EARS: Collaborative: Comprehensive Network State Inference for Robust and Policy-Cognizant Spectrum Access

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Objective: The objective of this project is a compendious cognition infrastructure enabling robust, cross-layer protocol designs under governance policy constraints, so as to maximize the cognitive radio (CR) network performance and end-user satisfaction, notwithstanding the challenges associated with dynamic and opportunistic radio-spectrum access, as well as the incomplete, corrupt and sporadic data reflecting the cost and restrictions in acquiring network state measurements.Intellectual merit: The intellectual merit is to transform state-of-the-art learning tools for comprehensive and resource-aware cognition of the "global" network state (which includes interference, any-to-any link gains, band occupancies, queue lengths, and path delays), jointly with stochastic, state-cognizant optimization of CR networks with policy implications, to pioneer transformative adaptation approaches to routing, medium-access, and physical-layer designs of wireless CR networks endowed with the much needed robustness to state uncertainty.Broader impacts: The broader impacts include tangible implications to mobile ad hoc, smart grid, intelligent transportation networks, as well as medical telemetry, geo-monitoring, and surveillance systems. Advances in the foundations of kernel-based learning, kernel matrix completion, inference on graphs, and sampling-based scenario optimization, will benefit a gamut of research areas including social analytics, bio-informatics, medical imaging, and surveillance using sensor networks. Policy research integrated with technical components will encourage community embracing of novel CR technologies and open up investment and business opportunities yielding greater economic impact.Undergraduate Design Projects and Botball Competition, facilitated by the proposed Cognitive Spectrum Operations Testbed, will also benefit student training with hands-on experience in state-of-the-art learning, wireless systems on mobile robots, and network optimization subjects.