Relay Selection for Cooperative NOMA Systems Based on the DQN Algorithm

Abstract

In this study, a solution based on deep Q network (DQN) is proposed to address the relay selection problem in cooperative non-orthogonal multiple access (NOMA) systems. DQN is particularly effective in addressing problems within dynamic and complex communication environments. By formulating the relay selection problem as a Markov decision process (MDP), the DQN algorithm employs deep neural networks (DNNs) to learn and make decisions through real-time interactions with the communication environment, aiming to minimize the system’s outage probability. During the learning process, the DQN algorithm progressively acquires channel state information (CSI) between two nodes, thereby minimizing the system’s outage probability until a stable level is reached. Simulation results show that the proposed method effectively reduces the outage probability by 82% compared to the two-way relay selection scheme (Two-Way) when the signal-to-noise ratio (SNR) is 30 dB. This study demonstrates the applicability and advantages of the DQN algorithm in cooperative NOMA systems, providing a novel approach to addressing real-time relay selection challenges in dynamic communication environments.

Keywords

deep Q network (DQN) / cooperative non-orthogonal multiple access (NOMA) / relay selection / outage probability