User Grouping and Power Allocation in NOMA Systems: A Reinforcement Learning-Based Solution

Abstract

In this paper, we present a pioneering solution to the problem of user grouping and power allocation in Non-Orthogonal Multiple Access (NOMA) systems. There are two fundamentally salient and difficult issues associated with NOMA systems. The first involves the task of grouping users together into the pre-specified time slots. The subsequent second phase augments this with the solution of determining how much power should be allocated to the respective users. We resolve this with the first reported Reinforcement Learning (RL)-based solution, which attempts to solve the partitioning phase of this issue. In particular, we invoke the Object Migration Automata (OMA) and one of its variants to resolve the user grouping problem for NOMA systems in stochastic environments. Thereafter, we use the consequent groupings to infer the power allocation based on a greedy heuristic. Our simulation results confirm that our solution is able to resolve the issue accurately, and in a very time-efficient manner.