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:
Sep. 15, 2026

Filed:

Jan. 18, 2022
Applicant:

Nec Corporation, Tokyo, JP;

Inventors:

Ankith Vinayachandran, Tokyo, JP;

Shinsuke Fujisawa, Tokyo, JP;

Naoto Ishii, Tokyo, JP;

Hidemi Noguchi, Tokyo, JP;

Emmanuel Le Taillandier De Gabory, Tokyo, JP;

Assignee:

NEC CORPORATION, Tokyo, JP;

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
H04B 10/588 (2013.01); H04B 10/2507 (2013.01); H04B 10/58 (2013.01); H04B 10/60 (2013.01);
U.S. Cl.
CPC ...
H04B 10/2507 (2013.01); H04B 10/58 (2013.01); H04B 10/60 (2013.01);
Abstract

A calibration apparatus trains a first machine learning-based model with a first training dataset to determine configuration parameters of an intermediate pre-distortion compensator in an optical communication system that includes a transmitter, a receiver, and an optical communication channel. The transmitter includes a pre-distortion compensator, the intermediate pre-distortion compensator, and an MZM compensator. The calibration apparatus trains a second machine learning-based model with a second training dataset to determine configuration parameters of the post-distortion compensator in the receiver. The calibration apparatus trains a third machine learning-based model with a third training dataset to determine configuration parameters of the pre-distortion compensator. When generating the second training data, the intermediate pre-distortion compensator is configured with the configuration parameters generated using the first machine learning-based model. When generating the third training data, the post-distortion compensator is configured with the configuration parameters generated using the second machine learning-based model.


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