Joint Trajectory, Sensing, and Transmission Design for IRS-Assisted Cognitive UAV Systems

In this letter, the novel intelligent reflecting surface (IRS)-assisted cognitive unmanned aerial vehicle systems (CUAVS) are investigated, where the UAV serves as a mobile node sensing spectrum state of primary user (PU) and transmitting data to ground secondary users (SUs), while IRS is exploited to assist both the UAV sensing and transmission in CUAVS. Our aim is to maximize the effective throughput of CUAVS by jointly optimizing the spectrum sensing duration and the UAV's trajectory, as well as IRS phase shift matrices in the sensing and transmission stages, where UAV needs to balance its relative location among IRS, primary transmitter, PU and SUs. To solve this challenging optimization problem, the original problem is divided into four sub-problems, and these sub-problems are solved by exploiting the bisection search, semidefinite relaxation, Gaussian randomization and successive convex approximation techniques, respectively. Numerical results demonstrate that our proposed design can significantly enhance the effective throughput of CUAVS.