Deepsig Inc.

Virginia Tech Intellectual Properties, Inc.

Hawkeye 360, Inc.

Counter-drone Research Corporation

Timothy James O'Shea

Nicholas Aaron McCarthy

Ben Hilburn

Robert W McGwier

Nathan West

Tamoghna Roy

James P Shea

T Charles Clancy

Tugba Erpek

Thomas Charles Clancy, Iii

Johnathan Corgan

Kiran Karra

Edward Joseph Kreinar

Darek Kawamoto

Timothy A Newman

Nitin Nair

Daniel DePoy

Jacob Gilbert

Raj Bhattacharjea

Michael Piscopo

Justus Graham

Nicholas Aaron Mccarthy

Robert W Mcgwier

Methods, systems, and apparatus, including computer programs encoded on computer-storage media, for providing one or more values from a distribution of values to a neural network trained to generate simulated channel responses corresponding to one or more radio frequency (RF) communication channels; and obtaining an output of the neural network based on processing the one or more values by the neural network, the output indicating a simulated channel response corresponding to at least one communication channel of the one or more RF communication channels.

Generating variable communication channel responses using machine learning networks

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for radio frequency band segmentation, signal detection and labelling using machine learning. In some implementations, a sample of electromagnetic energy processed by one or more radio frequency (RF) communication receivers is received from the one or more receivers. The sample of electromagnetic energy is examined to detect one or more RF signals present in the sample. In response to detecting one or more RF signals present in the sample, the one or more RF signals are extracted from the sample, and time and frequency bounds are estimated for each of the one or more RF signals. For each of the one or more RF signals, at least one of a type of a signal present, or a likelihood of signal being present, in the sample is classified.

Radio frequency band segmentation, signal detection and labelling using machine learning

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.

Encoding and decoding of information for wireless transmission using multi-antenna transceivers

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training and deploying machine-learned communication. One of the methods includes: receiving an RF signal at a signal processing system for training a machine-learning network; providing the RF signal through the machine-learning network; producing an output from the machine-learning network; measuring a distance metric between the signal processing model output and a reference model output; determining modifications to the machine-learning network to reduce the distance metric between the output and the reference model output; and in response to reducing the distance metric to a value that is less than or equal to a threshold value, determining a score of the trained machine-learning network using one or more other RF signals and one or more other corresponding reference model outputs, the score indicating an a performance metric of the trained machine-learning network to perform the desired RF function.

Radio signal processing network model search

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.

Learning and deploying compression of radio signals

Methods, systems, and apparatus, including computer programs encoded on computer-storage media, for processing communications signals using a machine-learning network are disclosed. In some implementations, pilot and data information are generated for a data signal. The data signal is generated using a modulator for orthogonal frequency-division multiplexing (OFDM) systems. The data signal is transmitted through a communications channel to obtain modified pilot and data information. The modified pilot and data information are processed using a machine-learning network. A prediction corresponding to the data signal transmitted through the communications channel is obtained from the machine-learning network. The prediction is compared to a set of ground truths and updates, based on a corresponding error term, are applied to the machine-learning network.

Processing communications signals using a machine-learning network

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.

Learning approximate estimation networks for communication channel state information

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for placement and scheduling of radio signal processing dataflow operations. An example method provides a primitive radio signal processing computational dataflow graph that comprises nodes representing operations and directed edges representing data flow. The nodes and directed edges of the primitive radio signal processing computational dataflow graph are partitioned to produce a set of software kernels that, when executed on processing units of a target hardware platform, achieve a specific optimization objective. Runtime resource scheduling, including data placement for individual software kernels in the set of software kernels to efficiently execute operations on the processing units of the target hardware platform. The resources of the processing units in the target hardware platform are then allocated according to the defined runtime resource scheduling.

Placement and scheduling of radio signal processing dataflow operations

A task scheduling system that can be used to improve task assignment for multiple satellites, and thereby improve resource allocation in the execution of a task. In some implementations, configuration data for one or more satellites is obtained. Multiple objectives corresponding to a task to be performed using the satellites, and resource parameters associated with executing the task to be performed using the satellites are identified. A score for each objective included in the multiple objectives is computed by the terrestrial scheduler based on the resource parameters and the configuration data for the one or more satellites. The multiple objectives are assigned to one or more of the satellites. Instructions are provided to the one or more satellites that cause the one or more satellites to execute the task according to the assignment of the objectives to the one or more satellites.

Hierarchical satellite task scheduling system

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.

Learning-based space communications systems

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.

Learning radio signals using radio signal transformers

Methods, systems, and apparatus, including computer programs encoded on computer-storage media, for adversarially generated communication. In some implementations, first information is used as input for a generator machine-learning network. Information is taken from both the generator machine-learning network and target information that includes sample signals or other data. The information is sent to a discriminator machine-learning network which produces decision information including whether the information originated from the generator machine-learning network or the target information. An optimizer takes the decision information and performs one or more iterative optimization techniques which help determine updates to the generator machine-learning network or the discriminator machine-learning network. One or more rounds of updating the generator machine-learning network or the discriminator machine-learning network can allow the generator machine-learning network to produce information that is similar to the target information.

Adversarially generated communications

Methods, systems, and apparatus, including computer programs encoded on computer-storage media, for positioning a radio signal receiver at a first location within a three dimensional space; positioning a transmitter at a second location within the three dimensional space; transmitting a transmission signal from the transmitter to the radio signal receiver; processing, using a machine-learning network, one or more parameters of the transmission signal received at the radio signal receiver; in response to the processing, obtaining, from the machine-learning network, a prediction corresponding to a direction of arrival of the transmission signal transmitted by the transmitter; computing an error term by comparing the prediction to a set of ground truths; and updating the machine-learning network based on the error term.

Estimating direction of arrival of electromagnetic energy using machine learning

Methods, systems, and apparatus, including computer programs encoded on computer storage media for characterizing radio propagation channels. One method includes receiving, at a first modem, a first unit of communication over a radio frequency (RF) communication path from a second modem, wherein the first modem and the second modem process information for RF communications. The first modem identifies fields in the first unit of communication, the fields used to analyze the RF communication path. The first modem extracts data from the fields. The first modem accesses a channel model for approximating a channel representative of the RF communication path from the first modem to the second modem, wherein the channel model includes machine learning models. The first modem trains the channel model using the extracted data. The first modem applies the trained channel model to simulate a set of channel effects associated with the communication path.

Communications and measurement systems for characterizing radio propagation channels

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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Arlington, VA, United States of America

Timothy James O'Shea