Supervised Learning for Passive Intermodulation Detection
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Solution Overview
Problem
Current methods for detecting passive intermodulation (PIM) interference in cellular networks are inefficient, requiring active testing, site visits, and specialized equipment, which are costly and do not allow for continuous monitoring or accurate differentiation from other interference types.
Innovation Solution
A method using supervised learning models to analyze existing cellular radio network performance data and site configuration data, allowing for continuous and accurate detection of PIM interference without the need for site visits or specialized equipment, by defining time slices and calculating specific performance metrics to determine if a cell has experienced PIM interference.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If active testing or on-site testing is performed to accurately detect PIM interference, then detection precision is improved, but device complexity and loss of time increase due to specialized equipment and site visits
Solution Approach 1:
The patent creates a virtual model of PIM interference detection by copying the essential characteristics of active testing into a passive observational framework. Instead of physically deploying specialized equipment at cell sites, the system replicates the detection capability through machine learning models trained on historical performance data, allowing accurate PIM identification without physical presence or specialized hardware at the remote location
Solution Approach 2:
The patent replaces the mechanical system of physical site visits and hands-on testing equipment with an automated computational system. Machine learning algorithms process existing network performance metrics to detect PIM interference, substituting the need for technicians to travel to cell sites with remote automated analysis that achieves comparable or superior detection accuracy
2Measurement precision
If on-site testing is performed with technicians and specialized equipment, then detection precision is improved, but loss of time and productivity decrease due to travel and site visit requirements
Solution Approach 1:
The patent performs preliminary action by continuously collecting and analyzing network performance data in the background, so that when PIM interference occurs, the system has already processed relevant metrics and can immediately identify the issue. This eliminates the need for technicians to travel to sites for initial investigation, as the detection and preliminary analysis are completed automatically before human intervention is needed
Solution Approach 2:
The system enables self-service by allowing the network to detect and diagnose PIM interference autonomously without requiring external technician intervention. The machine learning model continuously monitors performance metrics and automatically identifies PIM events, enabling the network to service itself and reduce dependency on human technicians for routine detection tasks
3Ease of operation
If observational methods are used to detect interference, then ease of operation is improved, but measurement precision deteriorates due to inability to differentiate PIM from other interference types
Solution Approach 1:
The patent applies parameter changes by transforming raw network performance metrics into differentiated interference classifications through machine learning. Instead of treating all interference equally, the system analyzes multiple parameters including PRB utilization, interference power, and temporal patterns to distinguish PIM from other interference types, achieving both ease of operation and measurement precision through automated parameter transformation
Solution Approach 2:
The system implements feedback by continuously monitoring network performance and using detected interference patterns to refine its classification capabilities. The machine learning model learns from accumulated data to improve its ability to differentiate PIM from other interference types, creating a feedback loop that enhances measurement precision while maintaining the ease of remote operation
Data Source
AI summary
A method for determining whether a target cell has experienced interference due to Passive Intermodulation (PIM) distinguished from other forms of interference at cellular network sites during a time window. In one aspect, the method includes defining a set of N time slices and obtaining a first performance metric for each of the N time slices. The method includes selecting a subset of the N time slices using a set of N first performance metrics where the subset of N time slices includes a first time slice. For the first time slice, at least a first data point is determined using a performance metric for the target cell that was collected during the first time slice. The method includes using the first data point and a supervised learning model to determine whether the target cell has experience PIM interference during the time window.