Work-in-Progress: AI Based Resource and Power Allocation for NOMA Systems

Non-orthogonal multiple access (NOMA) is a promising technology to meet the challenging requirements of 5G services by providing spectral efficient resource utilization. As the number of IoT devices increases significantly, NOMA becomes more important to support the massive machine type communication (mMTC) service, where, a huge amount of devices is simultaneously connected to the network. In this paper, we develop three different artificial intelligence (AI) based resource and power allocation algorithms, namely Genetic Algorithm (GA), Simulated Annealing (SA), and Hill Climbing (HC), for downlink NOMA systems. In the proposed approach, one of the AI algorithms is used to determine the NOMA user groups along with the frequency resource block for each group. Then, the optimum power allocation is performed to maximize the geometric mean of the user throughputs. The simulation experiments are performed to compare and contrast the performance of these three AI algorithms. The numerical results demonstrate that the GA provides the best results while the HC performs the worst.