Deep Q-Learning Aided Networking, Caching, and Computing Resources Allocation in Software-Defined Satellite-Terrestrial Networks
IEEE Transactions on Vehicular Technology , Volume 68 - Issue 6 p. 5871- 5883
With the development of satellite networks, there is an emerging trend to integrate satellite networks with terrestrial networks, called satellite-Terrestrial networks (STNs). The improvements of STNs need innovative information and communication technologies, such as networking, caching, and computing. In this paper, we propose a software-defined STN to manage and orchestrate networking, caching, and computing resources jointly. We formulate the joint resources allocation problem as a joint optimization problem, and use a deep Q-learning approach to solve it. Simulation results show the effectiveness of our proposed scheme.
|caching, deep Q-learning, edge computing, network virtualization, resources allocation, Satellite-Terrestrial networks, software-defined networking (SDN)|
|IEEE Transactions on Vehicular Technology|
|Organisation||School of Information Technology|
Qiu, C. (Chao), Yao, H. (Haipeng), Yu, F.R, Xu, F. (Fangmin), & Zhao, C. (Chenglin). (2019). Deep Q-Learning Aided Networking, Caching, and Computing Resources Allocation in Software-Defined Satellite-Terrestrial Networks. IEEE Transactions on Vehicular Technology, 68(6), 5871–5883. doi:10.1109/TVT.2019.2907682