公开(公告)号：US20230055213A1

公开(公告)日：2023-02-23

申请号：US17889420

申请日：2022-08-17

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 method includes: determining an encoder and a decoder, at least one of which is configured to implement an encoding or decoding that is based on at least one of an encoder machine-learning network or a decoder machine-learning network that has been trained to encode or decode information over a communication channel; determining first information; using the encoder to process the first information and generate a first RF signal; transmitting, by at least one transmitter, the first RF signal through the communication channel; receiving, by at least one receiver, a second RF signal that represents the first RF signal altered by transmission through the communication channel; and using the decoder to process the second RF signal and generate second information as a reconstruction of the first information.

公开(公告)号：US20230136529A1

公开(公告)日：2023-05-04

申请号：US17962007

申请日：2022-10-07

Applicant: Virginia Tech Intellectual Properties, Inc.

Inventor： Timothy James O`Shea

IPC: G06N3/08 ,  H04B1/00 ,  G06N3/045

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.

公开(公告)号：US20200334575A1

公开(公告)日：2020-10-22

申请号：US16864516

申请日：2020-05-01

Applicant: Virginia Tech Intellectual Properties, Inc.

Inventor： Timothy James O`Shea

IPC: G06N20/00 ,  H04W24/08 ,  G06N3/04 ,  G06N3/08

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：US20190188565A1

公开(公告)日：2019-06-20

申请号：US16281246

申请日：2019-02-21

Applicant: Virginia Tech Intellectual Properties, Inc.

Inventor： Timothy James O`Shea

IPC: G06N3/08

CPC classification number: G06N3/08 ,  G06N3/04 ,  G06N3/0445 ,  G06N3/0454 ,  G06N3/082

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 method includes: determining an encoder and a decoder, at least one of which is configured to implement an encoding or decoding that is based on at least one of an encoder machine-learning network or a decoder machine-learning network that has been trained to encode or decode information over a communication channel; determining first information; using the encoder to process the first information and generate a first RF signal; transmitting, by at least one transmitter, the first RF signal through the communication channel; receiving, by at least one receiver, a second RF signal that represents the first RF signal altered by transmission through the communication channel; and using the decoder to process the second RF signal and generate second information as a reconstruction of the first information.

公开(公告)号：US20230316083A1

公开(公告)日：2023-10-05

申请号：US18135259

申请日：2023-04-17

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 ,  H04W24/08 ,  G06N20/00 ,  H04B17/30 ,  G06N3/045 ,  G06N3/08

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training and deploying machine-learned compact representations of radio frequency (RF) signals. One of the methods includes: determining a first RF signal to be compressed; using an encoder machine-learning network to process the first RF signal and generate a compressed signal; calculating a measure of compression in the compressed signal; using a decoder machine-learning network to process the compressed signal and generate a second RF signal that represents a reconstruction of the first RF signal; calculating a measure of distance between the second RF signal and the first RF signal; and updating at least one of the encoder machine-learning network or the decoder machine-learning network based on (i) the measure of distance between the second RF signal and the first RF signal, and (ii) the measure of compression in the compressed signal.

公开(公告)号：US20230089393A1

公开(公告)日：2023-03-23

申请号：US17856611

申请日：2022-07-01

Applicant: Virginia Tech Intellectual Properties, Inc.

Inventor： Timothy James O`Shea ,  Tugba Erpek

IPC: H04B7/0452 ,  G06N3/08 ,  H04B7/0413 ,  G06N3/04

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：US20210367690A1

公开(公告)日：2021-11-25

申请号：US17339033

申请日：2021-06-04

Applicant: Virginia Tech Intellectual Properties, Inc.

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

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

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：US20210211164A1

公开(公告)日：2021-07-08

申请号：US17145501

申请日：2021-01-11

Applicant: Virginia Tech Intellectual Properties, Inc.

Inventor： Timothy James O`Shea ,  Tugba Erpek

IPC: H04B7/0452 ,  G06N3/08 ,  H04B7/0413 ,  G06N3/04

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：US20200266910A1

公开(公告)日：2020-08-20

申请号：US16744369

申请日：2020-01-16

Applicant: Virginia Tech Intellectual Properties, Inc.

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

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

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：US20180367192A1

公开(公告)日：2018-12-20

申请号：US16012691

申请日：2018-06-19

Applicant: Virginia Tech Intellectual Properties, Inc.

Inventor： Timothy James O`Shea ,  Tugba Erpek

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

CPC classification number: H04B7/0452 ,  G06N3/08 ,  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.