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US Patent 10217047 Learning and deployment of adaptive wireless communications

Patent 10217047 was granted and assigned to Virginia Tech Intellectual Properties on February, 2019 by the United States Patent and Trademark Office.

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Contents

Is a
Patent
Patent

Patent attributes

Patent Applicant
Virginia Tech Intellectual Properties
Virginia Tech Intellectual Properties
Current Assignee
Virginia Tech Intellectual Properties
Virginia Tech Intellectual Properties
Patent Jurisdiction
United States Patent and Trademark Office
United States Patent and Trademark Office
Patent Number
10217047
Date of Patent
February 26, 2019
Patent Application Number
15970324
Date Filed
May 3, 2018
Patent Citations Received
0
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US Patent 11423301 Learning and deployment of adaptive wireless communications
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US Patent 11863221 Low size, weight and power (swap) efficient hardware implementation of a wide instantaneous bandwidth neuromorphic adaptive core (NeurACore)
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US Patent 11468328 Managing information transmission for wireless communication
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US Patent 11233561 Learning-based space communications systems
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US Patent 11533658 Compressed measurement feedback using an encoder neural network
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US Patent 10746843 Method and system for learned communications signal shaping
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US Patent 10749594 Learning-based space communications systems
...
Patent Primary Examiner
‌
Alan Chen
Patent abstract

Methods, systems, and apparatus, 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; using an encoder machine-learning network to process the first information and generate a first RF signal for transmission through a communication channel; determining a second RF signal that represents the first RF signal having been altered by transmission through the communication channel; using a decoder machine-learning network to process the second RF signal and generate second information as a reconstruction of the first information; calculating a measure of distance between the second information and the first information; and updating at least one of the encoder machine-learning network or the decoder machine-learning network based on the measure of distance between the second information and the first information.

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