Energy-efficient User Clustering and Resource Management for NOMA Based MEC Systems

Recent years, mobile edge computing (MEC) has appeared as a promising technology for delay and energy minimization, and nonorthogonal multiple access (NOMA) has been recognized as a powerful solution to improving spectrum efficiency and system capacity. In order to capture the gains of the both, in this paper, we study the energy minimization issues in a NOMA based MEC system, and formulate an optimization problem via optimizing the user clustering, computation resource allocation, and transmit power control, with task processing latency deadline guaranteed. To solve the intractable problem, we first propose a heuristic algorithm to obtain user clustering and computation resource allocation. And then, based on a swarm intelligence algorithm, i.e., fireworks algorithm (FA), we propose a low-complexity scheme for transmit power control optimization. Simulation results demonstrate that our proposed algorithms could reduce the system energy consumption effectively compared with other existing schemes.