Deep Reinforcement Learning For Multi-User Access Control in Non-Terrestrial Networks

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Abstract
Non-Terrestrial Networks (NTNs) composed of space-borne (e.g., satellites) and airborne vehicles (e.g., drones and blimps) have recently been proposed by 3GPP as a new paradigm of infrastructures to enhance the capacity and coverage of existing terrestrial wireless networks. The mobility of non-terrestrial base stations (NT-BSs) however leads to a dynamic environment, which imposes unique challenges for handover and throughput optimization particularly in multi-user access control for NTNs. To achieve performance optimization, each terrestrial user equipment (UE) should autonomously estimate the dynamics of moving NT-BSs, which is different from the existing user access control schemes in terrestrial wireless networks. Consequently, new learning schemes for optimum multi-user access control are desired. In this article, we therefore propose a UE-driven deep reinforcement learning (DRL) based scheme, in which a centralized agent deployed at the backhaul side of NT-BSs is responsible for training the parameter of a deep Q-network (DQN), and each UE independently makes its own access decisions based on the parameter from the trained DQN. With the proposed scheme, each UE is able to access a proper NT-BS intelligently to enhance the long-term system throughput and avoid frequent handovers among NT-BSs. Through comprehensive simulation studies, we justify the performance of the proposed scheme, and show its effectiveness in addressing the fundamental issues in the NTNs deployment.
Funding Information
  • National Natural Science Foundation of China (61631005, U1801261)
  • National Key R&D Program of China (2018YFB1801105)
  • Key Areas of Research and Development Program of Guangdong Province, China (2018B010114001)
  • Fundamental Research Funds for the Central Universities (ZYGX2019Z022)
  • Programme of Introducing Talents of Discipline to Universities (B20064)
  • Ministry of Science and Technology (MOST 108-2221-E-194-020-MY3, 109-2218-E-009-005-)

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