Network Interference Detection via PRB Transformation and Clustering
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Solution Overview
Problem
Current techniques for detecting network interference are unable to effectively identify and mitigate periodic and transient interference, leading to degraded call success rates, poor voice quality, and reduced data throughput for user equipment (UEs).
Innovation Solution
A management system that receives time domain physical resource block (PRB) data from base stations, transforms it into frequency domain data, identifies periodic interference patterns, processes these patterns with a clustering model to identify clusters, and uses a time offset analysis to link clusters and predict the route of transient periodic interferers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If current interference detection techniques are used, then network interference can be detected, but periodic and transient interference cannot be effectively identified and mitigated
Solution Approach 1:
The system performs preliminary actions by transforming PRB data from time domain to frequency domain before analysis, enabling the detection system to identify periodic and transient interference patterns that would otherwise remain undetected. This preliminary transformation prepares the data for subsequent pattern recognition and clustering operations.
Solution Approach 2:
The system implements feedback mechanisms through continuous monitoring of interference patterns, clustering analysis results, and route predictions. The management system uses this feedback to dynamically adjust detection parameters and mitigate identified interference, thereby improving call success rates while maintaining high detection accuracy.
2Productivity
If interference mitigation actions are taken without effective detection, then resources are consumed, but call success rates and data throughput remain degraded
Solution Approach 1:
The system enables self-service by automatically detecting, analyzing, and mitigating interference patterns without requiring manual technician intervention. The management system autonomously processes PRB data, identifies interference types, determines affected UEs, and executes mitigation strategies, thereby improving data throughput while conserving computing resources by eliminating unnecessary manual dispatches.
3Ease of repair
If technician dispatches are made to identify interference sources, then interference can be addressed, but computing and networking resources are wasted
Solution Approach 1:
The system replaces the mechanical process of manual technician dispatches with an automated electronic detection and analysis system. The management system uses algorithms to process PRB data, identify interference patterns, and determine resolution strategies, thereby making interference resolution easier while conserving computing resources by eliminating unnecessary field visits.
Solution Approach 2:
The system introduces an intermediary layer in the form of an automated management system that mediates between interference detection and resolution. This intermediary processes data from base stations, identifies interference patterns, and coordinates mitigation actions, thereby simplifying the repair process while reducing the computational burden on network elements.
4Productivity
If periodic and transient interference patterns are not identified, then network operations continue, but voice quality and call success rates deteriorate
Solution Approach 1:
The system applies parameter changes by transforming PRB data from the time domain to the frequency domain, which fundamentally alters the data representation and enables the identification of periodic and transient interference patterns. This transformation changes the parameters of the data to make interference characteristics visible and actionable.
Solution Approach 2:
The system segments the interference detection process into distinct phases: data collection from base stations, time-domain to frequency-domain transformation, pattern identification, clustering analysis, and mitigation execution. This segmentation allows each phase to be optimized independently, thereby improving call success rates while reducing the overall impact of interference on network operations.
Data Source
AI summary
A device may receive time domain PRB data associated with a plurality of base stations of a network, and may transform the time domain PRB data into frequency domain PRB data. The device may identify, based on the frequency domain PRB data, periodic interference patterns associated with the network, and may process periodic data associated with the periodic interference patterns, with a clustering model, to identify clusters of periodic interference patterns. The device may utilize a time offset analysis to link clusters and identify a transient periodic interferer, and may process cluster data associated with the clusters and the transient periodic interferer, and public transportation data, with a machine learning model, to predict a route associated with the transient periodic interferer. The device may perform one or more actions based on the route associated with the transient periodic interferer.