Delay Estimation Using Quantized Signal Co-occurrences
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Solution Overview
Problem
Existing delay estimation methods for echo cancellation in communications networks face high computational complexity, sensitivity to nonlinear distortions, and difficulties in handling time-varying delays, particularly when using cross-correlation methods.
Innovation Solution
A method based on joint probability of signal measurements at a reference and reception point, using quantized values to count co-occurrences and assign significances, which reduces computational effort and improves accuracy by considering the probability of co-occurrences to estimate delays effectively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If cross-correlation methods are used for delay estimation, then measurement precision is improved, but device complexity increases due to high computational complexity
Solution Approach 1:
The patent extracts only the essential information needed for delay estimation by quantizing signal measurements into discrete levels. Instead of processing complete continuous signal waveforms through complex correlation operations, the method extracts amplitude threshold exceedance events and their timing, reducing the data to essential delay-relevant features while discarding redundant information.
Solution Approach 2:
The patent changes the parameter representation from continuous signal amplitudes to discrete quantized levels. By transforming the continuous measurement space into discrete bins and using histogram-based probability distributions, the method enables efficient computational processing while maintaining delay estimation accuracy. The parameter transformation from time-domain waveforms to frequency-domain histograms facilitates lower complexity operations.
2Measurement precision
If cross-correlation methods are used for delay estimation, then measurement precision is improved, but productivity decreases due to high computational effort
Solution Approach 1:
The patent segments the continuous signal processing task into discrete quantized levels and time bins. By dividing the amplitude range into thresholds and creating histograms of threshold exceedance events across time segments, the method transforms a single complex correlation computation into multiple simpler counting and aggregation operations that can be performed more efficiently.
Solution Approach 2:
The patent replaces the mechanical computation of continuous cross-correlation with a statistical histogram-based approach. Instead of performing intensive arithmetic operations on continuous waveforms, the method uses discrete counting of threshold exceedance events and probability distribution comparisons, which are computationally less demanding and can be implemented with simpler logic operations.
3Measurement precision
If long evaluation intervals are used for delay estimation, then measurement precision is improved, but device complexity increases due to large storage requirements
Solution Approach 1:
The patent extracts only the essential temporal relationship information needed for delay estimation by using histograms to count threshold exceedance events. Instead of storing complete signal waveforms or large sets of correlation computations over long intervals, the method extracts and stores only the probability distribution of time differences between threshold events, which is sufficient for delay estimation and requires minimal storage.
4Measurement precision
If cross-correlation methods are used for delay estimation, then measurement precision is improved, but reliability decreases due to sensitivity to nonlinear distortions
Solution Approach 1:
The patent changes the measurement parameters from continuous signal amplitudes to discrete quantized levels and uses histogram-based probability distributions. This parameter transformation makes the delay estimation more robust to nonlinear distortions because the discrete threshold-based approach and statistical aggregation are less sensitive to amplitude nonlinearities and distortion effects that would significantly impact continuous correlation-based methods.
Data Source
AI summary
A method and apparatus for finding an estimate of the delay of a signal travelling between two points. A quantity is evaluated from the signal at a final number of time instants, at both a reference point and a reception point. The values are quantized by comparison with a threshold adapted to a typical magnitude of the quantity. If the quantized values from the reception point are shifted back by the true delay with respect to the quantized values from the reference point, then certain co-occurrences of quantized values have very low probability. Hence, the best delay estimate is that shift which yields the least number of low-probability co-occurrences.