Optimization of Energy Consumption in 5G Networks Using Learning Algorithms in Reinforcement Learning


  • Daffa Dean Naufal Universitas Muhammadiyah Prof DR HAMKA
  • Harry Ramza Universitas Muhammadiyah Prof DR HAMKA
  • Emilia Roza Universitas Muhammadiyah Prof DR HAMKA




Algorithms, Energy, Long Term Evolution, Reinforcement Learning, 5G


The 5G network is an evolution of the 4G LTE (Long Term Evolution) 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.


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