Machine Learning BER Estimation via Waveform Images
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Solution Overview
Problem
Conventional bit error ratio (BER) testing is time-consuming and costly, requiring large numbers of bits to compute small BER values, and often necessitates specialized equipment, including a behavioral receiver equalizer that is hard or costly to implement.
Innovation Solution
A machine learning-based system using an oscilloscope to transform waveforms into composite waveform images, which are then used to estimate BER values, reducing the need for specialized equipment and significantly decreasing testing time by leveraging dynamic range compression techniques like gamma correction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional BER testing methods are used with dedicated equipment, then measurement precision is maintained, but productivity is severely reduced due to lengthy testing times
Solution Approach 1:
The patent replaces the conventional mechanical/bit-by-bit BER testing system with a machine learning-based system that uses waveform images and neural networks. Instead of transmitting large numbers of bits through dedicated BER equipment, the system captures waveforms, converts them to images, and uses trained neural networks to estimate BER values, achieving both high precision and fast testing speeds
Solution Approach 2:
The patent changes the fundamental parameters of BER testing by transitioning from bit-counting methods to waveform image analysis. The system transforms the testing approach by using composite waveform images as input features and applying machine learning models to predict BER, fundamentally altering how BER measurements are obtained while maintaining accuracy and improving speed
2Measurement precision
If dedicated BER testing equipment is used, then measurement precision is ensured, but device complexity and cost increase due to specialized components
Solution Approach 1:
The patent makes the testing system universal by using an oscilloscope—a general-purpose instrument already present in most labs—instead of dedicated BER testing equipment. The machine learning model can handle various waveform types and devices under test, making the system multi-functional and eliminating the need for specialized BER testers
Solution Approach 2:
The patent creates a virtual model of the BER testing process through machine learning. Instead of using physical specialized equipment, the system uses trained neural networks that have learned to predict BER from waveform images, effectively copying the functionality of dedicated equipment through software-based intelligence
3Measurement precision
If large numbers of bits are transmitted for testing, then measurement precision improves, but loss of time increases significantly
Solution Approach 1:
The patent applies preliminary action by training the machine learning model in advance using large datasets. During actual testing, the pre-trained model can quickly estimate BER from waveform images without needing to process large numbers of bits in real-time. The time-consuming data collection and model training are performed beforehand, enabling fast testing afterward
Solution Approach 2:
The patent substitutes the time-consuming mechanical process of transmitting and counting bits with a faster image-based machine learning approach. The system captures waveforms, converts them to images, and uses neural networks to estimate BER, eliminating the need for lengthy bit transmission sequences while maintaining measurement precision
Data Source
AI summary
A test and measurement system includes a machine learning system, a test and measurement device including a port configured to connect the test and measurement device to a device under test (DUT), and one or more processors, configured to execute code that causes the one or more processors to: acquire a waveform from the device under test (DUT), transform the waveform into a composite waveform image, and send the composite waveform image to the machine learning system to obtain a bit error ratio (BER) value for the DUT. A method of determining a bit error ratio for a device under test (DUT), includes acquiring one or more waveforms from the DUT, transforming the one or more waveforms into a composite waveform image, and sending the composite waveform image to a machine learning system to obtain a bit error ratio (BER) value for the DUT.