Robust Bayesian Learning for Reliable Wireless AI: Framework and Applications

• Published in: IEEE transactions on cognitive communications and networking Vol. 9; no. 4; pp. 897 - 912
• Main Authors: Zecchin, Matteo, Park, Sangwoo, Simeone, Osvaldo, Kountouris, Marios, Gesbert, David
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.08.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Bayes methods; Bayesian analysis; Bayesian learning; channel modeling; Computer Science; Constraint modelling; Data models; Deep learning; localization; Machine learning; modulation classification; Outliers (statistics); Robustness; Statistics; Training; Training data; Uncertainty; Wireless communication; Wireless communications; localization; robustness; modulation classification; Bayesian learning robustness localization modulation classification channel modeling; channel modeling; Bayesian learning
• ISSN: 2332-7731; 2332-7731
• DOI: 10.1109/TCCN.2023.3261300

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.