Neural Network Channel Frequency Response Prediction
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Solution Overview
Problem
Existing methods for predicting the channel frequency response and bitrate of repaired communication channels, such as DSL lines, are inaccurate, leading to incorrect estimations of bandwidth gain and loop length, which affects the reliability of network management and customer service improvements.
Innovation Solution
A communication network apparatus and method using a machine learning model, specifically a convolutional neural network, to estimate the channel frequency response and bitrate of repaired communication channels by training on simulated impairment and non-impairment data, employing hybrid error metrics for improved accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If curve fitting techniques or interpolation methods are used to predict channel frequency response, then prediction can be performed, but prediction accuracy is insufficient leading to incorrect bandwidth gain and loop length estimations
Solution Approach 1:
The patent replaces traditional mechanical/mathematical prediction methods (curve fitting, interpolation) with a neural network-based intelligent system. The neural network learns complex nonlinear relationships between impaired and repaired channel characteristics from training data, enabling more accurate predictions of channel frequency response and bitrate without relying on simplified mathematical models.
Solution Approach 2:
The patent implements preliminary training of the neural network using simulated impairment and non-impairment data before actual prediction. This preliminary learning phase allows the system to pre-establish the mapping between impaired and repaired channel characteristics, improving prediction accuracy when deployed in real network management scenarios.
2Reliability
If traditional prediction methods are used, then implementation is simple, but the cost/benefit ratio analysis of repair actions is unreliable
Solution Approach 1:
The patent substitutes traditional simple prediction algorithms with a neural network system that provides reliable cost/benefit analysis. Although the neural network increases system complexity, it delivers significantly improved reliability in predicting repaired channel performance, enabling accurate prioritization of repair actions based on true cost/benefit ratios.
3Measurement precision
If more accurate prediction methods are implemented, then prediction reliability improves, but computational complexity and training requirements increase
Solution Approach 1:
The patent performs the computationally intensive neural network training in advance using simulated data, separating the learning phase from the prediction phase. This preliminary action allows the complex model to be developed offline, while online prediction operations remain relatively simple and fast, balancing accuracy requirements with operational complexity.
Data Source
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AI summary
Embodiments relate to a method and an apparatus for predicting the bitrate of a repaired communication channel. The method may comprise: - generating a dataset specifying, for a plurality of communication channels: - a channel frequency response of a communication channel affected by an impairment, and - a channel frequency response of the communication channel non-affected by said impairment, - training, based on the dataset, a machine learning model configured for predicting, based on the channel frequency response of an impaired communication channel, the channel frequency response of the repaired communication channel.