A DRL-Based NOMA Power Allocation Scheme for LEO Satellite Networks

Satellite networks provide higher coverage and provide ubiquitous mobile services. However, how to allocate precious satellite spectrum resources to improve better network performance has become an important issue in satellite networks. In this paper, we study the power allocation problem of the Low Earth Orbit (LEO) satellite to maximize the Supply-Demand Ratio (SDR) of the LEO satellite users while minimize the standard deviation (SD) of LEO satellite users’ SDR. We propose an Event-Driven Deep Reinforcement Learning based Power Allocation Mechanism (EDRL-PAM), which utilizes the LEO satellite’s power manager to intelligently allocate the power request for each cell of the LEO satellite. Furthermore, we propose a NOMA-based power allocation algorithm for allocating power to all LEO satellite users within the cells. The proposed EDRL-PAM fully utilizes a Deep Reinforcement Learning (DRL) technique, Deep Deterministic Policy Gradient (DDPG), to deal with stochastic arrivals of power requests in the power manager to achieve long-term optimization of the network performance. The simulation results show that our proposed EDRL-PAM can significantly improve the average data rate, and achieve the long-term optimization and fairness of network performance for satellite users.