Machine learning based methodology for signal waveform, eye diagram, and bit error rate (BER) bathtub prediction
US11621808B1 · kind B1 · utility
Assignee
Inventors
Key dates
| Filing date | Oct 16, 2019 |
| Grant date | Apr 4, 2023 |
| Priority date | — |
| Expiry date | Sep 3, 2041 |
Classification
- Technology area (CPC H)Electricity
- CPC primaryH04L43/50
- WIPO fieldDigital communication
- WIPO sectorElectrical engineering
Abstract
Apparatus and associated methods relate to predicting various transient output waveforms at a receiver's output after an initial neural network model is trained by a receiver's transient input waveform and a corresponding transient output waveform. In an illustrative example, the machine learning model may include an adaptive-ordered auto-regressive moving average external input based on neural networks (NNARMAX) model designed to mimic the performance of a continuous time linear equalization (CTLE) mode of the receiver. A Pearson Correlation Coefficient (PCC) score may be determined to select numbers of previous inputs and previous outputs to be used in the neural network model. In other examples, corresponding bathtub characterizations and eye diagrams may be extracted from the predicted transient output waveforms. Providing a machine learning model may, for example, advantageously predict various data patterns without knowing features or parameters of the receiver or related channels.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.