or even no statistical knowledge on the system noise, which in many cases is non-Gaussian, impulsive and not analyzable. Existing detection methods have mainly focused on specific noise models, which are not robust enough with unknown noise statistics. To tackle this issue, we propose a novel ML detection framework to effectively recover the desired signal. Our framework is a fully probabilistic one that can efficiently approximate the unknown noise distribution through a normalizing flow. Importantly, this framework is driven by an unsupervised learning approach, where only the noise samples are required. To reduce the computational complexity, we further present a low-complexity version of the framework, by utilizing an initial estimation to reduce the search space. Simulation results show that our framework outperforms other existing algorithms in terms of bit error rate (BER) in non-analytical noise environments, while it can reach the ML performance bound in analytical noise environments.

Original languageEnglish
Article number9353547
Pages (from-to)3025-3038
Number of pages14
Issue number5
Publication statusPublished - May 2021


  • generative models
  • impulsive noise
  • MIMO
  • Signal detection
  • unknown noise statistics
  • unsupervised learning


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