Vectored Data Communication Noise Detection
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Solution Overview
Problem
In DSL transmission systems, impulse noise can mislead error calculations during vectoring training, leading to suboptimal crosstalk reduction and increased bit error rates when adding a new communication connection to a vectored group, as error values may reflect noise rather than crosstalk.
Innovation Solution
Implement a method to detect and account for temporary noise events, such as impulse noise, during the vector training process by evaluating deviations in received training signals and transmitting noise indications to adapt vectoring coefficients, potentially aborting or modifying the training process if noise exceeds thresholds.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If training signals are transmitted to determine crosstalk influence when adding a new communication connection to a vectored group, then crosstalk reduction accuracy is improved, but impulse noise causes error values to reflect noise rather than crosstalk, leading to misadaptation of vectoring
Solution Approach 1:
The system performs preliminary noise detection and evaluation before completing the vectoring training process. By detecting impulse noise during training signal transmission and evaluating its impact on error values, the system can identify when noise is dominating the error calculation and take preliminary actions to prevent misadaptation, such as extending the training period or adjusting the adaptation algorithm.
Solution Approach 2:
The system implements feedback mechanisms where error values from training signal reception are continuously monitored and evaluated. When impulse noise is detected and identified as the dominant factor in error values, the feedback loop triggers corrective actions such as extending the training period, re-evaluating error values, or adjusting vectoring coefficients, thereby preventing unreliable adaptation.
2Reliability
If the vectoring training process is extended to account for potential impulse noise, then vectoring adaptation reliability is improved, but training time and system activation time are increased
Solution Approach 1:
The system dynamically adjusts the vectoring training process based on real-time noise detection. Rather than using a fixed extended training period for all cases, the system monitors for impulse noise during training and adaptively extends or modifies the training duration only when noise is detected. This dynamic approach maintains reliability by responding to actual noise conditions while minimizing unnecessary training time extensions.
Solution Approach 2:
The system changes training parameters such as error value thresholds, training signal characteristics, or adaptation coefficients based on detected noise conditions. When impulse noise is detected, the system can modify these parameters to account for noise influence, allowing for more reliable adaptation without necessarily extending the training time proportionally to the noise level.
3Productivity
If error values are used directly for vectoring adaptation without noise evaluation, then processing speed is improved, but bit error rates increase due to misadaptation caused by impulse noise dominance
Solution Approach 1:
The system introduces an intermediary noise evaluation step between error value calculation and vectoring adaptation. Rather than directly using error values for adaptation, the system first evaluates whether impulse noise is dominating the error values. This intermediary evaluation acts as a filter that prevents noisy error values from causing misadaptation, thereby maintaining both processing efficiency and connection stability.
Solution Approach 2:
The system extracts and separates the noise component from the error value calculation process. By detecting and identifying impulse noise influence, the system can exclude or de-emphasize noise-dominated error values from the vectoring adaptation process, using only the reliable portions of error measurements. This extraction of noise from the adaptation process prevents bit errors while maintaining processing speed.
Data Source
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AI summary
In an embodiment, vector training signals are received. Noise affecting the training signals is evaluated, and a noise indication is thus determined.