CIF: Small: Risk-Aware Resource Allocation for Robust Wireless Autonomy

Yale University, New Haven CT

Investigators

Abstract

Wireless autonomous networked systems (WANS) are virtually everywhere around us, performing all kinds of often complex and pervasive data-centric manipulations, such as sensing, processing, learning, and acting (i.e., decision-making). Examples include modern wireless communication networks (e.g., based on 5G/6G, mmWave/THz technologies), drone swarms, mobile or robotic networks, unmanned aerial vehicles (UAVs), self-driving cars, and the Internet of Things (IoT). While WANS and their applications present a high potential for societal and economic growth, the operation of such systems requires not only to be efficient and driven by actual, observable data but also to meet often strict design specifications. These specifications are induced by the need to maintain performance robustness and resilience, which, in turn, translates into latency, reliability, fairness, and trustworthiness guarantees. Such criteria and constraints are fundamentally connected to intrinsic risks associated with the operation of wireless systems; those risks are due to inherent uncertainties caused by naturally occurring phenomena such as nontrivial statistical dispersion of the wireless medium as well as randomness in the behavior of multiple users and devices, often with complex and heterogeneous features and objectives. This project puts forward a new principled methodological framework for systematic risk-aware resource allocation in wireless systems, bridging the operational gap between ergodic risk neutrality and minimax conservativeness. The investigation focuses not only on formulation and dual-domain variational analysis of new constrained risk-aware resource-allocation problems but also on the development of theory as well as efficient methods for both model-based synthesis and model-free reinforcement learning of optimal risk-aware policies. It is expected that this work will establish a new paradigm in wireless systems resource allocation. Preliminary results on basic stylized resource-allocation problems - as simple as single-user power-constrained rate maximization - demonstrate clear advantages of risk-aware policies against both their ergodic (i.e., risk-neutral) and minimax counterparts. However, obtaining optimal risk-aware policies in more realistic and useful settings is nontrivial: in risk-aware problems, the role of expectations is played by more general functionals, called risk measures, for which fundamental properties of expectation - such as linearity, homogeneity, or the tower property - are generally absent. Such complications are naturally amplified within a constrained optimization setup. This project concentrates on such risk-aware problems within the context of constrained resource allocation for wireless systems and is divided into three main thrusts: 1) Lagrangian duality in risk-aware resource allocation, 2) model-based data-driven synthesis of risk-aware resource-allocation policies and 3) model-free learning of risk-aware resource-allocation policies. The principal investigator anticipates that the project will be instrumental in ameliorating inherent challenges under the risk-aware setting, such as the presence of risk-measure-based variational stochastic constraints, infinite dimensionality of resource policies, nonconvexity of random services, and channel/system-model availability, ultimately rendering risk-aware wireless-system resource allocation an intellectually accessible and computationally affordable task. The project will also be relevant to several areas beyond wireless autonomy - such as finance, economics, energy, and robotics - and may trigger new developments in the intersection of communications, information theory, statistics, and optimization, as well as inspire new tools in risk-aware and constrained learning. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.