Joint User Pairing and Association for Multicell NOMA: A Pointer Network-based Approach
This addresses a complex resource allocation problem in wireless communication networks, offering a more efficient solution for network operators, though it is incremental as it applies an existing machine learning method to a specific domain.
The paper tackles the joint user pairing and association problem in multicell NOMA systems by formulating it as a combinatorial optimization problem and solving it with a Pointer Network-based approach, achieving near-optimal performance and outperforming a random heuristic by up to 30% in aggregate data rate.
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%.