MEC Communication Resource Allocation Optimization Algorithm Based on NOMA

To solve the problem of communication delay and resource shortage when multiple users offload tasks at the same time in mobile edge computing (MEC), the deep reinforcement learning algorithm based on non-orthogonal multiple access (NOMA) technology was proposed to optimize users' communication resource allocation. Firstly, the taboo tag deep Q-network algorithm was used to train the relationship between users and subchannels at the users grouping stage, then the deep deterministic policy gradient algorithm was used to allocate users transmission power who sharing subchannel. The simulation results display that the proposed algorithm perform more stable than other reinforcement learning and traditional algorithm, moreover, the system sum rate have been significantly improved when multiple edge users offload tasks.