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Research on Resource Allocation Method of Space Information Networks Based on Deep Reinforcement Learning

Science and Technology on Complex Electronic System Simulation Laboratory, Space Engineering University, Beijing 101416, China
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Remote Sens. 2019, 11(4), 448; https://doi.org/10.3390/rs11040448
Received: 16 January 2019 / Revised: 16 February 2019 / Accepted: 18 February 2019 / Published: 21 February 2019
The space information networks (SIN) have a series of characteristics, such as strong heterogeneity, multiple types of resources, and difficulty in management. Aiming at the problem of resource allocation in SIN, this paper firstly establishes a hierarchical and domain-controlled SIN architecture based on software-defined networking (SDN). On this basis, the transmission, caching, and computing resources of the whole network are managed uniformly. The Asynchronous Advantage Actor-Critic (A3C) algorithm in deep reinforcement learning is introduced to model the process of resource allocation. The simulation results show that the proposed scheme can effectively improve the expected benefits of unit resources and improve the resource utilization efficiency of the SIN. View Full-Text
Keywords: space information networks; software-defined network; deep reinforcement learning; transmission resource; caching resource; computing resource space information networks; software-defined network; deep reinforcement learning; transmission resource; caching resource; computing resource
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Meng, X.; Wu, L.; Yu, S. Research on Resource Allocation Method of Space Information Networks Based on Deep Reinforcement Learning. Remote Sens. 2019, 11, 448.

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