Deep Reinforcement Learning-Based Resource Allocation for Satellite Internet of Things with Diverse QoS Guarantee

• Main Authors: Hu, G. (Author), Li, Y. (Author), Pan, Z. (Author), Tang, S. (Author), Wu, Y. (Author)
• Format: Article
• Language: English
• Published: MDPI 2022
• Subjects: channel allocation; Channel allocation; Deep learning; deep reinforcement learning; Efficiency; Internet of things; Onboard training; power control; Power control; Power-control; Power-efficiency; QoS requirements; Quality of service; Reinforcement learning; Resource allocation; Resources allocation; Satellite internet; Satellite internet of thing; Satellite Internet of Things; Satellites; transfer learning; Transfer learning; various QoS; Various QoS
• Online Access: View Fulltext in Publisher

 Large-scale terminals’ various QoS requirements are key challenges confronting the resource allocation of Satellite Internet of Things (S-IoT). This paper presents a deep reinforcement learning-based online channel allocation and power control algorithm in an S-IoT uplink scenario. The intelligent agent determines the transmission channel and power simultaneously based on contextual information. Furthermore, the weighted normalized reward concerning success rate, power efficiency, and QoS requirement is adopted to balance the performance between increasing resource efficiency and meeting QoS requirements. Finally, a practical deployment mechanism based on transfer learning is proposed to promote onboard training efficiency and to reduce computation consumption of the training process. The simulation demonstrates that the proposed method can balance the success rate and power efficiency with QoS requirement guaranteed. For S-IoT’s normal operation condition, the proposed method can improve the power efficiency by 60.91% and 144.44% compared with GA and DRL_RA, while its power efficiency is only 4.55% lower than that of DRL-EERA. In addition, this method can be transferred and deployed to a space environment by merely 100 onboard training steps. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.