User grouping and power allocation in NOMA systems: a novel semi-supervised reinforcement learning-based solution

In this paper, we present a pioneering solution to the problem of user grouping and power allocation in non-orthogonal multiple access (NOMA) systems. The problem is highly pertinent because NOMA is a well-recognized technique for future mobile radio systems. The salient and difficult issues associated with NOMA systems involve the task of grouping users together into the pre-specified time slots, which are augmented with the question of determining how much power should be allocated to the respective users. This problem is, in and of itself, NP-hard. Our solution is the first reported reinforcement learning (RL)-based solution, which attempts to resolve parts of this issue. In particular, we invoke the object migration automaton (OMA) and one of its variants to resolve the grouping in NOMA systems. Furthermore, unlike the solutions reported in the literature, we do not assume prior knowledge of the channels’ distributions, nor of their coefficients, to achieve the grouping/partitioning. Thereafter, we use the consequent groupings to heuristically infer the power allocation. The simulation results that we have obtained confirm that our learning scheme can follow the dynamics of the channel coefficients efficiently, and that the solution is able to resolve the issue dynamically.