Invention Grant
- Patent Title: Digital predistortion with hybrid basis-function-based actuator and neural network
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Application No.: US17732764Application Date: 2022-04-29
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Publication No.: US12028188B2Publication Date: 2024-07-02
- Inventor: Tao Yu , Cristobal Alessandri , Wenjie Lu
- Applicant: Analog Devices, Inc.
- Applicant Address: US MA Wilmington
- Assignee: Analog Devices, Inc.
- Current Assignee: Analog Devices, Inc.
- Current Assignee Address: US MA Wilmington
- Agency: ARENTFOX SCHIFF LLP
- Main IPC: H04L25/02
- IPC: H04L25/02 ; H04L25/03 ; H04L25/49 ; H04L27/36

Abstract:
Systems, devices, and methods related to hybrid basis function, neural network-based digital predistortion (DPD) are provided. An example apparatus for a radio frequency (RF) transceiver includes a digital predistortion (DPD) actuator to receive an input signal associated with a nonlinear component of the RF transceiver and output a predistorted signal. The DPD actuator includes a basis-function-based actuator to perform a first DPD operation using a set of basis functions associated with a first nonlinear characteristic of the nonlinear component. The DPD actuator further includes a neural network-based actuator to perform a second DPD operation using a first neural network associated with a second nonlinear characteristic of the nonlinear component. The predistorted signal is based on a first output signal of the basis-function-based actuator and a second output signal of the neural network-based actuator.
Public/Granted literature
- US20220368571A1 DIGITAL PREDISTORTION WITH HYBRID BASIS-FUNCTION-BASED ACTUATOR AND NEURAL NETWORK Public/Granted day:2022-11-17
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