Smart Power Management with Small Cells: A Path to Sustainable Data Connectivity
Keywords:
Small Cell, Power Control, Energy Efficiency, Power DistributionAbstract
The rising demand for energy-efficient networks capable of supporting high-speed data traffic poses a critical challenge for network operators. This study addresses this issue by proposing a power control strategy that dynamically adjusts small cell transmit power based on traffic patterns. Power usage is reduced to 40% of the total capacity during normal traffic, while 60% is utilized during high traffic intensity. This traffic-driven power allocation approach achieves a 13-15% improvement in energy efficiency compared to conventional small cell-controlled sleep modes. By optimizing energy consumption without compromising network performance, this research provides a practical solution for balancing efficiency and user satisfaction in modern mobile networks.
References
R. G. Lopamudra Kundu, Xingqin Lin, “Towards Energy Efficient RAN: From Industry Standards to Trending Practice,” arXiv:2402.11993, 2024, doi: https://doi.org/10.48550/arXiv.2402.11993.
D. B. Josip Lorincz, Zvonimir Klarin, “Advances in Improving Energy Efficiency of Fiber–Wireless Access Networks: A Comprehensive Overview,” Sensors, vol. 24, no. 4, p. 2239, 2023, doi: https://doi.org/10.3390/s23042239.
and Y. I. D. W. Alshaibani, I. Shayea, R. Caglar, J. Din, “Mobility Management of Unmanned Aerial Vehicles in Ultra–Dense Heterogeneous Networks,” Sensors, vol. 22, no. 16, p. 6013, 2022, doi: https://doi.org/10.3390/s22166013.
A. K. Tiwari, P. K. Mishra, and S. Pandey, “Optimize Energy Efficiency Through Base Station Switching and Resource Allocation For 5g Heterogeneous Networks,” Int. J. Intell. Syst. Appl. Eng., vol. 11, no. 1s, pp. 113–119, Jan. 2023, Accessed: Jan. 04, 2025. [Online]. Available: https://www.ijisae.org/index.php/IJISAE/article/view/2483
A. El Amine, J. P. Chaiban, H. A. H. Hassan, P. Dini, L. Nuaymi, and R. Achkar, “Energy Optimization With Multi-Sleeping Control in 5G Heterogeneous Networks Using Reinforcement Learning,” IEEE Trans. Netw. Serv. Manag., vol. 19, no. 4, pp. 4310–4322, Dec. 2022, doi: 10.1109/TNSM.2022.3157650.
S. Ramesh, S. Nirmalraj, S. Murugan, R. Manikandan, and F. Al-Turjman, “Optimization of Energy and Security in Mobile Sensor Network Using Classification Based Signal Processing in Heterogeneous Network,” J. Signal Process. Syst., vol. 95, no. 2–3, pp. 153–160, Mar. 2023, doi: 10.1007/S11265-021-01690-Y/METRICS.
A. S. M. Azhar, “5G Networks: Challenges and Techniques for Energy Efficiency,” Eng. Technol. Appl. Sci. Res., vol. 8, no. 2, pp. 2864–2868, 2018, doi: https://doi.org/10.48084/etasr.1623.
P. Kaur, R. Garg, and V. Kukreja, “Energy-efficiency schemes for base stations in 5G heterogeneous networks: a systematic literature review,” Telecommun. Syst. 2023 841, vol. 84, no. 1, pp. 115–151, Jul. 2023, doi: 10.1007/S11235-023-01037-X.
H. Younis, M. Asad Arshed, F. ul Hassan, M. Khurshid, H. Ghassan, and M. Haseeb-, “Tomato Disease Classification using Fine-Tuned Convolutional Neural Network,” Int. J. Innov. Sci. Technol., vol. 4, no. 1, pp. 123–134, Feb. 2022, doi: 10.33411/IJIST/2022040109.
A. Shahzad, M. A. Arshed, F. Liaquat, M. Tanveer, M. Hussain, and R. Alamdar, “Pneumonia Classification from Chest X-ray Images Using Pre-Trained Network Architectures,” VAWKUM Trans. Comput. Sci., vol. 10, no. 2, pp. 34–44, Dec. 2022, doi: 10.21015/VTCS.V10I2.1271.
C. D. A. Afzal, F. Kanwal, A. Amjad, M. A. Arshed, “The Virtual Shift: Investigating Technology Adoption Factors for Remote Learning on Non-Educational Platforms during the COVID-19 Pandemic,” J. Comput. Biomed. Informatics, vol. 6, no. 1, pp. 195–207, 2023, [Online]. Available: https://www.researchgate.net/publication/377030225_The_Virtual_Shift_Investigating_Technology_Adoption_Factors_for_Remote_Learning_on_Non-Educational_Platforms_during_the_COVID-19_Pandemic
M. A. Arshed, A. Shahzad, K. Arshad, D. Karim, S. Mumtaz, and M. Tanveer, “Multiclass Brain Tumor Classification from MRI Images using Pre-Trained CNN Model,” VFAST Trans. Softw. Eng., vol. 10, no. 4, pp. 22–28, Nov. 2022, doi: 10.21015/VTSE.V10I4.1182.
C. J.-G. Shafiq M, Ali A, Ali F, “Machine Learning, Data Mining, and IoT Applications in Smart and Sustainable Networks,” Sustainability, vol. 16, no. 18, p. 8059, 2024, doi: https://doi.org/10.3390/su16188059.
M. F. R. B. & P. Das Faiaz Ahsan, Nazia Hasan Dana, Subrata K. Sarker, Li Li, S. M. Muyeen, Md. Firoj Ali, Zinat Tasneem, Md. Mehedi Hasan, Sarafat Hussain Abhi, Md. Robiul Islam, Md. Hafiz Ahamed, Md. Manirul Islam, Sajal K. Das, “Data-driven next-generation smart grid towards sustainable energy evolution: techniques and technology review,” Prot. Control Mod. Power Syst., vol. 8, p. 43, 2023, [Online]. Available: https://pcmp.springeropen.com/articles/10.1186/s41601-023-00319-5
R. R. M. K. N. S. V. K. C. J. S. R. K. Lokesh, “Machine Learning Based Smart Energy Management for Residential Application in Grid Connected System,” First Int. Conf. Electr. Electron. Inf. Commun. Technol. Trichy, India, pp. 1–6, 2022, doi: 10.1109/ICEEICT53079.2022.9768420.
I. Z. Arvind R. Singh, R. Seshu Kumar, Mohit Bajaj, Chetan B. Khadse, “Machine learning-based energy management and power forecasting in grid-connected microgrids with multiple distributed energy sources,” Sci. Rep., vol. 14, p. 19207, 2024, [Online]. Available: https://www.nature.com/articles/s41598-024-70336-3
J. Y. Yunlong Ma, Xiao Chen, Liming Wang, “Study on Smart Home Energy Management System Based on Artificial Intelligence,” J. Sensors, 2021, doi: https://doi.org/10.1155/2021/9101453.
I. Priyadarshini, S. Sahu, R. Kumar, and D. Taniar, “A machine-learning ensemble model for predicting energy consumption in smart homes,” Internet of Things, vol. 20, p. 100636, Nov. 2022, doi: 10.1016/J.IOT.2022.100636.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 50SEA
This work is licensed under a Creative Commons Attribution 4.0 International License.
