Reinforcement Learning Based Dynamic Basestation Orchestration for High Energy Efficiency
Computer Science

Computer Science ›› 2021, Vol. 48 ›› Issue (11): 363-371.doi: 10.11896/jsjkx.201000008

• Computer Network • Previous Articles     Next Articles

Reinforcement Learning Based Dynamic Basestation Orchestration for High Energy Efficiency

ZENG De-ze1, LI Yue-peng1, ZHAO Yu-yang1, GU Lin2   

  1. 1 School of Computer Science,China University of Geosciences,Wuhan 430074,China
    2 School of Computer Science and Technology,Huazhong University of Science and Technology,Wuhan 430074,China
  • Received:2020-10-03 Revised:2021-04-10 Online:2021-11-15 Published:2021-11-10
  • About author:ZENG De-ze,born in 1984,Ph.D,professor,Ph.D supervisor,is a member of China Computer Federation.His main research interests include edge computing and artificial intelligence.
    GU Lin,born in 1985,Ph.D,associate professor,is a member of China Computer Federation.Her main research interests include edge computing and artificial intelligence.
  • Supported by:
    National Natural Science Foundation of China(61772480,61972171,62073300) and Open Research Projects of Zhijiang Lab(2021KE0AB02).

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Abstract: The mutual promotion of mobile communication technology and mobile communication industry has achieved unprecedented prosperity in the mobile Internet era.The explosion of mobile devices,expansion of the network scale,improvement of service requirements are driving the next technological revolution in wireless networks.5G meets the requirements for the thousand-fold improvement of service performance through intensive network deployment,but co-channel interference and bursty request problems make the energy consumption of this solution very huge.In order to support 5G network to provide energy-efficient and high-performance services,it is imperative to upgrade and improve the management scheme of mobile networks.In this article,we use a short-cycle management framework with cache queues to achieve agile and smooth management of request burst scenarios to avoid dramatic fluctuations in service quality due to request bursts.We use deep reinforcement learning to learn the user distribution and communication needs,and infer the load change rules of the base station,and then realize the pre-scheduling and pre-allocation of energy,while ensuring the quality of service and improving the energy efficiency.Compared with the classic DQN algorithm,the two-buffer DQN algorithm proposed in this paper can provide nearly 20% acceleration in convergence.In terms of decision performance,it can save 4.8% energy consumption compared to the currently widely used keep on strategy.

Key words: Base station sleep, Deep reinforcement learning, Double buffer, Heterogeneous network, Mobile network management, Request burst

CLC Number: 

  • TP391
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