CRII: CNS: Towards Robust and Efficient Dynamic Spectrum Sharing with Knowledge Transfer

Old Dominion University Research Foundation, Norfolk VA

Investigators

Abstract

The proliferation of wireless devices and the increasing demand for wireless services, coupled with inefficient spectrum allocation and limited spectrum availability, exacerbate the scarcity of wireless spectrum resources in next-generation (NextG) networks. This project focuses on the development of novel transfer learning (TL) frameworks for dynamic spectrum sharing (DSS) to enable knowledge transfer across users, environments, and wireless systems, offering viable approaches to intelligently utilize underutilized licensed spectrum more effectively. DSS wireless systems exhibit characteristics such as dynamic environments, heterogeneous networks, massive connections, interference, high communication overhead, limited computing and storage capacity, as well as security and privacy concerns, making it challenging to learn and leverage transferable knowledge. Moreover, achieving the desired performance of knowledge transfer often requires substantial amounts of high-quality training data, while transferring data knowledge may raise security and privacy issues, limiting adaptation and generalization to other tasks. Therefore, this project aims to explore novel TL strategies for learning transferable knowledge and addressing concerns related to robustness, efficiency, security, and privacy in DSS systems. A key thrust of the project involves a systematic investigation into the characteristics and parameters of target DSS wireless systems, alongside an exploration of the fundamental principles, theories, and unique challenges associated with knowledge transfer. These studies aim to bridge the gap between system characteristics and algorithm development. The research tasks include the following: (1) Design an ensemble evaluation scheme to assess the robustness, efficiency, security, and privacy of TL-based DSS frameworks. (2) Develop efficient TL-based DSS frameworks for adaptive spectrum sensing, selection, access, and handoff. (3) Create robust security and privacy TL strategies for monitoring, detecting, mitigating, and preventing various malicious attacks, while also protecting sensitive data. Concurrently, the research team is developing a Wireless Knowledge Transfer testbed that incorporates transferable knowledge, evaluation schemes, pre-trained TL models, attack knowledge databases, and security and privacy strategies. This testbed helps to facilitate and standardize research on knowledge reuse in wireless communication systems. The integration of research and education plans prepares the NextG workforce in the fields of DSS, artificial intelligence, transfer learning, and cybersecurity. Outreach activities establish connections between the DSS research, and K-12 students, minority groups, and college students through various learning approaches. 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.