Predictive Energy Consumption Modeling for Cellular Network Anomaly Detection
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Solution Overview
Problem
Identifying locations with anomalous energy consumption and determining the root causes in cellular networks is challenging due to the complexity and large scale of these networks.
Innovation Solution
A method that involves collecting and normalizing data from various sources within the cellular network, using predictive models (such as XGBoost) to estimate energy consumption at logical cell sites, and performing root-cause analysis to detect outliers and potential energy-saving opportunities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional monitoring methods are used in cellular networks, then device simplicity is maintained, but the ability to identify anomalous energy consumption and root causes deteriorates due to network complexity and scale
Solution Approach 1:
The patent segments the cellular network into multiple logical cell sites, each treated as an independent analysis unit. This segmentation allows the system to manage and analyze energy consumption at a granular level, making it feasible to identify anomalies in individual sites rather than being overwhelmed by the entire network's complexity.
Solution Approach 2:
The patent introduces a predictive modeling system as an intermediary between raw energy consumption data and anomaly detection. This intermediary uses machine learning models to process and interpret complex network data, translating it into actionable insights about energy consumption patterns and anomalies without requiring direct manual analysis of the complex network infrastructure.
2Measurement precision
If detailed monitoring of all network devices is implemented, then energy consumption measurement precision improves, but the complexity of data collection and analysis increases
Solution Approach 1:
The patent performs preliminary actions by collecting and normalizing energy consumption data from multiple sources before analysis. This includes gathering data from network elements, environmental sensors, and operational systems, then normalizing it to a common format. This preliminary data preparation simplifies subsequent anomaly detection and root cause analysis by having clean, standardized data ready for modeling.
Solution Approach 2:
The patent implements feedback mechanisms where the predictive model continuously compares actual energy consumption against predicted values, and when anomalies are detected, the system provides feedback to identify potential root causes. This feedback loop enables automated detection and analysis, reducing the difficulty of measuring and interpreting energy consumption patterns across the complex network.
3Measurement precision
If manual analysis of energy consumption data is performed, then measurement accuracy can be maintained, but productivity and scalability deteriorate due to the large number of devices
Solution Approach 1:
The patent implements self-service through automated predictive modeling systems that independently analyze energy consumption data without requiring manual intervention. The machine learning models automatically detect anomalies, identify patterns, and suggest root causes, enabling the system to serve itself in terms of data analysis and reducing the need for human analysts to manually examine each device's energy consumption.
Solution Approach 2:
The patent replaces manual mechanical analysis with computational modeling. Instead of human analysts manually examining energy consumption data, the system uses machine learning algorithms and predictive models to automatically process and analyze the data. This substitution dramatically increases productivity and scalability while maintaining or improving detection accuracy through consistent, algorithmic analysis.
Data Source
AI summary
A predictive modeling approach to managing cellular network infrastructure is disclosed. In an embodiment, a method can include receiving raw data from a plurality of data sources populated while operating a cellular network. The method can then generate per-logical cell site data by normalizing the raw data based on a set of LCSs in the cellular network to generate per-LCS data. The method can then generate an example from the per-LCS data and generate a predicted energy consumption value for the given LCS by inputting the example into a predictive model (e.g., a decision tree-based model, such as an XGBoost model). From this output, the method can determine if the predicted energy consumption value is higher than an expected energy consumption value (e.g., a historical range of consumption). If so, the method can then label the given LCS as an outlier.