CNS Core: Small: Secured Spectrum Allocation and Patrolling in Shared Spectrum Systems

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The RF spectrum is a natural resource in great demand and a tremendous economic driver. Shared spectrum systems, which facilitate spectrum allocation to unlicensed users without harming the licensed users, offer great promise in optimizing spectrum utility. However, spectrum management in such systems is challenging mainly due to the lack of knowledge of signal propagation characteristics in the geographic areas. Thus, spectrum allocation in such systems is either done very conservatively or is based on imperfect models. Efficient spectrum utilization also depends on the ability to guard against unauthorized usage attacks; however, the current techniques are limited. The goal of this project is to develop effective spectrum management techniques by leveraging a large number of spectrum sensors in a crowdsensed architecture. In general, improved spectrum efficiency could impact many diverse fields such as IoT, education, cyber-physical systems security, healthcare, media and entertainment, which have tremendous future spectrum needs and innovation is perhaps halted due to lack of enough bandwidth. Thus, improved spectrum utilization via efficient allocation and patrolling is bound to yield significant economic benefits. This project is expected to provide technology inputs for industry and inform regulators about best practices and tradeoffs.This project will develop efficient and secured techniques for spectrum allocation and patrolling, for a shared spectrum system, by leveraging a large number of crowdsourced spectrum sensors. The project’s focus will be on using deep-learning techniques to obviate the need to assume a propagation model. The project has the following research themes: 1. Spectrum Allocation. The project will develop efficient supervised learning techniques to learn the spectrum allocation function, in general settings. 2. Privacy Preserving Spectrum Management Protocols. To maximize participation in a crowdsourced sensing model, it is imperative to preserve privacy of the entities involved in the spectrum management. Thus, the project will develop efficient cryptographic protocols that provide privacy to all system entities. 3. Spectrum Patrolling. Preventing unauthorized access of the shared spectrum is key to improved spectrum utilization. Thus, the project will develop efficient techniques to protect the spectrum from unauthorized access, by developing effective deep learning approaches to localize intruders. 4. Evaluation. The project will evaluate the overall system, over three simulation platforms including: (i) small indoor and outdoor testbeds, and (ii) a simulation platform that will use channel-state data gathered by drones.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.