Unmanned Aerial Vehicle Assisted Task Unloading and Resource Allocation Algorithm Based on NOMA System

In order to improve the flexibility of transmission, for the mobile edge computing (MEC) system assisted by multiple UAVs, an energy-saving UAV MEC framework combined with non orthogonal multiple access (NOMA) is proposed, in which multiple UAVs are deployed as edge servers to provide computing assistance for ground users, and NOMA is used to reduce the energy consumption of task unloading. Considering the User grouping、 joint optimization of computing resources and drone trajectory planning under given computing tasks and time, the weighted system energy consumption is minimized. Due to the coupling of parameters, minimizing energy consumption is a highly non-convex problem. Therefore, the problem is decomposed into three more manageable sub problems, namely user grouping, optimal allocation of computing resources for a given drone trajectory, and trajectory planning based on a given resource allocation scheme. Use matching game algorithm to solve grouping problems separately, and then convert these two problems into convex problems through CVX and quadratic approximation. Then, an effective iterative algorithm is proposed to alternately solve these two sub problems, gradually approaching the optimal resource management of the proposed system.