eprintid: 30835
rev_number: 6
eprint_status: archive
userid: 2950
dir: disk0/00/03/08/35
datestamp: 2024-01-20 08:22:37
lastmod: 2024-01-20 08:22:37
status_changed: 2024-01-20 08:22:37
type: article
metadata_visibility: show
creators_name: Ramza, Harry
creators_id: hramza@uhamka.ac.id
creators_orcid: 0000-0002-4126-8797
contributors_type: http://www.loc.gov/loc.terms/relators/AUT
contributors_type: http://www.loc.gov/loc.terms/relators/AUT
contributors_type: http://www.loc.gov/loc.terms/relators/AUT
contributors_name: Naufal, Daffa Dean
contributors_name: Ramza, Harry
contributors_name: Roza, Emilia
contributors_id: daffadean22@gmail.com
contributors_id: hramza@uhamka.ac.id
contributors_id: emilia_roza@uhamka.ac.id
title: Optimization of Energy Consumption in 5G Networks Using Learning Algorithms in Reinforcement Learning
ispublished: pub
subjects: TK
divisions: 20201
abstract: The 5G network is an evolution of the 4G Long Term Evolution (LTE) fast internet network that is widely adopted in smart
phones or gadgets. 5G networks offer faster wireless internet for various purposes. This research is a literature review of several articles related to machine learning, specifically regarding energy consumption optimization with 5G networks and reinforcement learning algorithms.The results show that various techniques have evolved to overcome the complexity of large energy intake including integration with 5G networks and algorithms have been completed by many researchers. Related to electricity consumption, it was found that during 5G use cases, in a low site visitor load scenario and while reducing power intake takes precedence over QoS, power savings can be made by 80% with 50 ms latency, 75% with 20 ms and 10 ms latency, and 20% with 1 ms latency. If QoS is prioritized, then power savings reach a maximum of five percent with minimum impact in terms of latency. Moreover, with regards to power performance, it has been observed that DQN-assisted motion can offer improvements.
date: 2023-10-22
date_type: completed
publisher: INSTITUT RISET DAN PUBLIKASI INDONESIA (IRPI)
official_url: https://journal.irpi.or.id/index.php/malcom
full_text_status: public
publication: MALCOM: Indonesian Journal of Machine Learning and Computer Science
volume: 3
number: 2
pagerange: 281-292
refereed: TRUE
issn: 2775-8575
funders: Universitas Muhammadiyah Prof. Dr. HAMKA
citation:   Ramza, Harry  (2023) Optimization of Energy Consumption in 5G Networks Using Learning Algorithms in Reinforcement Learning.  MALCOM: Indonesian Journal of Machine Learning and Computer Science, 3 (2).  pp. 281-292.  ISSN 2775-8575     
document_url: http://repository.uhamka.ac.id/id/eprint/30835/1/Optimization%20of%20Energy%20Consumption%20in%205G%20Networks%20Using%20Learning%20Algorithms%20in%20Reinforcement%20Learning.pdf