OFDM channel estimation in the presence of NBI and the effect of misspecified NBI model

Kalyani, Sheetal; Giridhar, K.

doi:10.1109/spawc.2007.4401334

Cited by 6 publications

(1 citation statement)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…On the other hand, we detail applications of robust Bayesian learning to communication systems, focusing on automated modulation classification (AMC), received signal strength indicator (RSSI)-based localization, as well as channel modeling and simulation. These applications have been selected in order to highlight the importance of considering uncertainty quantification, in addition to accuracy, while also emphasizing the problems of model misspecification and outliers in wireless communications [30]- [32].…”

Section: Main Contributionsmentioning

confidence: 99%

Robust Bayesian Learning for Reliable Wireless AI: Framework and Applications

Zecchin¹,

Park²,

Simeone³

et al. 2022

Preprint

View full text Add to dashboard Cite

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 tutorialstyle 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.

show abstract

Section: Main Contributionsmentioning

confidence: 99%