Secrecy Rate Maximization in Active RIS Enhanced Multi-User Cognitive Radio Networks

As IoT technology continues to advance, there is a growing trend of connecting more devices to the network. This presents an increased need for improved channel access and resource management. Cognitive radio technology allows cognitive users to access underutilized spectrum resources of primary users in a cognitive wireless network through a dynamic spectrum access mechanism, significantly improving the spectrum utilization. However, this process also brings about serious physical layer security issues. Hence, this paper presents a secure transmission system at the physical layer that leverages active reconfigurable intelligent surfaces (active RIS) to improve the connection between cognitive base stations and cognitive users, while simultaneously achieving resistance against interference. We present a communication system framework for MISO CRN, incorporating active RIS support in the presence of multiple eavesdropping users. Our goal is to develop a comprehensive optimization approach that aims to maximize the sum secrecy rate achieved by multiple cognitive users. We propose using an alternating optimization technique to address the non-convex nature of the optimization problem, and leveraging Semi-Definite Relaxation (SDR) and Successive Convex Approximation (SCA) algorithm for optimizing subproblems. Through simulations, we investigate how various parameters affect the secrecy capacity of cognitive users and demonstrate that our suggested active RIS-assisted system outperforms conventional passive RIS-assisted systems in terms of performance.