- Title
- A Reinforcement learning based algorithm towards energy efficient 5G Multi-tier network
- Creator
- Islam, Nahina; Alazab, Ammar; Alazab, Mamoun
- Date
- 2019
- Type
- Text; Conference proceedings
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/180822
- Identifier
- vital:15817
- Identifier
-
https://doi.org/10.1109/CCC.2019.000-2
- Identifier
- ISBN:978-1-7281-2600-5
- 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.
- Publisher
- IEEE
- Relation
- 2019 Cybersecurity and Cyberforensics Conference (CCC); Melbourne, Vic; 8th-9th May, 2019 p. 96-101
- Rights
- All metadata describing materials held in, or linked to, the repository is freely available under a CC0 licence
- Rights
- Copyright IEEE
- Subject
- 5G mobile communication; Base stations; Delays; Energy Efficient 5G networks; Green communication; Markov decision process; Markov processes; Power demand; Quality of service; Reinforcement based learning; Sleep mode; Switches
- Full Text
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