Uncertainty Awareness in Wireless Communications and Sensing

TL;DR

This paper reviews the challenges posed by uncertainties in wireless communications and sensing systems, and discusses architectural, modeling, and computational strategies to enhance robustness and trustworthiness.

Contribution

It provides a comprehensive overview of uncertainty-aware design principles and frameworks, highlighting recent advances and trade-offs in WCS.

Findings

Uncertainties can cause performance degradation and security issues.

Various uncertainty mitigation strategies improve system robustness.

Trade-offs between robustness and optimality are crucial in design choices.

Abstract

Wireless communications and sensing (WCS) establish the backbone of modern information exchange and environment perception. Typical applications range from mobile networks and the Internet of Things to radar and sensor grids. Despite transformative capabilities, wireless systems often face diverse uncertainties in design and operation, such as modeling errors due to incomplete physical knowledge, statistical errors arising from data scarcity, measurement errors caused by sensor imperfections, computational errors owing to resource limitation, and unpredictability of environmental evolution. Once ignored, these uncertainties can lead to severe outcomes, e.g., performance degradation, system untrustworthiness, inefficient resource utilization, and security vulnerabilities. As such, this article reviews mature and emerging architectural, computational, and operational countermeasures, encompassing uncertainty-aware designs of signals and systems (e.g., diversity, adaptivity, modularity), as well as uncertainty-aware modeling and computational frameworks (e.g., risk-informed optimization, robust signal processing, and trustworthy machine learning). Trade-offs to employ these methods, e.g., robustness vs optimality, are also highlighted.