CONTRACT: Conformal Calibration for Reliable AI-Based Wireless Communications

CONTRACT sets out to investigate a novel, theoretically principled, framework for the reliable deployment of artificial intelligence (AI)-based black-box models in wireless systems. Given the highly non-deterministic patterns of traffic and connectivity conditions, AI is currently viewed as an essential technology to ensure the support of communication services with widely heterogeneous performance requirements, ranging from cloud gaming to Industry 4.0. However, AI ``apps'' are, by and large, black boxes, whose introduction within the larger wireless network raises critical concerns about reliability. These concerns are not addressed by the vast literature on AI for wireless systems, which adopts a standard ``train-and-deploy'' best-effort approach. The main goal of CONTRACT is to endow for the first time AI-based wireless deployments with formal reliability guarantees. To this end, CONTRACT will introduce novel methodologies for: (i) the offline calibration of pre-trained AI apps via hyperparameter optimization prior to deployment; and (ii) the online run-time monitoring of key performance indicators and conflicts among simultaneously deployed apps. In CONTRACT, hyperparameter optimization and online monitoring will leverage contextual information describing traffic and connectivity conditions, as well as synthetic data from a digital twin, i.e., a simulator, of the wireless network. The research will build on conformal prediction, a statistical framework for assumption-free calibration, and on prediction-powered inference as a mechanism for the reliable integration of synthetic data. This project will lead to a reconsideration of the role of AI in the engineering of wireless networks from a potential reliability concern to a dependable part of the system.