Cell Switch-Off Thresholds for Energy-Aware Radio Access Networks
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Solution Overview
Problem
Cellular communication networks face significant energy consumption due to the need for continuous operation during peak hours, which can be optimized by switching off cells during low usage periods to reduce energy consumption while maintaining quality of service.
Innovation Solution
Implementing a system that dynamically adjusts cell switching based on load prediction using machine learning techniques to determine optimal threshold values for switching cells on and off, balancing energy savings with quality of service by optimizing pre-configured thresholds through offline and online optimization methods.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by stationary object
If cells are switched off during off-peak hours, then energy consumption is reduced, but network coverage and capacity may be compromised
Solution Approach 1:
The patent implements dynamic cell switching based on real-time traffic load conditions. Cells are switched off when traffic demand is low and switched on when demand increases, making the network infrastructure adaptable to varying usage patterns. This dynamic approach allows the system to reduce energy consumption during off-peak hours while maintaining network coverage when needed.
Solution Approach 2:
The system changes the operational state parameter of cells (on/off) based on traffic load thresholds. By monitoring traffic parameters and switching cells accordingly, the system optimizes energy consumption while ensuring network coverage is maintained when traffic demand requires it.
2Loss of energy
If threshold pairs are optimized using historical data, then energy savings are improved, but system complexity increases
Solution Approach 1:
The patent performs preliminary optimization of threshold pairs using historical traffic data before deployment. By analyzing past traffic patterns and determining optimal threshold values in advance, the system achieves energy savings without requiring complex real-time decision-making algorithms during operation. This preliminary analysis phase separates the complexity from the operational phase.
Solution Approach 2:
The system uses its own historical traffic data to automatically determine optimal threshold pairs for cell switching. This self-service approach eliminates the need for external manual configuration or complex external optimization systems, reducing overall system complexity while achieving energy optimization.
3Use of energy by stationary object
If cells are switched off frequently, then energy consumption is reduced, but network stability deteriorates
Solution Approach 1:
The system dynamically adjusts cell operational status based on traffic load conditions, switching cells off during low-demand periods and on during high-demand periods. This dynamic adaptation allows energy savings while maintaining network stability by responding to actual usage conditions rather than using fixed switching schedules.
Solution Approach 2:
The system monitors traffic load conditions and uses this feedback to determine when to switch cells on or off. By continuously monitoring network conditions and adjusting cell status accordingly, the system achieves energy savings while maintaining stability through responsive, condition-based decision-making rather than arbitrary switching.
Data Source
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AI summary
Disclosed is a method comprising obtaining historical data from a plurality of access nodes comprised in a network, determining, for at least one of the plurality of access nodes, a region comprising a set of values for threshold pairs that comprise minimum and maximum threshold pairs, determining, from the region, one threshold pair, wherein the threshold pair defines pre-determined thresholds for determining if a cell is to be switched on or switched off, providing the threshold pair to the network for deployment, and collecting data regarding at least one key performance indicator from the plurality of access nodes.