Wired Line Impairment Location Using Neural Noise Modeling
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Solution Overview
Problem
Wired networks experience physical layer impairments that are challenging to locate and monitor accurately, leading to service degradation and increased maintenance costs, particularly in telecommunication subscriber networks.
Innovation Solution
A method and apparatus using a neural network to determine the location and type of impairments in wired networks by processing measured channel frequency response and noise power spectral density, deriving theoretical noise representations, and exploiting far-end crosstalk and line length to enhance accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional measurement methods are used to detect physical impairments, then the detection process is simple, but the location accuracy is insufficient
Solution Approach 1:
The patent introduces a neural network as an intermediary processing layer between the measured channel frequency response and the impairment location determination. The neural network processes the frequency response data to extract features and determine impairment locations with high accuracy, resolving the contradiction by adding computational intelligence without requiring complex physical measurement equipment
Solution Approach 2:
The patent transforms the one-dimensional channel frequency response measurement into a multi-dimensional analysis by considering frequency-dependent characteristics and comparing against theoretical models across different impairment scenarios. This dimensional expansion enables precise location determination through pattern recognition in the frequency domain
2Reliability
If remote impairment detection is implemented, then service interruption is avoided, but the detection accuracy may be compromised
Solution Approach 1:
The patent employs an iterative feedback mechanism where the neural network's initial impairment location estimates are refined by comparing predicted channel responses with actual measurements. This feedback loop continues until convergence, ensuring high detection accuracy while maintaining service continuity through remote operation
Solution Approach 2:
The patent performs preliminary actions by pre-training the neural network with extensive simulation data covering various impairment types and locations before actual deployment. This preliminary training enables the system to achieve high detection accuracy from the start of remote operations without requiring service interruptions for calibration
3Productivity
If high accuracy impairment location is achieved, then maintenance costs are reduced, but the computational complexity increases
Solution Approach 1:
The patent changes the parameter representation from raw time-domain signals to frequency-domain channel frequency responses with specific spectral characteristics. This parameter transformation enables the neural network to process impairment information more efficiently, achieving high location accuracy while controlling computational complexity through dimensionality reduction in the frequency domain
Data Source
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AI summary
Embodiments of the present disclosure relate to an apparatus comprising means configured to perform: obtaining a measured channel frequency response, and a measured noise power spectral density for a line of a wired network susceptible to an impairment; deriving, in case of an indication of an impairment present on the line, from the measured channel frequency response and the measured noise power spectral density, a first theoretical noise representation for the line with the impairment and a second theoretical noise representation for the line without the impairment; and determining information indicative of a location of the impairment in the line, by processing the measured noise power spectral density, the first theoretical noise representation, and the second theoretical noise representation with a neural network. Further example embodiments relate to a method for determining information indicative of a location of the impairment, a computer program product, and a computer readable storage medium.