The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Apr. 02, 2024

Filed:

Jan. 11, 2021
Applicant:

Mitsubishi Electric Research Laboratories, Inc., Cambridge, MA (US);

Inventors:

Mouhacine Benosman, Boston, MA (US);

Rui Ma, Carlisle, MA (US);

Chouaib Kantana, Paris, FR;

Richard C Waters, Lincoln, MA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06N 3/065 (2023.01); G06F 18/214 (2023.01); G06F 18/2415 (2023.01); G06N 3/08 (2023.01); H03F 3/20 (2006.01); H03F 3/26 (2006.01);
U.S. Cl.
CPC ...
G06N 3/065 (2023.01); G06F 18/214 (2023.01); G06F 18/24155 (2023.01); G06N 3/08 (2013.01); H03F 3/20 (2013.01); H03F 3/26 (2013.01);
Abstract

An auto-tuning controller for improving a performance of a power amplifier system is provided. The controller includes an interface including input terminals and output terminals, the interface being configured to acquire input signal conditions of power amplifiers (PAs), a training circuit including a processor and a memory running and storing a Digital Doherty amplifier (DDA) controller (module), a DPD controller (module) and a DDA-DPD neural network (NN). The training circuit is configured to perform sampling the input signal conditions, and selecting a DPD model from a set of polynomial models for the DPD controller and a set of DDA optimization variables for the DDA controller, using optimized DPD model and DDA coefficients, wherein the optimized DPD model and DDA coefficients are provided by performing an offline optimization for the DPD model and DDA coefficients based on a predetermined optimization method, collecting the optimized DPD coefficients and optimized DDA optimization variables, generating online-DDA optimal coefficients and DPD optimal coefficients using a trained DDA-DPD NN, and updating the generated optimal DDA and DPD coefficients via the output terminals of the interface.


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