RIS-Assisted Green and Secure Symbiotic AAV-MEC Network

The unmanned aerial vehicle (UAV) aided mobile edge computing (MEC) network has attracted significant attention due to its enhanced computing power, reliable network connectivity, dynamic environment adaptability, which however still suffer from non-instantaneous channel reconstruction and wireless security threats. Therefore, this paper considers a reconfigurable intelligent surface (RIS) assisted UAV-MEC network, aiming to minimize UAV energy consumption by jointly optimizing task offloading rate, user scheduling coefficient, RIS phase and UAV trajectory with the constraints of secure offloading rate. Given the multi-variable coupling and non-convex nature of this optimization problem, we decouple it into three subproblems, which include the user scheduling and offloading ratio optimization, the RIS phase optimization, and the UAV trajectory optimization. For the fractional programming problem involving RIS phase optimization, the Dinkelbach algorithm is used to transform it into a parametric subtraction problem, thus obtaining a closed-form solution for the RIS phases. Furthermore, the successive convex approximation (SCA) algorithm is employed for the UAV trajectory optimization subproblem. Ultimately, a double-layer iterative optimization algorithm based on block coordinate descent (BCD) is proposed to solve the original non-convex optimization problem. Simulation results confirm its superior performance in saving UAV energy compared to other baseline schemes.