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Enhancing 5G Wireless Network Security with Reconfigurable Intelligent Surfaces

Northern Virginia Node

Principal Investigator: 
Kai Zeng, associate professor, Department of Electrical and Computer Engineering, George Mason University.

Co-Principal Investigators: 
Brian Mark, professor, electrical and computer engineering, GMU; Lingjia Liu, associate professor, electrical and computer engineering, Virginia Tech; Changqing Luo, assistant professor, computer science, Virginia Commonwealth University

Project Description: 
The concept of reconfigurable intelligent surface (RIS) has recently gained much research attention for beyond 5G technology applications. RISs are man-made surfaces of electromagnetic material that are electronically controlled with integrated electronics and have unique wireless communication capabilities. Although key benefits of RISs in beyond 5G applications have been demonstrated for enhanced capacity, spectral efficiency, and higher rates, its benefits for enhancing 5G wireless network security have not been well understood or explored. This project aims to build theoretical foundations and develop novel techniques for RIS-enhanced 5G wireless security that fully harvest the degrees of freedom provided by RISs. It consists of four research thrusts: 1) to develop a reinforcement learning (RL)-agent assisted physical layer key generation scheme for RIS-enhanced 5G wireless communications; 2) to investigate the use of LPD/LPI waveforms in conjunction with RIS to improve the security of 5G communications and increase spectrum utilization as a cognitive underlay; 3) to design efficient RIS-transmitter/receiver channel state information (CSI) estimation methods to support secure communication; 4) to build an RIS mmWave 5G testbed to evaluate and validate the proposed ideas. The proposed research is well aligned with the strategic focus areas at the CCI NoVa Node, including Strategic Focus Area 1.A.ii: Cyber Research for the National Defense and Initiative 1.B.i: 5G-enabled Test Beds. Part of the research results will be further developed as training and course materials. The developed testbed will also be made available to the CCI partners for training, teaching, and research activities.