公开(公告)号：US11699086B1

公开(公告)日：2023-07-11

申请号：US17732683

申请日：2022-04-29

Applicant: Virginia Tech Intellectual Properties, Inc.

Inventor： Timothy James O'Shea ,  Kiran Karra ,  T. Charles Clancy

IPC: G06N5/046 ,  H04B17/309 ,  G06N20/00 ,  H04B17/391 ,  G06N3/02

CPC classification number: G06N5/046 ,  G06N3/02 ,  G06N20/00 ,  H04B17/309 ,  H04B17/3912

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training and deploying machine-learning estimation networks in a communications system. One of the methods includes: processing first information with ground truth information to generate a first RF signal by altering the first information by channel impairment having at least one channel effect, using a receiver to process the first RF signal to generate second information, training a machine-learning estimation network based on a network architecture, the second information, and the ground truth information, receiving by the receiver a second RF signal transmitted through a communication channel including the at least one channel effect, inferring by the trained estimation network the receiver to estimate an offset of the second RF signal caused by the at least one channel effect, and correcting the offset of the RF signal with the estimated offset to obtain a recovered RF signal.

公开(公告)号：US11334807B1

公开(公告)日：2022-05-17

申请号：US16017904

申请日：2018-06-25

Applicant: Virginia Tech Intellectual Properties, Inc.

Inventor： Timothy James O'Shea ,  Kiran Karra ,  T. Charles Clancy

IPC: G06N5/04 ,  G06N3/02 ,  H04B17/309 ,  H04B17/391 ,  G06N20/00

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training and deploying machine-learning estimation networks in a communications system. One of the methods includes: processing first information with ground truth information to generate a first RF signal by altering the first information by channel impairment having at least one channel effect, using a receiver to process the first RF signal to generate second information, training a machine-learning estimation network based on a network architecture, the second information, and the ground truth information, receiving by the receiver a second RF signal transmitted through a communication channel including the at least one channel effect, inferring by the trained estimation network the receiver to estimate an offset of the second RF signal caused by the at least one channel effect, and correcting the offset of the RF signal with the estimated offset to obtain a recovered RF signal.

公开(公告)号：US11630996B1

公开(公告)日：2023-04-18

申请号：US16017396

申请日：2018-06-25

Applicant: Virginia Tech Intellectual Properties, Inc.

Inventor： Timothy James O'Shea ,  Tamoghna Roy

IPC: G06N3/08 ,  H04B1/16

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training and deploying machine-learned classification of radio frequency (RF) signals. One of the methods includes obtaining input data corresponding to the RF spectrum; segmenting the input data into one or more samples; and for each sample of the one or more samples: obtaining information included in the sample, comparing the information to one or more labeled signal classes that are known to the machine-learning network, using results of the comparison, determining whether the information corresponds to the one or more labeled signal classes, and in response, matching, using an identification policy of a plurality of policies available to the machine-learning network, the information to a class of the one or more labeled signal classes, and providing an output that identifies an information signal corresponding to the class matching the information obtained from the sample.

公开(公告)号：US10541765B1

公开(公告)日：2020-01-21

申请号：US16549011

申请日：2019-08-23

Applicant: Virginia Tech Intellectual Properties, Inc.

Inventor： Timothy James O'Shea ,  Thomas Charles Clancy, III

IPC: H04B17/391 ,  H04B17/10 ,  H04B17/373 ,  H04L25/02 ,  G06N3/04 ,  H04L25/03 ,  G06N20/00 ,  G06N5/04 ,  H04B17/24

Abstract: One or more processors control processing of radio frequency (RF) signals using a machine-learning network. The one or more processors receive as input, to a radio communications apparatus, a first representation of an RF signal, which is processed using one or more radio stages, providing a second representation of the RF signal. Observations about, and metrics of, the second representation of the RF signal are obtained. Past observations and metrics are accessed from storage. Using the observations, metrics and past observations and metrics, parameters of a machine-learning network, which implements policies to process RF signals, are adjusted by controlling the radio stages. In response to the adjustments, actions performed by one or more controllers of the radio stages are updated. A representation of a subsequent input RF signal is processed using the radio stages that are controlled based on actions including the updated one or more actions.

公开(公告)号：US10217047B2

公开(公告)日：2019-02-26

申请号：US15970324

申请日：2018-05-03

Applicant: Virginia Tech Intellectual Properties, Inc.

Inventor： Timothy James O'Shea

IPC: G06N3/08 ,  G06N3/04

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.

公开(公告)号：US11863258B2

公开(公告)日：2024-01-02

申请号：US17856611

申请日：2022-07-01

Applicant: Virginia Tech Intellectual Properties, Inc.

Inventor： Timothy James O'Shea ,  Tugba Erpek

IPC: H04B7/04 ,  H04B7/0452 ,  G06N3/08 ,  H04B7/0413 ,  G06N3/088 ,  G06N3/044 ,  G06N3/045 ,  H04B7/06 ,  G06N3/086 ,  G06N3/006 ,  G06N3/082 ,  G06N3/048

CPC classification number: H04B7/0452 ,  G06N3/044 ,  G06N3/045 ,  G06N3/08 ,  G06N3/088 ,  H04B7/0413 ,  G06N3/006 ,  G06N3/048 ,  G06N3/082 ,  G06N3/086 ,  H04B7/0626

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training and deploying machine-learned communication over multi-input-multi-output (MIMO) channels. One of the methods includes: determining a transmitter and a receiver, at least one of which implements a machine-learning network; determining a MIMO channel model; determining first information; using the transmitter to process the first information and generate first RF signals representing inputs to the MIMO channel model; determining second RF signals representing outputs of the MIMO channel model, each second RF signal representing aggregated reception of the first RF signals altered by transmission through the MIMO channel model; using the receiver to process the second RF signals and generate second information as a reconstruction of the first information; calculating a measure of distance between the second and first information; and updating the machine-learning network based on the measure of distance between the second and first information.

公开(公告)号：US11783196B2

公开(公告)日：2023-10-10

申请号：US16864516

申请日：2020-05-01

Applicant: Virginia Tech Intellectual Properties, Inc.

Inventor： Timothy James O'Shea

IPC: G06N3/086 ,  H04W24/08 ,  G06N20/00 ,  H04B17/30 ,  G06N3/045 ,  G06N3/08

CPC classification number: G06N3/086 ,  G06N3/045 ,  G06N3/08 ,  G06N20/00 ,  H04B17/30 ,  H04W24/08

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training and deploying machine-learned identification of radio frequency (RF) signals. One of the methods includes: determining an RF signal configured to be transmitted through an RF band of a communication medium; determining first classification information that is associated with the RF signal, and that includes a representation of a characteristic of the RF signal or a characteristic of an environment in which the RF signal is communicated; using at least one machine-learning network to process the RF signal and generate second classification information as a prediction of the first classification information; calculating a measure of distance between (i) the second classification information that was generated by the at least one machine-learning network, and (ii) the first classification information associated with the RF signal; and updating the at least one machine-learning network based on the measure of distance.

公开(公告)号：US10396919B1

公开(公告)日：2019-08-27

申请号：US15978920

申请日：2018-05-14

Applicant: Virginia Tech Intellectual Properties, Inc.

Inventor： Timothy James O'Shea ,  Thomas Charles Clancy, III

IPC: G06N3/04 ,  G06N5/04 ,  G06N20/00 ,  H04B17/10 ,  H04B17/24 ,  H04L25/02 ,  H04L25/03 ,  H04B17/373 ,  H04B17/391

Abstract: One or more processors control processing of radio frequency (RF) signals using a machine-learning network. The one or more processors receive as input, to a radio communications apparatus, a first representation of an RF signal, which is processed using one or more radio stages, providing a second representation of the RF signal as. Observations about, and metrics of, the second representation of the RF signal are obtained. Past observations and metrics are accessed from storage. Using the observations, metrics and past observations and metrics, parameters of a machine-learning network, which implements policies to process RF signals, are adjusted by controlling the radio stages. In response to the adjustments, actions performed by one or more controllers of the radio stages are updated. A representation of a subsequent input RF signal is processed using the radio stages that are controlled based on actions including the updated one or more actions.

公开(公告)号：US10305553B2

公开(公告)日：2019-05-28

申请号：US16012691

申请日：2018-06-19

Applicant: Virginia Tech Intellectual Properties, Inc.

Inventor： Timothy James O'Shea ,  Tugba Erpek

IPC: H04B7/0413 ,  H04B7/0452 ,  G06N3/08 ,  H04B7/06

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training and deploying machine-learned communication over multi-input-multi-output (MIMO) channels. One of the methods includes: determining a transmitter and a receiver, at least one of which implements a machine-learning network; determining a MIMO channel model; determining first information; using the transmitter to process the first information and generate first RF signals representing inputs to the MIMO channel model; determining second RF signals representing outputs of the MIMO channel model, each second RF signal representing aggregated reception of the first RF signals altered by transmission through the MIMO channel model; using the receiver to process the second RF signals and generate second information as a reconstruction of the first information; calculating a measure of distance between the second and first information; and updating the machine-learning network based on the measure of distance between the second and first information.

公开(公告)号：US10296831B2

公开(公告)日：2019-05-21

申请号：US15970510

申请日：2018-05-03

Applicant: Virginia Tech Intellectual Properties, Inc.

Inventor： Timothy James O'Shea

IPC: G06N3/08 ,  G06N3/04 ,  H04B1/00 ,  H04B17/309

Abstract: Methods, systems, and apparatus, including computer programs encoded on a storage medium, for processing radio signals. In once aspect, a system is disclosed that includes a processor and a storage device storing computer code that includes operations. The operations may include obtaining first output data generated by a first neural network based on the first neural network processing a received radio signal, receiving, by a signal transformer, a second set of input data that includes (i) the received radio signal and (ii) the first output data, generating, by the signal transformer, data representing a transformed radio signal by applying one or more transforms to the received radio signal, providing the data representing the transformed radio signal to a second neural network, obtaining second output data generated by the second neural network, and determining based on the second output data a set of information describing the received radio signal.