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:
Oct. 01, 2019

Filed:

Aug. 20, 2018
Applicant:

Deepsig Inc., Arlington, VA (US);

Inventors:

Timothy James O'Shea, Arlington, VA (US);

James Shea, Arlington, VA (US);

Ben Hilburn, Arlington, VA (US);

Assignee:

DeepSig Inc., Arlington, VA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G01S 5/00 (2006.01); G01S 5/02 (2010.01); G01S 11/06 (2006.01); G06N 20/00 (2019.01);
U.S. Cl.
CPC ...
G01S 5/0268 (2013.01); G01S 5/021 (2013.01); G01S 5/0215 (2013.01); G01S 11/06 (2013.01); G06N 20/00 (2019.01);
Abstract

Methods and systems including computer programs encoded on computer storage media, for training and deploying machine-learned communication over radio frequency (RF) channels. One of the methods includes: determining first information; generating a first RF signal by processing using an encoder machine-learning network; determining a second RF signal that represents the first RF signal altered by transmission through a communication channel; determining a first property of the first signal or the second RF signal; calculating a first measure of distance between a target value of the first property and an actual value of the first or second RF signal; generating second information as a reconstruction of the first information using a decoder machine-learning network; calculating a second measure of distance between the first information and the second information; and updating at least one of the encoder machine-learning network or the decoder machine-learning network based on the first and second measures.


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