  In this paper, we investigate the joint user pairing and association problem
for multicell non-orthogonal multiple access (NOMA) systems. We consider a
scenario where the user equipments (UEs) are located in a multicell network
equipped with multiple base stations. Each base station has multiple orthogonal
physical resource blocks (PRBs). Each PRB can be allocated to a pair of UEs
using NOMA. Each UE has the additional freedom to be served by any one of the
base stations, which further increases the complexity of the joint user pairing
and association algorithm design. Leveraging the recent success on using
machine learning to solve numerical optimization problems, we formulate the
joint user pairing and association problem as a combinatorial optimization
problem. The solution is found using an emerging deep learning architecture
called Pointer Network (PtrNet), which has a lower computational complexity
compared to solutions based on iterative algorithms and has been proven to
achieve near-optimal performance. The training phase of the PtrNet is based on
deep reinforcement learning (DRL), and does not require the use of the optimal
solution of the formulated problem as training labels. Simulation results show
that the proposed joint user pairing and association scheme achieves
near-optimal performance in terms of the aggregate data rate, and outperforms
the random user pairing and association heuristic by up to 30%.
