Power Allocation Based on Federated Multiagent Deep Reinforcement Learning for NOMA Maritime Networks

The absence of established communication infrastructures at sea presents a significant hurdle for the development of the Internet of Things (IoT) in maritime environments. In this article, we propose a novel solution: a nonorthogonal multiple access (NOMA) maritime network employing power allocation facilitated by federated multiagent deep reinforcement learning (DRL). Conventional single-agent DRL algorithms encounter challenges, such as dimensionality explosion in real-world scenarios. We address this issue by extending these algorithms to incorporate multiple agents. Furthermore, to mitigate risks associated with centralized training, such as data leakage and network attacks, we adopt a federated learning (FL) framework for distributed training across multiple agents. By uploading only select parameters during training and keeping data locally, FL not only enhances algorithm convergence speed but also bolsters data privacy. Specifically, we introduce the federated multiagent deep Q-network (FLMADQN) algorithm tailored for power allocation in NOMA maritime networks. Our algorithm aims to maximize system throughput, optimize data transfer rates, and expedite convergence. Through extensive computer simulations, we validate the efficacy of our proposed approach. Results demonstrate that the FLMADQN algorithm significantly outperforms the traditional DRL algorithm, DQN, in NOMA maritime environments, improving average system throughput by 20.12%, peak system throughput by 45.10%, and system spectral efficiency by 48.33%. Moreover, FLMADQN exhibits a twofold increase in convergence speed compared to MADQN without FL and is 2.5 times faster than DQN. Our findings underscore the potential of federated multiagent DRL in advancing communication systems for maritime IoT applications.