Robust Processing and Learning: Principles, Methods, and Wireless Applications

TL;DR

This tutorial overview explores the principles and methods of robustness in wireless sensing and communication, covering theoretical foundations, key techniques, and recent signal processing applications addressing uncertainties and adversarial challenges.

Contribution

It provides a comprehensive synthesis of robustness theory and recent advances in wireless signal processing, bridging classical and modern approaches for the community.

Findings

Review of robust estimation and optimization techniques

Analysis of robustness costs in system design

Application of robust methods to wireless sensing tasks

Abstract

This tutorial-style overview article examines the fundamental principles and methods of robustness, using wireless sensing and communication (WSC) as the narrative and exemplifying framework. First, we formalize the conceptual and mathematical foundations of robustness, highlighting the interpretations and relations across robust statistics, optimization, and machine learning. Key techniques, such as robust estimation and testing, distributionally robust optimization, and regularized and adversary training, are investigated. Together, the costs of robustness in system design, for example, the compromised nominal performances and the extra computational burdens, are discussed. Second, we review recent robust signal processing solutions for WSC that address model mismatch, data scarcity, adversarial perturbation, and distributional shift. Specific applications include robust ranging-based localization, modality sensing, channel estimation, receive combining, waveform design, and federated learning. Through this effort, we aim to introduce the classical developments and recent advances in robustness theory to the general signal processing community, exemplifying how robust statistical, optimization, and machine learning approaches can address the uncertainties inherent in WSC systems.