Echo Detection Using Vector Correlation in Telecommunications
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Solution Overview
Problem
Existing echo detection methods in telecommunications networks are unreliable in detecting time-invariant or time-varying echo from electrical or acoustical sources, especially in the presence of non-linear network elements, and often require complex algorithms and are susceptible to noise and misconfiguration.
Innovation Solution
A method using a similarity metric, such as a correlation function, to compare Fourier transforms of inbound and outbound signals to determine the presence and delay of echo by generating vectors and counting relative positions with maximum correlation, capable of operating in various operational conditions including non-linear elements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If complex algorithms are used for echo detection, then detection reliability improves, but computational complexity increases
Solution Approach 1:
The signal processing is divided into separate stages: generating outbound vectors from outbound signal, generating inbound vectors from inbound signal, comparing vectors to generate similarity metrics, and determining relative positions. This segmentation allows each stage to be optimized independently, maintaining reliability while reducing overall computational complexity.
Solution Approach 2:
The invention extracts only the essential features needed for echo detection by generating vectors from the signals and comparing them to produce similarity metrics. This extraction approach focuses computational resources on the most relevant signal characteristics, improving reliability without requiring complex full-signal analysis.
2Reliability
If existing echo detection methods are used, then echo detection is performed, but reliability deteriorates in the presence of non-linear network elements
Solution Approach 1:
The invention changes the parameter representation by transforming signals into vectors and using similarity metrics instead of direct signal comparison. This parameter transformation makes the detection method robust to non-linear distortions, as vector comparison is less sensitive to non-linear effects than raw signal comparison.
Solution Approach 2:
The method uses iterative vector comparison and similarity metric calculation that effectively provides feedback on the correlation between outbound and inbound signals. This feedback mechanism allows the system to adapt to varying echo conditions including non-linear elements, improving reliability across different network configurations.
3Reliability
If traditional echo detection methods are used, then detection is performed, but computational complexity increases
Solution Approach 1:
The invention performs preliminary actions by generating outbound and inbound vectors before the actual echo detection comparison. These pre-computed vectors contain the essential signal characteristics, which speeds up the subsequent similarity metric calculation and relative position determination, improving overall processing efficiency.
Solution Approach 2:
The method creates simplified copies of the signals in the form of vectors that capture the essential characteristics needed for echo detection. These vector representations are computationally lighter than full signal processing while maintaining detection accuracy, thus improving productivity without sacrificing reliability.
Data Source
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AI summary
The present invention relates to detection of echo in telecommunications networks. The invention provides a method of echo detection comprising the steps of: generating a series of inbound vectors for an inbound signal; generating a series of outbound vectors for an outbound signal; repeating a predetermined number of comparison steps comprising the sub-steps of selecting an outbound vector from the outbound vectors; selecting an inbound vector from the inbound vectors; comparing said outbound vector with said inbound vector and with successive inbound vectors to generate a plurality of similarity metrics; and determining a relative position of the compared outbound vector having maximum correlation with said inbound vector; and counting the number of times each relative position is determined to be the position of maximum correlation.