Fiber Connectivity Detection Using Optical Loss Regression
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Solution Overview
Problem
Optical signal loss in fiber networks due to fiber connectivity issues, such as improper connections and underperforming components, leads to poor network performance and costly technician dispatches.
Innovation Solution
A method using rule-based models to predict fiber connectivity issues by analyzing optical signal levels and distances between network components, employing regression analysis and threshold comparisons to identify excessive signal loss.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If optical signal level measurements are taken at multiple optical terminals, then the ability to detect fiber connectivity issues is improved, but the complexity of the detection system increases
Solution Approach 1:
The detection system segments the fiber network into multiple optical terminals (OLT, ONT, intermediate terminals) and measures signal levels at each segment. This allows the complex detection task to be broken down into manageable measurements at individual terminals, improving detection accuracy while keeping each individual measurement simple.
Solution Approach 2:
The system adds the dimension of distance measurement between terminals to the signal level measurements. By combining signal level data with distance information, the system can calculate expected signal levels and detect anomalies, improving detection capability without requiring overly complex measurement hardware at each terminal.
2Measurement precision
If regression analysis is performed to predict expected signal levels, then the accuracy of fiber problem detection is improved, but the computational complexity increases
Solution Approach 1:
The regression model is pre-trained using historical data from the fiber network, capturing the relationship between distance, signal levels, and network conditions. This preliminary training allows the system to make accurate predictions during operation without performing complex real-time analysis, reducing computational burden while maintaining high accuracy.
3Reliability
If optical signal levels are monitored continuously, then proactive detection of fiber issues is improved, but energy consumption increases
Solution Approach 1:
Instead of continuous monitoring, the system performs signal level measurements at periodic intervals or when events occur (such as connection establishment or changes). This periodic measurement approach maintains proactive detection capability while significantly reducing energy consumption compared to continuous monitoring.
4Measurement precision
If distance values are collected for all optical terminals, then the accuracy of signal loss analysis is improved, but the amount of data to process increases
Solution Approach 1:
The system collects and processes distance values selectively based on the specific detection task. Rather than processing all available distance data uniformly, the system focuses on distance values relevant to the current measurement context (e.g., distance between specific terminals involved in a signal loss complaint), reducing data volume while maintaining detection precision.
Data Source
AI summary
Aspects of the subject disclosure may include, for example, trained, rule based models used to predict whether fiber connections exist upstream of an optical terminal. A regression model is built using optical signal measurements at optical terminals and distances between the optical terminals and an upstream optical terminal. The regression model may be used to predict an optical signal level at an under-test optical terminal based on a distance between the under-test optical terminal and the upstream optical terminal. Other embodiments are disclosed.