RIS-Enabled UAV Cognitive Radio Networks: Trajectory Design and Resource Allocation

Abstract

Due to its flexible deployment and high mobility of unmanned aerial vehicles (UAVs), the UAV-assisted cognitive radio (CR) network has attracted a lot of attention as one of the most promising techniques to address spectrum congestion issues in futuristic networks. However, its performance can be severely affected by the blocked line-of-sight (LoS) channel in its air-to-ground (A2G) links. In this paper, we propose a UAV CR system enabled by a reconfigurable intelligent surface (RIS), which helps to reconstruct reliable links in UAV-assisted cognitive radio (CR) networks. Our goal is to maximize the achievable rate of a secondary receiver (SR) through the proper selection of the UAV trajectory, transmit power, and RIS phase shifts based on a given interference temperature threshold and other practical constraints. In addition, we solve the corresponding non-convex optimization problem using block coordinate descent (BCD) and successive convex approximation (SCA) algorithms. The simulation results will demonstrate the effectiveness of the proposed algorithms.