Digital Signal Error Distribution Evaluation Using Chi-Square Test
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Solution Overview
Problem
Current methods are incomplete in generating random error signals with error distributions accurately following the Poisson distribution and in quantitatively evaluating the conformity of error distributions in digital signals to the Poisson distribution, which affects the accuracy of testing apparatuses in communication networks.
Innovation Solution
A method and apparatus that divide digital signals into measurement units, calculate average measurement values, determine the number of occurrences, compute the Poisson distribution, and use the chi-square value to assess conformity to the Poisson distribution by comparing measured and expected values, determining if the error distribution conforms to the Poisson distribution based on a designated significance level.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If conventional random error signal generation methods are used, then error signals can be generated with a designated error occurrence rate, but the error distribution cannot accurately follow the Poisson distribution
Solution Approach 1:
The digital signal is divided into multiple measurement units, each containing a specific number of data items determined based on the error rate. This segmentation allows for accurate statistical analysis of error distribution while maintaining manageable processing complexity.
Solution Approach 2:
The measurement unit data items are determined in advance based on the error rate before actual error measurement. This preliminary determination ensures that the statistical analysis will accurately reflect Poisson distribution characteristics without requiring complex real-time adjustments.
2Measurement precision
If quantitative evaluation of Poisson distribution conformity is implemented, then test accuracy is improved, but the evaluation process becomes more complex
Solution Approach 1:
The patent replaces complex statistical evaluation mechanisms with a simplified chi-square value calculation approach. By using the chi-square statistic to compare measured error distribution against expected Poisson distribution, the system achieves accurate quantitative evaluation without requiring complex computational resources or multiple evaluation stages.
Data Source
AI summary
A degree of conformity of error distribution of a digital signal to the Poisson distribution is quantitatively determined. The digital signal including error data, which is randomly generated at a predetermined error rate, is divided into data number of measurement units, wherein the data number is determined on the basis of the error rate. A sample number of the measurement units are acquired from the measurement units, and the number of errors contained in each measurement unit is measured as a measurement value. Further, the number of times of occurrence of each measurement value is calculated, a Poisson distribution function is calculated, and a degree of a bond between the Poisson distribution and the distribution of the number of times of occurrence is determined by using the chi-square goodness-of-fit test method.