Citation :

Shraddha Gupta, Ugrasen Suman, "Cellular Network Base Station Power Scheduling Using Machine Learning to Prevent Energy Wastage," International Journal of Engineering Trends and Technology (IJETT), vol. 74, no. 5, pp. 238-249, 2026. Crossref, https://doi.org/10.14445/22315381/IJETT-V74I5P116

Abstract

Energy conservation is crucial because of the growing need for cellular communication and energy usage. To address the issue of energy conservation, a number of green computing approaches have been proposed; however, the majority of these are less suitable and only achieve a small amount of energy savings in comparison to the anticipated level. This paper aims to develop and model a traffic-aware power-saving plan for cellular base stations. A Green Cellular Base Station Scheduling (GCBS) method is suggested. The training of a base station's historical traffic load is the first step in the GCBS technique. Next, forecast future traffic. The power of the base station will be adjusted based on the future traffic loads. A simulation has been conducted in this regard, and performance has been examined in terms of the base station's maximum energy requirements, actual energy requirements, and the quantity of energy saved by the suggested approach. The simulation results show that the GCBS approach can reduce the base station's energy consumption by 60%. The conclusion has finally been reached, and the plan for future extensions has been discussed.

Keywords

Cellular network, Machine Learning, Power Consumption, Proactive Power Scheduling, Workload Prediction.

References

[1] Stefano Carboni, “Smart Cities in Comparison: An Analysis of the Best Smart Cities,” Orașe Inteligente Și Dezvoltare Regională, vol. 8, no. 3, pp. 65-78, 2024.
[Google Scholar] [Publisher Link]

[2] Reza Mortaheb, and Piotr Jankowski, “Smart City Re-Imagined: City Planning and GeoAI in the Age of Big Data,” Journal of Urban Management, vol. 12, no. 1, pp. 4–15, 2023.
[CrossRef] [Google Scholar] [Publisher Link]

[3] Ángel Lloret et al., “A Data-Driven Framework for Digital Transformation in Smart Cities: Integrating AI, Dashboards, and IoT Readiness,” Sensors, vol. 25, no. 16, pp. 1-30, 2025.
[CrossRef] [Google Scholar] [Publisher Link]

[4] Mohammadali Kiehbadroudinezhad et al., “The Role of Biofuels for Sustainable MicrogridsF: A Path Towards Carbon Neutrality and the Green Economy,” Heliyon, vol. 9, no. 2, pp. 1-21, 2023.
[CrossRef] [Google Scholar] [Publisher Link]

[5] Mohamed G. Moh Almihat et al., “Energy and Sustainable Development in Smart Cities: An Overview,” Smart Cities, vol. 5, no. 4, pp. 1389-1408, 2022.
[CrossRef] [Google Scholar] [Publisher Link]

[6] Arshi Naim, “New Trends in Business Process Management: Applications of Green Information Technologies,” British Journal of Environmental Studies, vol. 1, no. 1, pp. 12-23, 2021.
[Google Scholar] [Publisher Link]

[7] Cosmin Avasalcai, Christos Tsigkanos, and Schahram Dustdar, “Resource Management for Latency-Sensitive IoT Applications with Satisfiability,” IEEE Transactions on Service Computing, vol. 15, no. 5, pp. 2982-2993, 2021.
[CrossRef] [Google Scholar] [Publisher Link]

[8] Md Whaiduzzaman et al., “A Review of Emerging Technologies for IoT-based Smart Cities,” Sensors, vol. 22, no. 23, pp. 1-28, 2022.
[CrossRef] [Google Scholar] [Publisher Link]

[9] Bing Li, “Effective Energy Utilization Through Economic Development for Sustainable Management in Smart Cities,” Energy Reports, vol. 8, pp. 4975-4987, 2022.
[CrossRef] [Google Scholar] [Publisher Link]

[10] Ali Gohar, and Gianfranco Nencioni, “The Role of 5G Technologies in a Smart City: The Case for Intelligent Transportation System,” Sustainability, vol. 13, no. 9, pp. 1-24, 2021.
[CrossRef] [Google Scholar] [Publisher Link]

[11] Chilakala Sudhamani et al., “A Survey on 5G Coverage Improvement Techniques: Issues and Future Challenges,” Sensors, vol. 23, no. 4, pp. 1-47, 2023.
[CrossRef] [Google Scholar] [Publisher Link]

[12] Mohsen Attaran, “The Impact of 5G on the Evolution of Intelligent Automation and Industry Digitization,” Journal of Ambient Intelligence and Humanized Computing, vol. 14, no. 5, pp. 5977-5993, 2021.
[CrossRef] [Google Scholar] [Publisher Link]

[13] Abu Jahid et al., “Techno-Economic and Energy Efficiency Analysis of Optimal Power Supply Solutions for Green Cellular Base Stations,” IEEE Access, vol. 8, pp. 43776-43795, 2020.
[CrossRef] [Google Scholar] [Publisher Link]

[14] Mahshid Javidsharifi et al., “Optimum Sizing of Photovoltaic and Energy Storage Systems for Powering Green Base Stations in Cellular Networks,” Energies, vol. 14, no. 7, pp. 1-21, 2021.
[CrossRef] [Google Scholar] [Publisher Link]

[15] Feifeng Zheng, Kezheng Chen, and Ming Liu, “Optimization of Communication base Station Battery Configuration Considering Demand Transfer and Sleep Mechanism under Uncertain Interruption Duration,” Sustainability, vol. 15, no. 24, pp. 1-18, 2023.
[CrossRef] [Google Scholar] [Publisher Link]

[16] M. Abdur Rahman et al., “A Review of Environmental Friendly Green Composites: Production Methods, Current Progresses, and Challenges,” Environmental Science and Pollution Research, vol. 30, no. 7, pp. 16905-16929, 2023.
[CrossRef] [Google Scholar] [Publisher Link]

[17] Damilare Samson Olaleye et al., “Advancing Green Communications: The Role of Radio Frequency Engineering in Sustainable Infrastructure Design,” International Journal of Latest Technology in Engineering, Management and Applied Science (IJLTEMAS), vol. 13, no. 5, pp. 113-121, 2024.
[CrossRef] [Google Scholar] [Publisher Link]

[18] Ferheen Ayaz et al., “Digital Twin based Reinforcement Learning for Energy Exchange among Electric Vehicles and Base Stations in a Disaster-affected Region,” IEEE Transactions on Intelligent Transportation Systems, vol. 27, no. 5, pp. 6181-6190, 2026.
[CrossRef] [Google Scholar] [Publisher Link]

[19] Sivakumar Sangeetha et al., “Smart Performance Optimization of Energy-Aware Scheduling Model for Resource Sharing in 5G Green Communication Systems,” The Journal of Engineering, vol. 2024, no. 2, pp. 1-20, 2024.
[CrossRef] [Google Scholar] [Publisher Link]

[20] Mohammed H. Alsharif et al., “Green IoT: A Review and Future Research Directions,” Symmetry, vol. 15, no. 3, pp. 1-37, 2023.
[CrossRef] [Google Scholar] [Publisher Link]

[21] Ali El Amine et al., “Energy Optimization with Multi-Sleeping Control in 5G Heterogeneous Networks using Reinforcement Learning,” IEEE Transactions on Network and Service Management, vol. 19, no. 4, pp. 4310-4322, 2022.
[CrossRef] [Google Scholar] [Publisher Link]

[22] Tianzhu Pan, Xuanli Wu, and Xuesong Li, “Dynamic Multi-Sleeping Control with Diverse Quality-of-Service Requirements in Sixth-Generation Networks using Federated Learning,” Electronics, vol. 13, no. 3, pp. 1-19, 2024.
[CrossRef] [Google Scholar] [Publisher Link]

[23] Nicola Piovesan et al., “Machine Learning and Analytical Power Consumption Models for 5G base Stations,” IEEE Communications Magazine, vol. 60, no. 10, pp. 56-62, 2022.
[CrossRef] [Google Scholar] [Publisher Link]

[24] Shuo Sun et al., “Deep Learning-based Traffic-Aware Base Station Sleep Mode and Cell Zooming Strategy in RIS-Aided Multi-Cell Networks,” IEEE Transactions on Cognitive Communications and Networking, vol. 11, no. 4, pp. 2171-2184, 2025.
[CrossRef] [Google Scholar] [Publisher Link]

[25] Vala Saleh, Mohsen Eslami, and Kamran Kazemi, “DPG-based Energy Efficiency Optimization for ABS-Assisted Beyond-5G Cellular Networks with Sleep Mode Management,” Frontiers in Communications and Networks, vol. 6, pp. 1-11, 2025.
[CrossRef] [Google Scholar] [Publisher Link]

[26] Tripti Dimri, Shamshad Ahmad, and Mohammad Sharif, “Time Series Analysis of Climate Variables using Seasonal ARIMA Approach,” Journal of Earth System Science, vol. 129, no. 1, pp. 1-16, 2020.
[CrossRef] [Google Scholar] [Publisher Link]

[27] Amjad Ali et al., “A k-Nearest Neighbours based Ensemble via Optimal Model Selection for Regression,” IEEE access, vol. 8, pp. 132095- 132105, 2020.
[CrossRef] [Google Scholar] [Publisher Link]

[28] Abdul-Lateef Balogun et al., “Spatial Prediction of Landslide Susceptibility in Western Serbia using Hybrid Support Vector Regression (SVR) with GWO, BAT and COA Algorithms,” Geoscience Frontiers, vol. 12, no. 3, pp. 1-15, 2021.
[CrossRef] [Google Scholar] [Publisher Link]

[29] Xinmeng Zhang et al., “Predicting Missing Values in Medical Data Via XGBoost Regression,” Journal of Healthcare Informatics Research, vol. 4, no. 4, pp. 383-394, 2020.
[CrossRef] [Google Scholar] [Publisher Link]

[30]Xiaoyan Ma et al., “Energy Consumption Optimization of 5G Base Stations Considering Variable Threshold Sleep Mechanism,” Energy Reports, vol. 9, pp. 34-42, 2023.
        [CrossRef] [Google Scholar] [Publisher Link]