Securing Full-Duplex Cognitive Radio With Reconfigurable Intelligent Surfaces via Coordinated Beamforming and Power Control

Abstract

This paper addresses the unexplored challenge of achieving secure communication in a full-duplex (FD) cognitive radio (CR) system employing a reconfigurable intelligent surface (RIS) where a passive eavesdropper (Eve) is equipped with multiple antennas. The secondary base station (SBS), operating in full-duplex mode, serves as an uplink (UL) communication provider for secondary users (SUs) while actively launching a jamming signal against Eve to degrade its downlink (DL) interception capability, thus aiding the primary network. This study faces several technical challenges. First, complex interference management arises as the FD-SBS must manage interference between UL communications and active jamming signals to ensure both secure communication and minimal interference to legitimate users. Additionally, optimizing multi-dimensional beamforming across the primary base station (PBS) DL, the FD-SBS jamming, the passive RIS, and the UL communication power imposes substantial complexities due to conflicting objectives and constraints. To this end, we propose a coordinated beamforming approach, which maximizes the minimum secrecy rate with a minimum target rate for the UL SUs, a maximum DL PBS transmission power, jamming power limits and RIS unit modulus constraints. To address the problem non-convexity, it is decomposed into four sub-problems, which are solved via employing semidefinite programming (SDP) and successive convex approximation (SCA) based alternating optimization. Simulation results show the effectiveness of RIS phase shift optimization to enhance secrecy performance, how much jamming power is needed to keep balance between the secrecy performance and the UL communication service, as well as the effectiveness of the proposed solution against various benchmarks.

Keywords

• Reconfigurable intelligent surfaces (RIS)
• full-duplex cognitive radio systems
• multi-user single-input and multiple-output (MU-SIMO)
• coordinated beamforming
• alternative optimization