Machine learning based methodology for adaptative equalization
US11423303B1 · kind B1 · utility
Assignee
Inventors
Key dates
| Filing date | Nov 21, 2019 |
| Grant date | Aug 23, 2022 |
| Priority date | — |
| Expiry date | Mar 16, 2041 |
Classification
- Technology area (CPC H)Electricity
- CPC primaryH03G3/3089
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Apparatus and associated methods relate to providing a machine learning methodology that uses the machine learning's own failure experiences to optimize future solution search and provide self-guided information (e.g., the dependency and independency among various adaptation behavior) to predict a receiver's equalization adaptations. In an illustrative example, a method may include performing a first training on a first neural network model and determining whether all of the equalization parameters are tracked. If not all of the equalization parameters are tracked under the first training, then, a second training on a cascaded model may be performed. The cascaded model may include the first neural network model, and training data of the second training may include successful learning experiences and data of the first neural network model. The prediction accuracy of the trained model may be advantageously kept while having a low demand for training data.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.