Autonomous Traffic-Aware and QoS-Constrained Capacity Cell Shutdown for Green Mobile Networks
- Resource Type
- Conference
- Authors
- Zaki, Mohamed; Gaber, Ayman; Khafagy, Mohammad Galal; Beshara, Mohamed; Abdelbaki, Nashwa
- Source
- 2023 5th Novel Intelligent and Leading Emerging Sciences Conference (NILES) Novel Intelligent and Leading Emerging Sciences Conference (NILES), 2023 5th. :142-145 Oct, 2023
- Subject
- Bioengineering
Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Engineered Materials, Dielectrics and Plasmas
Power, Energy and Industry Applications
Robotics and Control Systems
Signal Processing and Analysis
Training
Energy consumption
Switches
Quality of service
Telecommunication traffic
Traffic control
Energy efficiency
cellular networks
energy saving
machine learning
RAN intelligent controller
service management
orchestration
O-RAN
- Language
Energy efficiency of Radio Access Networks (RANs) is increasingly becoming a global strategic priority for Mobile Network Operators (MNOs) and governments to attain sustainable and uninterruptible network services. In this work, we propose an autonomous Machine Learning (ML)-based framework to maximize RAN energy efficiency via underutilized radio resource shutdown while maintaining an adequate network capacity with a preset Quality-Of-Service (QoS) level. This is achieved by dynamically switching radio resources on and off according to service demand. Training on a live network dataset and applying back the ML-advised parameters, the proposed framework is shown to save 10.3% of the overall RAN energy consumption while maintaining the imposed QoS level.