Robust Bayesian Learning for Reliable Wireless AI: Framework and Applications

Matteo Zecchin, Sangwoo Park, Osvaldo Simeone, Marios Kountouris, David Gesbert

Research output: Contribution to journalArticlepeer-review


This work takes a critical look at the application of conventional machine learning methods to wireless communication problems through the lens of reliability and robustness. Deep learning techniques adopt a frequentist framework, and are known to provide poorly calibrated decisions that do not reproduce the true uncertainty caused by limitations in the size of the training data. Bayesian learning, while in principle capable of addressing this shortcoming, is in practice impaired by model misspecification and by the presence of outliers. Both problems are pervasive in wireless communication settings, in which the capacity of machine learning models is subject to resource constraints and training data is affected by noise and interference. In this context, we explore the application of the framework of robust Bayesian learning. After a tutorial-style introduction to robust Bayesian learning, we showcase the merits of robust Bayesian learning on several important wireless communication problems in terms of accuracy, calibration, and robustness to outliers and misspecification.

Original languageEnglish (US)
Pages (from-to)897-912
Number of pages16
JournalIEEE Transactions on Cognitive Communications and Networking
Issue number4
StatePublished - Aug 1 2023

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Hardware and Architecture
  • Computer Networks and Communications


  • Bayesian learning
  • channel modeling
  • localization
  • modulation classification
  • robustness


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