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US Patent 11991658 Learning communication systems using channel approximation

Patent 11991658 was granted and assigned to DeepSig on May, 2024 by the United States Patent and Trademark Office.

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Contents

Is a
Patent
Patent
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Patent attributes

Patent Applicant
DeepSig
DeepSig
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Current Assignee
DeepSig
DeepSig
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Patent Jurisdiction
United States Patent and Trademark Office
United States Patent and Trademark Office
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Patent Number
119916580
Patent Inventor Names
Nathan West0
Tamoghna Roy0
Ben Hilburn0
Timothy J. O'Shea0
Date of Patent
May 21, 2024
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Patent Application Number
176740200
Date Filed
February 17, 2022
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Patent Citations
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US Patent 10531415 Learning communication systems using channel approximation
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US Patent 7133809 System, device, and method for time-domain equalizer training using a two-pass auto-regressive moving average model
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US Patent 10396919 Processing of communications signals using machine learning
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US Patent 11259260 Learning communication systems using channel approximation
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Patent Primary Examiner
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Mohamed A Kamara
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CPC Code
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H04B 17/3912
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H04B 17/391
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G06N 3/08
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G06N 3/10
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G06N 3/084
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G06N 20/00
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G06N 3/006
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G06N 3/126
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Patent abstract

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training and deploying machine-learned communication over RF channels. In some implementations, information is obtained. An encoder network is used to process the information and generate a first RF signal. The first RF signal is transmitted through a first channel. A second RF signal is determined that represents the first RF signal having been altered by transmission through the first channel. Transmission of the first RF signal is simulated over a second channel implementing a machine-learning network, the second channel representing a model of the first channel. A simulated RF signal that represents the first RF signal having been altered by simulated transmission through the second channel is determined. A measure of distance between the second RF signal and the simulated RF signal is calculated. The machine-learning network is updated using the measure of distance.

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