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:
Jul. 11, 2023

Filed:

Sep. 02, 2022
Applicant:

Nanjing University of Aeronautics and Astronautics, Nanjing, CN;

Inventors:

Fuhui Zhou, Nanjing, CN;

Rui Ding, Nanjing, CN;

Ming Xu, Nanjing, CN;

Hao Zhang, Nanjing, CN;

Lu Yuan, Nanjing, CN;

Qihui Wu, Nanjing, CN;

Chao Dong, Nanjing, CN;

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
H04L 27/00 (2006.01); G06N 3/08 (2023.01); G06N 5/02 (2023.01);
U.S. Cl.
CPC ...
H04L 27/0012 (2013.01); G06N 3/08 (2013.01); G06N 5/02 (2013.01);
Abstract

An intelligent data and knowledge-driven method for modulation recognition includes the following steps: collecting spectrum data; constructing corresponding attribute vector labels for different modulation schemes; constructing and pre-training an attribute learning model based on the attribute vector labels for different modulation schemes; constructing and pre-training a visual model for modulation recognition; constructing a feature space transformation model, and constructing an intelligent data and knowledge-driven model for modulation recognition based on the attribute learning model and the visual model; transferring parameters of the pre-trained visual model and the pre-trained attribute learning model and retraining the transformation model; and determining whether training on a network is completed and outputting a classification result. The intelligent data and knowledge-driven method for modulation recognition significantly improves the recognition accuracy at low SNRs and reduces the confusion between higher-order modulation schemes.


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