This project develops a novel online learning based framework for distributed low-cost devices to efficiently and effectively access the shared spectrum without spectrum sensing. It specifically focuses on no-sensing devices that do not have the powerful radio-frequency (RF) components to enable wideband spectrum sensing, and addresses the cross-technology spectrum access problem in a decentralized setting. A pertinent application the proposed solution addresses is the dynamic spectrum access of Internet-of-Things (IoT) devices that are deployed in either unlicensed or lightly licensed spectrum, in which the distributed IoT devices need to coexist with other active systems. The no-sensing spectrum access and sharing framework has the potential to revolutionize the operation and management of modern and future wireless networks, considerably enhance the spectrum utilization efficiency, and dramatically alleviate the constantly increasing pressure on the limited radio spectrum. The cross disciplinary nature of the research would naturally translate into case studies and projects in a number of undergraduate and graduate level courses taught by the PIs in areas of communications, machine learning, and networking.
This project aims to develop a suite of online learning based spectrum access algorithms for no-sensing devices to coexist with other active systems. The first study focuses on improving the learning efficiency by introducing the best arm identification framework and proposing meta-learning and good channel identification algorithms. The second thrust is devoted to designing spectrum access mechanisms that can seamlessly integrate hybrid automatic repeat request (HARQ). Novel algorithms will be designed to learn the optimal sequence of channels for possible retransmissions, and enhanced for fine-grained control that captures the coding level behavior of HARQ. The last thread of investigation considers multi-user multi-technology coexistence and will develop implicit-communication based distributed spectrum access algorithms. Finally, a thorough validation of the algorithms and spectrum access schemes will be performed using a lab testbed and real-world datasets.
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.
|Effective start/end date||9/15/20 → 8/31/23|
- National Science Foundation: $220,000.00