Ant-Colony Optimization for 5G NOMA User Grouping

Abstract

Non-orthogonal multiple access (NOMA) is essential for 5G networks in achieving improved fairness and throughput. Scheduling the massive number of users on NOMA systems and wireless networks lead to a rise in the computational loads, which can be reduced using heuristic algorithms. This paper proposes ant-colony optimization (ACO) for user grouping in NOMA systems for 5G networks. The number of resource blocks is increased from one up to twenty to reflect the increase in the number antennas in the large antenna systems. It is observed that the mean throughput of the proposed scheme is improved, and the average mean square error per user is reduced as opposed to the existing schemes.