Periodic Interference Detection in Cellular Networks
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Solution Overview
Problem
Current techniques for detecting network interference in cellular networks are ineffective in identifying periodic and transient interference sources, leading to resource consumption and poor user experience due to degraded call success rates, increased dropped calls, and reduced data throughput.
Innovation Solution
A management system dynamically determines abnormal periodic signals by transforming time domain PRB data into frequency domain, calculating seasonal strength, applying filters, and using machine learning models to identify clusters of periodic interference patterns, thereby conserving computing and networking resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If current techniques for detecting network interference are used, then existing interference detection capabilities are maintained, but periodic and transient interference sources cannot be identified, leading to resource consumption and poor user experience
Solution Approach 1:
The system performs preliminary actions by transforming time-domain PRB data into frequency-domain data and calculating seasonal strength metrics before actual interference detection. This preparation enables the system to identify periodic and transient interference patterns proactively, improving detection accuracy while optimizing resource consumption by processing data in a structured manner that filters out normal variations before analysis.
Solution Approach 2:
The system dynamically adapts its detection methodology by adjusting the analysis approach based on the characteristics of the data. It dynamically determines whether to focus on periodic patterns through seasonal strength calculation or transient patterns through rate-of-change analysis. This dynamic adaptation allows the system to maintain high reliability in interference detection while improving productivity by avoiding unnecessary processing of all data types uniformly.
2Measurement precision
If comprehensive interference detection is performed, then all types of interference can be detected, but computing and networking resources are consumed without effective mitigation
Solution Approach 1:
The system extracts and focuses analysis on the most critical aspects of interference data. By transforming data to frequency domain and calculating seasonal strength, it extracts periodic components that are most indicative of interference. This extraction approach enables high measurement precision for the most relevant interference types while reducing computing resource consumption by avoiding exhaustive analysis of all data characteristics.
Solution Approach 2:
The system applies different analysis qualities to different aspects of the data. It uses detailed frequency-domain analysis and seasonal strength calculation for identifying periodic interference, while using simpler rate-of-change methods for transient interference detection. This localized application of analysis depth optimizes measurement precision for specific interference types while managing computing resource consumption by not applying maximum analysis depth uniformly to all data.
3Ease of operation
If periodic and transient interference sources are not identified, then current detection methods remain simple, but user experience deteriorates due to degraded call success rates and reduced data throughput
Solution Approach 1:
The system segments the interference detection task into distinct analytical components: time-domain to frequency-domain transformation, seasonal strength calculation for periodic interference, and rate-of-change analysis for transient interference. This segmentation maintains operational simplicity by breaking down the complex detection process into manageable steps that can be executed systematically, while improving reliability by ensuring both periodic and transient interference sources are identified through dedicated analysis modules.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Accurately detects abnormal periodic signals, reducing resource consumption and improving user experience by efficiently locating and mitigating interference sources, thus enhancing call success rates and data throughput.
Implementation Method 1
calculate fast Fourier transforms (FFTs) for the normalized and scaled PRB data
Data Source
AI summary
A device may calculate a PRB seasonal strength based on PRB data from base stations, and may scale and normalize the PRB data based on the PRB seasonal strength. The device may calculate and combine FFTs for the normalized and scaled PRB data, may calculate a z-score for the combined FFT, and may calculate FFT IQRs for frequencies of the combined FFT. The device may filter the FFT IQRs based on the z-score, may process the FFT IQRs, with a clustering model, to identify clusters of periodic interference patterns, and may aggregate the clusters. The device may identify peak data in the PRB data, and may process the peak data, with a model, to determine a parameter for clustering. The device may process the PRB data, with the clustering model, to identify final clusters of periodic interference patterns, and may perform actions based on the final clusters.