Intelligent RAN Energy Management System for Base Station Optimization
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Solution Overview
Problem
Mobile telecommunications operators face significant energy costs due to high energy consumption in radio access networks, particularly in data-intensive LTE networks, where energy accounts for 10-15% of total network operating expenses in mature markets and up to 50% in developing markets, with over 90% of energy consumption attributed to the operators' side, mainly from Radio Base Stations.
Innovation Solution
An intelligent Radio Access Network (RAN) energy management system that correlates site configuration details, RAN attributes, and energy consumption attributes to categorize areas and identify energy-consuming sites, performing root cause analysis and optimizing energy usage by automating key attributes and switching off idle components, thereby reducing energy consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If radio base stations operate continuously to maintain network coverage and service quality, then network reliability is improved, but energy consumption increases
Solution Approach 1:
The system dynamically adjusts base station operational states based on real-time traffic conditions. Base stations transition between active, idle, and sleep states according to demand, allowing the network to maintain reliability when needed while reducing energy consumption during low-traffic periods. This dynamic state adjustment resolves the contradiction between continuous operation and energy savings.
Solution Approach 2:
The system changes operational parameters of base stations including transmit power levels, antenna configuration, and component activation states. By adjusting these parameters based on traffic demand, the system maintains network coverage and service quality while optimizing energy consumption, thus resolving the contradiction between reliability and energy use.
2Productivity
If more base stations are deployed to expand network capacity and coverage, then network service quality is improved, but energy consumption increases
Solution Approach 1:
The system segments the network into multiple zones with different operational characteristics. By categorizing base stations into groups based on their performance and energy consumption profiles, the system can apply different energy management strategies to each segment, maintaining network capacity while reducing overall energy consumption through targeted optimization.
Solution Approach 2:
The energy management system provides universal functionality across diverse base station types and network conditions. It categorizes base stations into performance groups and applies standardized energy optimization strategies that work across different technologies and deployment scenarios, enabling energy efficiency improvements without compromising network capacity.
3Use of energy by moving object
If manual energy optimization is performed to reduce energy costs, then energy consumption is reduced, but operational complexity increases
Solution Approach 1:
The system enables base stations to self-manage their energy consumption by automatically categorizing themselves into performance groups and selecting appropriate energy optimization actions. This self-service capability reduces the need for manual intervention and complex operational management while achieving significant energy consumption reductions across the network.
Solution Approach 2:
The system implements continuous monitoring and feedback loops that track base station performance, energy consumption, and traffic conditions. This feedback mechanism automatically adjusts energy management strategies based on real-time conditions, reducing operational complexity by eliminating the need for manual analysis and decision-making while maintaining optimal energy efficiency.
Data Source
AI summary
A system, method, and computer program product are provided for performing intelligent RAN energy management. In operation, a system correlates information including site configuration details, RAN attributes, and energy consumption attributes for each of a plurality of areas including sites, clusters, or zones. The system assigns each of the plurality of areas to one of a plurality of categories based on similar characteristics including associated site configuration details and RAN attributes. Further, the system identifies one of more of a plurality of sites in each of the plurality of categories as bad for energy consumption based on the energy consumption attributes associated with a particular category.