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
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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.
| Item Type: | Article |
|---|---|
| Subjects: | T Technology > TK Electrical engineering. Electronics Nuclear engineering |
| Divisions: | Fakultas Teknik > Teknik Elektro |
| Depositing User: | Harry Ramza |
| Date Deposited: | 20 Jan 2024 08:22 |
| Last Modified: | 20 Jan 2024 08:22 |
| URI: | http://repository.uhamka.ac.id/id/eprint/30835 |
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