Análise de desempenho de NOMA baseado em Q-Learning em redes de retransmissão satélite-terrestre

Abstract

In this work, we analyze the performance of Q-learning-based Non-Orthogonal Multiple Access (NOMA) in Satellite-Terrestrial Relay Networks (STRNs), addressing key challenges in massive Internet of Things (IoT) communications. Specifically, we focus on energy efficiency and normalized throughput metrics in uplink scenarios. By integrating a distributed Q-learning algorithm with NOMA, IoT devices can autonomously optimize transmission parameters — such as time slots, channels, and power levels — enhancing overall network performance. The proposed scheme outperforms fixed-power strategies by achieving higher normalized throughput and energy efficiency under varying network densities, offering up to 73% improvement in energy efficiency. Simulation results validate the protocol’s effectiveness, demonstrating its potential for large-scale IoT deployments in STRNs through efficient power allocation and reduced collision rates.