Genetic Algorithm Weight Optimization for Cellular Network Predictive Models
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Solution Overview
Problem
Cellular network operators face challenges in optimizing key performance indicators (KPIs) such as dropped calls without formal analytical models, requiring efficient methods to predict and adjust network parameters effectively.
Innovation Solution
A genetic algorithm is employed to determine weight sets for a predictive model that correlates input performance indicators with target KPIs like dropped calls, allowing for adjustments to be made in the cellular network based on predictions generated from historical data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional network optimization methods are used, then network parameters can be adjusted, but the ability to predict and prevent performance degradation is insufficient
Solution Approach 1:
The patent implements predictive modeling using genetic algorithms to determine optimal weight sets for performance indicators before actual performance degradation occurs. The system continuously analyzes historical data and adjusts network parameters proactively, enabling preventive action rather than reactive response to network issues.
2Measurement precision
If genetic algorithm-based predictive models are implemented, then prediction accuracy for target KPIs improves, but computational complexity increases
Solution Approach 1:
The patent segments the complex prediction problem by identifying and weighting individual performance indicators separately. The genetic algorithm optimizes weight sets for specific subsets of KPIs (e.g., dropped calls, handover failures) rather than attempting to model all network parameters simultaneously, reducing computational complexity while maintaining prediction accuracy.
Solution Approach 2:
The system dynamically adjusts the weight parameters of performance indicators based on genetic algorithm optimization. By changing the weights of different KPIs according to their predictive importance, the system achieves high prediction accuracy without needing to complexly model all parameters with equal detail.
3Loss of information
If multiple performance indicators are monitored with high weighting, then prediction capability improves, but data processing requirements increase
Solution Approach 1:
The genetic algorithm dynamically determines optimal weight sets that prioritize the most informative performance indicators. By adjusting weights based on predictive importance rather than uniformly monitoring all indicators with equal detail, the system maintains complete information where needed while reducing processing overhead for less critical parameters.
Data Source
AI summary
An example method may include a processing system including at least one processor determining a final weight set comprising weight factors to apply to each of a plurality of performance indicators for a predictive model associated with a target performance indicator using a genetic algorithm. The method may further include the processing system gathering a first plurality of measurements of the plurality of performance indicators for at least a portion of a cellular network, applying the predictive model to the first plurality of measurements of the plurality of performance indicators to generate a prediction for the target performance indicator, and adjusting at least one aspect of the cellular network in response to the prediction.