-
公开(公告)号：US20240072886A1
公开(公告)日：2024-02-29
申请号：US18240375
申请日：2023-08-31
Applicant: DeepSig Inc.
Inventor： Timothy James O`Shea , James Shea , Ben Hilburn
IPC: H04B7/185 , G06N20/00 , H04B17/318 , H04B17/336 , H04B17/345
CPC classification number: H04B7/18513 , G06N20/00 , H04B7/18521 , H04B7/18523 , H04B17/318 , H04B17/336 , H04B17/345
Abstract: Methods and systems including computer programs encoded on computer storage media, for training and deploying machine-learned communication over RF channels. One of the methods includes: determining first information; generating a first RF signal by processing the first information using an encoder machine-learning network of the first transceiver; transmitting the first RF signal from the first transceiver to a communications satellite or ground station through a first communication channel; receiving, from the communications satellite or ground station through a second communication channel, a second RF signal at a second transceiver; generating second information as a reconstruction of the first information by processing the second RF signal using a decoder machine-learning network of the second transceiver; calculating a measure of distance between the second information and the first information; and updating at least one of the encoder machine-learning network of the first transceiver or the decoder machine-learning network of the second transceiver.
-
公开(公告)号：US11831394B2
公开(公告)日：2023-11-28
申请号：US17582575
申请日：2022-01-24
Applicant: DeepSig Inc.
Inventor： Timothy James O'Shea , James Shea , Ben Hilburn
IPC: H04B17/345 , H04B7/185 , H04B17/318 , H04B17/336 , G06N20/00
CPC classification number: H04B7/18513 , G06N20/00 , H04B7/18521 , H04B7/18523 , H04B17/318 , H04B17/336 , H04B17/345
Abstract: Methods and systems including computer programs encoded on computer storage media, for training and deploying machine-learned communication over RF channels. One of the methods includes: determining first information; generating a first RF signal by processing the first information using an encoder machine-learning network of the first transceiver; transmitting the first RF signal from the first transceiver to a communications satellite or ground station through a first communication channel; receiving, from the communications satellite or ground station through a second communication channel, a second RF signal at a second transceiver; generating second information as a reconstruction of the first information by processing the second RF signal using a decoder machine-learning network of the second transceiver; calculating a measure of distance between the second information and the first information; and updating at least one of the encoder machine-learning network of the first transceiver or the decoder machine-learning network of the second transceiver.
-
公开(公告)号：US10749594B1
公开(公告)日：2020-08-18
申请号：US15999025
申请日：2018-08-20
Applicant: DeepSig Inc.
Inventor： Timothy James O'Shea , James Shea , Ben Hilburn
IPC: H04B17/345 , H04B7/185 , H04B17/318 , H04B17/336 , G06N20/00
Abstract: Methods and systems including computer programs encoded on computer storage media, for training and deploying machine-learned communication over RF channels. One of the methods includes: determining first information; generating a first RF signal by processing the first information using an encoder machine-learning network of the first transceiver; transmitting the first RF signal from the first transceiver to a communications satellite or ground station through a first communication channel; receiving, from the communications satellite or ground station through a second communication channel, a second RF signal at a second transceiver; generating second information as a reconstruction of the first information by processing the second RF signal using a decoder machine-learning network of the second transceiver; calculating a measure of distance between the second information and the first information; and updating at least one of the encoder machine-learning network of the first transceiver or the decoder machine-learning network of the second transceiver.
-
公开(公告)号：US11777540B1
公开(公告)日：2023-10-03
申请号：US17327946
申请日：2021-05-24
Applicant: DeepSig Inc.
Inventor： Timothy James O'Shea , James Shea
CPC classification number: H04B1/0475 , G06N3/04 , G06N3/08 , H04W88/08
Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for correcting distortion of radio signals A transmit radio signal corresponding to an output of a transmitting radio signal processing system is obtained. A pre-distorted radio signal is then generated by processing the transmit radio signal using a nonlinear pre-distortion machine learning model. The nonlinear pre-distortion machine learning model includes model parameters and at least one nonlinear function to correct radio signal distortion or interference. A transmit output radio signal is obtained by processing the pre-distorted radio signal through the transmitting radio signal processing system. The transmit output radio signal is then transmitted to one or more radio receivers.
-
公开(公告)号：US10746843B2
公开(公告)日：2020-08-18
申请号：US16581849
申请日：2019-09-25
Applicant: DeepSig Inc.
Inventor： Timothy James O'Shea , James Shea , Ben Hilburn
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.
-
Patent Agency Ranking
公开(公告)号：US10581469B1
公开(公告)日：2020-03-03
申请号：US15955485
申请日：2018-04-17
Applicant: DeepSig Inc.
Inventor： Timothy James O'Shea , James Shea
Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for correcting distortion of radio signals A transmit radio signal corresponding to an output of a transmitting radio signal processing system is obtained. A pre-distorted radio signal is then generated by processing the transmit radio signal using a nonlinear pre-distortion machine learning model. The nonlinear pre-distortion machine learning model includes model parameters and at least one nonlinear function to correct radio signal distortion or interference. A transmit output radio signal is obtained by processing the pre-distorted radio signal through the transmitting radio signal processing system. The transmit output radio signal is then transmitted to one or more radio receivers.
-
公开(公告)号：US10429486B1
公开(公告)日：2019-10-01
申请号：US15998986
申请日：2018-08-20
Applicant: DeepSig Inc.
Inventor： Timothy James O'Shea , James Shea , Ben Hilburn
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.
-
公开(公告)号：US20230284048A1
公开(公告)日：2023-09-07
申请号：US18113201
申请日：2023-02-23
Applicant: DeepSig Inc.
Inventor： Timothy James O'Shea , Nathan West , Timothy Newman , James Shea , Jacob Gilbert , Tamoghna Roy
IPC: H04W24/02 , H04B17/336
CPC classification number: H04W24/02 , H04B17/336
Abstract: A method includes obtaining, using a specified protocol of a radio access network, low-level signal data corresponding to a radio frequency (RF) signal processed in the radio access network; providing the low-level signal data as input to at least one machine learning network; in response to providing the low-level signal data as input to the at least one machine learning network, obtaining, as an output of the at least one machine learning network, metadata providing information on one or more characteristics of the RF signal; and controlling an operation of the radio access network based on the metadata.
-
公开(公告)号：US11233561B1
公开(公告)日：2022-01-25
申请号：US16994741
申请日：2020-08-17
Applicant: DeepSig Inc.
Inventor： Timothy James O'Shea , James Shea , Ben Hilburn
IPC: H04B17/345 , H04B7/185 , G06N20/00 , H04B17/336 , H04B17/318
Abstract: Methods and systems including computer programs encoded on computer storage media, for training and deploying machine-learned communication over RF channels. One of the methods includes: determining first information; generating a first RF signal by processing the first information using an encoder machine-learning network of the first transceiver; transmitting the first RF signal from the first transceiver to a communications satellite or ground station through a first communication channel; receiving, from the communications satellite or ground station through a second communication channel, a second RF signal at a second transceiver; generating second information as a reconstruction of the first information by processing the second RF signal using a decoder machine-learning network of the second transceiver; calculating a measure of distance between the second information and the first information; and updating at least one of the encoder machine-learning network of the first transceiver or the decoder machine-learning network of the second transceiver.
-
公开(公告)号：US20200018815A1
公开(公告)日：2020-01-16
申请号：US16581849
申请日：2019-09-25
Applicant: DeepSig Inc.
Inventor： Timothy James O'Shea , James Shea , Ben Hilburn
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.
-
-
-
-
-
-
-
-
-
Search Results
14
records
(took 0.012 seconds)
