Optimization of Energy Consumption in 5G Networks Using Learning Algorithms in Reinforcement Learning
DOI:
https://doi.org/10.57152/malcom.v3i2.959Keywords:
Algorithms, Energy, Long Term Evolution, Reinforcement Learning, 5GAbstract
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.
References
[Wijaya, Anggie. "Perkembangan Teknologi 5G." Universitas Pendidikan Indonesia 1, no. 1 (2021): 2-5.
A. G. Marques, et al. (2020). "Energy Efficiency in 5G Mobile Networks: A Survey of the State of the Art and Future Directions." IEEE Communications Surveys & Tutorials, 22(3), 1898-1939.
S. Wang, et al. (2019). "Energy-Efficient Resource Allocation in 5G Networks: Insights and Challenges." IEEE Network, 33(3), 123-129.
Q. Ye, et al. (2019). "Deep Reinforcement Learning for Dynamic Spectrum Access in Cellular Network." IEEE Transactions on Cognitive Communications and Networking, 5(2), 221-233.
Lestari, Maudy Andreana, Ahmad M. Ramli, and Tasya Safiranita Ramli. "TELAAH YURIDIS PENYELENGGARAAN TEKNOLOGI 5G DI INDONESIA: LANGKAH TRANSFORMASI MENUJU ERA SOCIETY 5.0." Citizen: Jurnal Ilmiah Multidisiplin Indonesia 2, no. 1 (2022): 129-137.
Series, M. "IMT Vision–Framework and overall objectives of the future development of IMT for 2020 and beyond." Recommendation ITU 2083, no. 0 (2015).
Carfora, Alfonso, Rosaria Vega Pansini, and Giuseppe Scandurra. "The causal relationship between energy consumption, energy prices and economic growth in Asian developing countries: A replication." Energy Strategy Reviews 23 (2019): 81-85.
Russell, S. J., and P. Norvig. "Artificial intelligence: a modern approach. 2016: Malaysia."
Somvanshi, Madan, Pranjali Chavan, Shital Tambade, and S. V. Shinde. "A review of machine learning techniques using decision tree and support vector machine." In 2016 international conference on computing communication control and automation (ICCUBEA), pp. 1-7. IEEE, 2016.
Das, Soubhik, and Manisha J. Nene. "A survey on types of machine learning techniques in intrusion prevention systems." In 2017 international conference on wireless communications, signal processing and networking (WiSPNET), pp. 2296-2299. IEEE, 2017.
Mahmud, Mufti, Mohammed Shamim Kaiser, Amir Hussain, and Stefano Vassanelli. "Applications of deep learning and reinforcement learning to biological data." IEEE transactions on neural networks and learning systems 29, no. 6 (2018): 2063-2079.
Qiang, Wang, and Zhan Zhongli. "Reinforcement learning model, algorithms and its application." In 2011 International Conference on Mechatronic Science, Electric Engineering and Computer (MEC), pp. 1143-1146. IEEE, 2011.
Mohammadi, Mehdi, Ala Al-Fuqaha, Mohsen Guizani, and Jun-Seok Oh. "Semisupervised deep reinforcement learning in support of IoT and smart city services." IEEE Internet of Things Journal 5, no. 2 (2017): 624-635.
Sharma, Akanksha Rai, and Pranav Kaushik. "Literature survey of statistical, deep and reinforcement learning in natural language processing." In 2017 International conference on computing, communication and automation (ICCCA), pp. 350-354. IEEE, 2017.
Wu, Zheng, Naimul Mefraz Khan, Lei Gao, and Ling Guan. "Deep reinforcement learning with parameterized action space for object detection." In 2018 IEEE International Symposium on Multimedia (ISM), pp. 101-104. IEEE, 2018.
Wang, Yong, Huachun Tan, Yuankai Wu, and Jiankun Peng. "Hybrid electric vehicle energy management with computer vision and deep reinforcement learning." IEEE Transactions on Industrial Informatics 17, no. 6 (2020): 3857-3868.
Malta, Silvestre, Pedro Pinto, and Manuel Fernández-Veiga. "Using Reinforcement Learning to Reduce Energy Consumption of Ultra-Dense Networks With 5G Use Cases Requirements." IEEE Access 11 (2023): 5417-5428.
Giannopoulos, Anastasios, Sotirios Spantideas, Nikolaos Kapsalis, Panagiotis Karkazis, and Panagiotis Trakadas. "Deep reinforcement learning for energy-efficient multi-channel transmissions in 5G cognitive hetnets: Centralized, decentralized and transfer learning based solutions." IEEE Access 9 (2021): 129358-129374.
