Tilt-Normalized Network Impairment Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for detecting network impairments in CATV systems are hindered by the need for wide limits to account for tilt, making it difficult to detect error conditions like roll-off, suck-out, and standing waves, as these conditions are masked by the tilt.
Innovation Solution
A method and apparatus that normalize measurement data by computing a best-fit tilt and performing tilt-normalization, allowing for threshold checks and pattern matching to detect network impairments, thereby isolating and identifying these error conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If wide limits are used to account for tilt in limit checking, then the measurement can accommodate tilt variations, but error conditions such as roll-off, suck-out, and standing waves become difficult to detect
Solution Approach 1:
The patent extracts the tilt component from the measurement data by computing a best-fit tilt line and removing it through tilt-normalization. This separation allows the limit checking to focus on detecting error conditions without being masked by tilt variations, effectively taking out the interfering tilt element from the measurement.
Solution Approach 2:
The patent transforms the measurement data by applying tilt-normalization, which changes the parameter space by removing the tilt component. This parameter transformation converts tilted data into tilt-normalized data, enabling precise detection of error conditions while maintaining adaptability to different tilt scenarios.
2Measurement precision
If tilt-normalized limit checks are performed, then error conditions can be accurately detected, but the process becomes more complex compared to standard limit checking
Solution Approach 1:
The patent performs preliminary tilt normalization by computing the best-fit tilt and removing it from the measurement data before performing limit checks. This preliminary action prepares the data in advance, making the subsequent error detection process more accurate without requiring complex real-time processing during the actual measurement.
Solution Approach 2:
The patent introduces tilt-normalized data as an intermediary representation between the raw measurement data and the final error detection. This intermediate form facilitates the transition from tilted measurements to accurate error condition detection, simplifying the overall process by providing a standardized intermediate state.
Data Source
AI summary
A method and apparatus are provided for detecting network impairments through tilt-normalized measurement data, the method including: collecting data for a network signal; computing a best-fit tilt for the collected data; performing tilt-normalization of the collected data responsive to the computed best-fit tilt; and determining whether the tilt-normalized data crosses a threshold, and if so, pattern matching the tilt-normalized data to detect a network impairment; and the apparatus including: an input unit for collecting data from a network signal; a tilt unit connected to the input unit for computing a best-fit tilt for the collected data and performing tilt-normalization of the collected data responsive to the computed best-fit tilt; and a pattern matching unit connected to the tilt unit for determining whether the tilt-normalized data crosses a threshold, and if so, pattern matching the tilt-normalized data to detect at least one network impairment.