Latency Optimization for Multi-user NOMA-MEC Offloading Using Reinforcement Learning

Both non-orthogonal multiple access (NOMA) and mobile edge computing (MEC) have been recognized as important techniques in future wireless networks, and the combination of them has received attention recently. It has been demonstrated that in a dual-user scenario, the use of the NOMA can effectively reduce the latency and energy consumption of MEC offloading. However, the scenario of multiple users needs to be considered further, which is more practical. In this paper, we consider a NOMA-MEC system with multiple users and single MEC server, and investigate the problem of minimizing offloading latency. Through using the Reinforcement learning (RL) algorithm Deep Q-network (DQN) to select the users who offload at the same time without knowing the actions of other users in advance, we will obtain the optimal user combination state and minimize system offloading latency. Simulation results show that the proposed method can significantly reduce the system offloading latency in the multi-user scenario of applying NOMA to MEC.