Wireless communication technology is rapidly transcending its primary applications (e.g., cellular communications) and plays a transformative role as the critical backbone in various emerging new technologies that affect different technological, social, and economic domains. All such technologies are viable at the expense of increasing demands for radio spectrum, which is the key commodity in the wireless industry. Enabling long-term and sustainable wireless infrastructures necessitates technologies that enhance spectrum access efficiency and allow for real-time and fine-granularity spectrum management. The effectiveness of spectrum management critically depends on forming agile and accurate situational awareness of spectrum occupancy states across time and space. This research project focuses on developing a comprehensive analytical framework for forming situational awareness of the spatio-temporal variations in wireless spectrum opportunities. This framework establishes bi-directional interactions with the sensing circuitry. In the forward direction, it receives measurements collected by the sensors in order to form various detection, inferential, and resource management decisions. In the reverse direction, based on the data deemed necessary for delivering accurate situational awareness, it sends instructions to the sensors on how to proceed with collecting data from various segments of the spectrum. The decisions formed by this framework can also be used for visualizing decisions and alarms. The research tasks of this project include: developing a comprehensive framework that coherently integrates several critical components for innovative spectrum sensing, forming agile inferential decisions, achieving fast computations and fast adaptivity in dynamic settings, and ensuring decision robustness under environment uncertainties.

To develop such a comprehensive framework, this project has several research thrusts, each addressing one aspect of the framework. In each thrust, the project pursues the following two intertwined research objectives: (1) Theoretical Foundations: The first objective is to establish the fundamental limits of forming quickest situational awareness, while recognizing the physical restraints due to the cost of sensing, communication delay tolerance, model uncertainties, and dynamic variations of the beneficiaries of spectrum sensing, which encompass various autonomous wireless applications that operate under distinct protocols and infrastructures. (2) Algorithm Design: Driven by the observations gained from the uncovered fundamental limits, the second objective is to design data analytic tools and mechanisms for forming the quickest situational awareness, which is amenable to real-time and scalable implementation, exhibiting sound decision guarantees, and fully adaptive and responsive to the spatio-temporal variations of the spectrum occupancy status in wideband spectrum. This project will produce a decision-theoretic framework for forming quick and reliable situational awareness of the spectral occupancy states over different segments in wideband spectrum. To achieve this goal, the project will address long-standing open problems in quickest detection of change over networked data, open problems in controlled sensing, as well as challenging problems motivated by the physical constraints of spectrum sensing such as 1) sensing budget, 2) decision-making under model uncertainty, and 3) detecting adversaries that seek to compromise sensing data and distort spectrum sensing decisions.

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.

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Rensselaer Polytechnic Institute
United States
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