Reinforcement learning approach to dynamic activation of base station resources in wireless networks

Peng Yong Kong, Dorin Panaitopol

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

21 Scopus citations

Abstract

Recently, the issue of energy efficiency in wireless networks has attracted much research attention due to the growing concern on global warming and operator's profitability. We focus on energy efficiency of base stations because they account for 80% of total energy consumed in a wireless network. In this paper, we intend to reduce energy consumption of a base station by dynamically activating and deactivating the modular resources at the base station depending on the instantaneous network traffic. We propose an online reinforcement learning algorithm that will continuously adapt to the changing network traffic in deciding which action to take to maximize energy saving. As an online algorithm, the proposed scheme does not require a separate training phase and can be deployed immediately. Simulation results have confirmed that the proposed algorithm can achieve more than 50% energy saving without compromising network service quality which is measured in terms of user blocking probability.

Original languageBritish English
Title of host publication2013 IEEE 24th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications, PIMRC 2013
Pages3264-3268
Number of pages5
DOIs
StatePublished - 2013
Event2013 IEEE 24th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications, PIMRC 2013 - London, United Kingdom
Duration: 8 Sep 201311 Sep 2013

Publication series

NameIEEE International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC

Conference

Conference2013 IEEE 24th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications, PIMRC 2013
Country/TerritoryUnited Kingdom
CityLondon
Period8/09/1311/09/13

Keywords

  • Energy efficient base station
  • Green wireless networks
  • Online Q-Learning
  • Reinforcement learning

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