Joint User Clustering, Resource Allocation and Power Control for NOMA-based Mobile Edge Computing

Mobile edge computing (MEC) has been considered as a promising technology to reduce the energy consumption and task execution latency of smart mobile devices (SMDs). To improve the number of user access and spectrum efficiency, we exploit non-orthogonal multiple access (NOMA) into the MEC system. NOMA will cause mutual interference between users, which will increase user's transmission latency. Therefore, we formulate an optimization problem which minimizes the average latency of the NOMA-based MEC system via jointly optimizing user clustering, resource allocation and transmission power. To solve the optimization problem, we first propose heuristic algorithms to solve user clustering and resource allocation. Then, we propose an iterative power optimization algorithm based on particle swarm optimization (PSO). Simulation results show that the proposed optimization strategy can effectively reduce the average latency of the system.