Distributed Q-Learning Based Joint Relay Selection and Access Control Scheme for IoT-Oriented Satellite Terrestrial Relay Networks

In this letter, we propose a distributed Q-learning (DQL) based joint relay selection and access control (JRSAC) scheme for Internet of Things (IoT)-oriented satellite terrestrial relay networks (STRNs) with massive IoT devices and multiple relays. Firstly, a semi-random access (SRA) architecture is proposed to improve the learning efficiency of the DQL algorithm. Subsequently, a JRSAC optimization problem is formulated and solved by the proposed DQL algorithm. Simulation results show that the proposed DQL based JRSAC scheme significantly outperforms conventional schemes in terms of the medium access control (MAC) throughput, total access delay, and sum rate.