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

2026-02-10

AI Summary

This work addresses the challenges posed by model mismatch, data scarcity, adversarial perturbations, and distribution shifts in wireless sensing and communication systems by proposing a unified robust signal processing framework. Integrating techniques from robust statistics, distributionally robust optimization, and adversarial training, the framework systematically characterizes the trade-off between performance and robustness. The proposed approach is evaluated across several critical tasks—including robust ranging and localization, multimodal sensing, receive combining, and waveform design—demonstrating significant improvements in system reliability under non-ideal conditions. Experimental results validate the effectiveness and broad applicability of the framework in enhancing resilience against diverse sources of uncertainty inherent in practical wireless environments.

Technology Category

• Machine Learning: Adversarial Learning & Robustness
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Planning, Routing, and Scheduling: Scheduling under Uncertainty

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems

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.

Problem

• model mismatch
• data scarcity
• adversarial perturbation
• distributional shift
• wireless sensing and communication

Innovation

• robustness
• distributionally robust optimization
• adversarial training
• wireless sensing and communication
• federated learning