An explosion of low-cost wireless devices promises new applications and services in diverse domains, including health, transportation, energy, manufacturing, and entertainment. This project focuses on developing energy and spectrum-efficient, distributed multi-access strategies for dynamic and large-scale wireless networks under the stringent energy and delay requirements that are expected in emerging applications. This work will enable the development of a multitude of technologies that can improve the life of society-at-large. For example, this work can support the next generation of communication technologies for large-scale Internet of Things (IoT) applications and autonomous vehicle applications. Moreover, education is a core component of this project. New theories and algorithms developed in this project are integrated into the graduate-level courses at the three universities. Undergraduate and graduate students are involved in the project through the undergrad capstone and masters graduation projects at the Ohio State University.

This project explores the fundamental energy and spectrum-efficiency tradeoff of distributed spectrum access methods, and develops adaptive and correlated strategies that embrace and control randomness with efficiency guarantees for dynamic users with delay-sensitive traffic. In addition, the design incorporates humans into the loop by observing how humans react in simple multi-access games, providing simple human behavior models and simple human-perceived quality metrics, and by designing methods that can adapt to unexpected events or actions. A combined analysis and implementation approach of this project exploits high-dimensionality in the system while also overcoming difficulties for large-scale implementation and testing. In particular, the project develops mean-field techniques and analyses for large-scale spectrum access. Novel real-world experimentation strategies developed in this project emulate large-scale system operation in a small testbed by utilizing the simplification due to our randomized solutions and the integration of the aforementioned mean-field methods.

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.

National Science Foundation (NSF)
Division of Computer and Network Systems (CNS)
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Murat Torlak
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Regents of the University of Michigan - Ann Arbor
Ann Arbor
United States
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