Optimizing Cloud Power In An Open Radio Access Network Based on Subscriber Behavior

Lotfi, Abdul Jalil (2024) Optimizing Cloud Power In An Open Radio Access Network Based on Subscriber Behavior. Masters thesis, Dublin, National College of Ireland.

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Abstract

The fast adoption of 5G has significantly changed mobile communications, due to the high speed, very low latency, and the ability to serve billions of devices. However, this rapid expansion comes at the cost of increased energy consumption, caused by the need for more Radio Units (RU) deployment in high-traffic areas because of the use of higher-frequency radio waves, which offer shorter transmission ranges. This challenge has increased the computational load on cloud based Distributed units and Central units further compromising energy efficiency. Open Radio Access Network (O-RAN) architecture was a paradigm shift by introducing disaggregation, virtualization and open interfaces as those features enable more flexibility and interoperable networks. Additionally, O-RAN introduced RAN Intelligent Controllers which enables important features such as closed-loop control and AI-driven decision-making.

This thesis proposes a novel AI-driven solution to optimize energy consumption in O-RAN networks by predicting RU power states based on subscriber behavior. What makes our work different from the traditional approaches, static power management, is the use of AI to adapt in real-time to network changes and user mobility ensuring more efficient and responsive energy use in 5G networks. To achieve this goal a virtual network simulation was created and Markov process used to simulate realistic user mobility and traffic patterns across different regions and times. The generated dataset was used to train Random Forest Classifier in AWS SageMaker, resulting in predictive accuracy of 99.09%. Finally, the trained model will be deployed in a real-time cloud-based environment enabling prediction request and response through API Gateway and AWS Lambda integration.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Makki, Ahmed UNSPECIFIED
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science Q Science > QH Natural history > QH301 Biology > Methods of research. Technique. Experimental biology > Data processing. Bioinformatics > Artificial intelligence Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Artificial intelligence T Technology > T Technology (General) > Information Technology > Cloud computing
Divisions:	School of Computing > Master of Science in Cloud Computing
Depositing User:	Ciara O'Brien
Date Deposited:	15 Jul 2025 14:00
Last Modified:	15 Jul 2025 14:00
URI:	https://norma.ncirl.ie/id/eprint/8118

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