A reinforcement learning based algorithm towards energy efficient 5G multi-tier network

Nahina Islam, Ammar Alazab, Mamoun Alazab

Research output: Chapter in Book/Report/Conference proceedingConference Paper published in Proceedings

Abstract

Energy efficiency is a key factor in the next generation wireless communication systems. Sleep mode implementation in multi-tier 5G networks has proven to be a very good approach for improving the energy efficiency. In this paper, we propose a novel reinforcement learning based decision making algorithm to implement sleep mode in the base stations (BSs) used in multi-tier 5G networks. We propose a Markovian Decision process (MDP) based algorithm to switch between three different power consumption modes of a BS for improving the energy efficiency of the 5G network. The MDP based approach intelligently switches between the states of the BS based on the offered traffic whilst maintaining a prescribed minimum channel rate per user. Our results show that there is a significant gain in the energy efficiency when using our proposed MDP algorithm together with the three-state BSs. We have also shown the energy-delay tradeoff in order to design a delay aware network.

Original languageEnglish
Title of host publicationProceedings - 2019 Cybersecurity and Cyberforensics Conference, CCC 2019
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages96-101
Number of pages6
ISBN (Electronic)9781728126005
DOIs
Publication statusPublished - 1 May 2019
Event2019 Cybersecurity and Cyberforensics Conference, CCC 2019 - Melbourne, Australia
Duration: 7 May 20198 May 2019

Publication series

NameProceedings - 2019 Cybersecurity and Cyberforensics Conference, CCC 2019

Conference

Conference2019 Cybersecurity and Cyberforensics Conference, CCC 2019
CountryAustralia
CityMelbourne
Period7/05/198/05/19

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  • Cite this

    Islam, N., Alazab, A., & Alazab, M. (2019). A reinforcement learning based algorithm towards energy efficient 5G multi-tier network. In Proceedings - 2019 Cybersecurity and Cyberforensics Conference, CCC 2019 (pp. 96-101). [8854559] (Proceedings - 2019 Cybersecurity and Cyberforensics Conference, CCC 2019). IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/CCC.2019.000-2