Deep Q-Learning Aided Networking, Caching, and Computing Resources Allocation in Software-Defined Satellite-Terrestrial Networks | IEEE Journals & Magazine | IEEE Xplore

Deep Q-Learning Aided Networking, Caching, and Computing Resources Allocation in Software-Defined Satellite-Terrestrial Networks


Abstract:

With the development of satellite networks, there is an emerging trend to integrate satellite networks with terrestrial networks, called satellite-terrestrial networks (S...Show More

Abstract:

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.
Published in: IEEE Transactions on Vehicular Technology ( Volume: 68, Issue: 6, June 2019)
Page(s): 5871 - 5883
Date of Publication: 27 March 2019

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