Integrating volterra series model and deep neural networks to equalize nonlinear power amplifiers
US11451419B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Apr 19, 2021 |
| Grant date | Sep 20, 2022 |
| Priority date | — |
| Expiry date | Apr 19, 2041 |
Classification
- Technology area (CPC H)Electricity
- CPC primaryH04L25/0224
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
The nonlinearity of power amplifiers (PAs) has been a severe constraint in performance of modern wireless transceivers. This problem is even more challenging for the fifth generation (5G) cellular system since 5G signals have extremely high peak to average power ratio. Non-linear equalizers that exploit both deep neural networks (DNNs) and Volterra series models are provided to mitigate PA nonlinear distortions. The DNN equalizer architecture consists of multiple convolutional layers. The input features are designed according to the Volterra series model of nonlinear PAs. This enables the DNN equalizer to effectively mitigate nonlinear PA distortions while avoiding over-fitting under limited training data. The non-linear equalizers demonstrate superior performance over conventional nonlinear equalization approaches.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.