AI-Driven Energy Saving in Radio Access Network Sectors
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Solution Overview
Problem
Wireless radio access networks face challenges in optimizing energy consumption while maintaining quality of service (QoS) metrics, as existing methods often trade off between energy efficiency and QoS performance, leading to suboptimal results in various network sectors.
Innovation Solution
The implementation of AI/ML techniques to model energy usage and generate weighted scores for each sector based on energy consumption and QoS metrics, allowing for the identification and application of tailored energy-saving techniques that balance energy reduction with maintaining acceptable QoS levels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If energy-saving techniques are applied to reduce energy consumption in RAN sectors, then energy efficiency is improved, but QoS metrics may deteriorate
Solution Approach 1:
The system applies different energy-saving techniques to different RAN sectors based on their individual characteristics, traffic patterns, and QoS requirements. Each sector receives a customized energy-saving profile rather than a uniform approach, allowing energy reduction in sectors where QoS can be maintained while preserving service quality in sectors where it is critical.
Solution Approach 2:
The system dynamically adjusts energy-saving techniques based on real-time monitoring of QoS metrics and energy consumption patterns. The machine learning models continuously learn from operational data and adapt the energy-saving strategies accordingly, enabling the system to respond to changing network conditions and maintain optimal balance between energy efficiency and QoS performance.
2Loss of energy
If existing energy-saving methods are implemented, then energy consumption is reduced, but network performance and user experience deteriorate
Solution Approach 1:
The system implements continuous feedback loops where QoS metrics and energy consumption data are monitored in real-time. The machine learning models use this feedback to adjust and optimize energy-saving techniques dynamically, ensuring that energy reduction does not come at the expense of network performance. The feedback mechanism allows the system to learn from past actions and improve future decisions.
Solution Approach 2:
The system changes multiple parameters simultaneously including energy-saving technique selection, configuration settings, and operational modes of RAN sectors. By adjusting these parameters based on learned patterns and real-time conditions, the system achieves energy reduction while maintaining network performance within acceptable thresholds.
3Device complexity
If uniform energy-saving strategies are applied across all sectors, then implementation complexity is reduced, but optimization effectiveness deteriorates
Solution Approach 1:
The system employs machine learning models that automatically analyze sector characteristics, traffic patterns, and performance metrics to generate customized energy-saving profiles for each sector. This self-service approach eliminates the need for manual configuration and complex centralized decision-making, reducing implementation complexity while achieving optimal energy savings tailored to each sector's specific needs.
Data Source
AI summary
A system described herein may provide for the use of artificial intelligence/machine learning (“AI/ML”) techniques to model energy usage information and energy saving techniques in various locations or regions (e.g., sectors) associated with one or more radio access networks (“RANs”) of a wireless network. The system may determine energy saving techniques, such as radio frequency (“RF”) transmission modification in time and/or frequency domains, antenna mode modification, cell sleep mode, beamforming parameters, and/or other suitable techniques to modify energy consumption at such sectors. The system may balance energy savings with maintaining Quality of Service (“QoS”) metrics at an acceptable level.