Abrégé

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.

Classes IPC  ?

• H04W 24/02 - Dispositions pour optimiser l'état de fonctionnement
• H04W 24/08 - Réalisation de tests en trafic réel

Abrégé

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.

Classes IPC  ?

• H04B 17/391 - Modélisation du canal de propagation
• G06N 3/047 - Réseaux probabilistes ou stochastiques
• G06N 3/08 - Méthodes d'apprentissage
• G06N 20/00 - Apprentissage automatique
• H04L 27/00 - Systèmes à porteuse modulée

Abrégé

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.

Classes IPC  ?

• G06N 3/088 - Apprentissage non supervisé, p. ex. apprentissage compétitif
• G06N 3/045 - Combinaisons de réseaux

Abrégé

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.

Classes IPC  ?

• G06N 20/00 - Apprentissage automatique
• G01S 5/02 - Localisation par coordination de plusieurs déterminations de direction ou de ligne de positionLocalisation par coordination de plusieurs déterminations de distance utilisant les ondes radioélectriques
• G01S 5/04 - Position de source déterminée par plusieurs radiogoniomètres espacés
• G01S 7/40 - Moyens de contrôle ou d'étalonnage
• G01S 13/42 - Mesure simultanée de la distance et d'autres coordonnées
• G06N 3/08 - Méthodes d'apprentissage
• G06N 3/084 - Rétropropagation, p. ex. suivant l’algorithme du gradient
• G06N 20/20 - Techniques d’ensemble en apprentissage automatique
• H04B 17/318 - Force du signal reçu
• H04B 17/336 - Rapport signal/interférence ou rapport porteuse/interférence
• H04B 17/391 - Modélisation du canal de propagation

Abrégé

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.

Classes IPC  ?

• H04W 56/00 - Dispositions de synchronisation
• G06N 3/044 - Réseaux récurrents, p. ex. réseaux de Hopfield
• G06N 3/08 - Méthodes d'apprentissage
• G06N 3/084 - Rétropropagation, p. ex. suivant l’algorithme du gradient
• G06N 20/00 - Apprentissage automatique
• H04B 17/391 - Modélisation du canal de propagation
• H04L 5/00 - Dispositions destinées à permettre l'usage multiple de la voie de transmission
• H04L 41/14 - Analyse ou conception de réseau
• H04W 16/22 - Outils ou modèles de simulation de trafic
• H04W 72/0453 - Ressources du domaine fréquentiel, p. ex. porteuses dans des AMDF [FDMA]

Abrégé

A radio-frequency (RF) receiver includes: at least n antennas, where n is an integer greater than two; m processing channels configured to receive and process n RF signals from the at least n antennas, where m is an integer greater than one and less than n; a controller configured to cause a first processing channel of the m processing channels to receive, at different corresponding times, a plurality of RF signals of the n RF signals; an indexing module configured to receive outputs from the m processing channels, and generate one or more representations of the n RF signals based on the outputs; and a spatial estimation module configured to receive the one or more representations, execute a machine learning model based on the one or more representations, and determine, based on an output of the machine learning model, a spatial estimate for an emitter of the n RF signals.

Classes IPC  ?

• H04L 25/03 - Réseaux de mise en forme pour émetteur ou récepteur, p. ex. réseaux de mise en forme adaptatifs
• H04L 25/02 - Systèmes à bande de base Détails

Abrégé

nnmnnmnmnmnnn RF signals.

Classes IPC  ?

• H04B 1/18 - Circuits d'entrée, p. ex. pour le couplage à une antenne ou à une ligne de transmission
• H04B 1/10 - Dispositifs associés au récepteur pour limiter ou supprimer le bruit et les interférences
• H04B 1/28 - Circuits pour récepteurs superhétérodynes le récepteur comportant au moins un dispositif à semi-conducteurs ayant trois électrodes ou plus

Abrégé

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.

Classes IPC  ?

• H04W 16/18 - Outils de planification de réseau
• G06F 9/50 - Allocation de ressources, p. ex. de l'unité centrale de traitement [UCT]
• G06F 17/11 - Opérations mathématiques complexes pour la résolution d'équations
• H04W 16/22 - Outils ou modèles de simulation de trafic

Abrégé

A method includes executing a radio frequency radiance field (RF-RF) model characterizing an environment; determining, based on outputs of the RF-RF model, one or more characteristics of a wireless channel between a first position and a second position in the environment; and controlling, based on the one or more characteristics of the wireless channel, an RF communication between the first position and the second position. RF-RF models can be used for RF control, communication systems testing and evaluation, system deployment, emitter localization, and other purposes.

Classes IPC  ?

• H04W 24/06 - Réalisation de tests en trafic simulé
• H04W 28/16 - Gestion centrale des ressourcesNégociation de ressources ou de paramètres de communication, p. ex. négociation de la bande passante ou de la qualité de service [QoS Quality of Service]

Abrégé

A method includes executing a radio frequency radiance field (RF-RF) model characterizing an environment; determining, based on outputs of the RF-RF model, one or more characteristics of a wireless channel between a first position and a second position in the environment; and controlling, based on the one or more characteristics of the wireless channel, an RF communication between the first position and the second position. RF-RF models can be used for RF control, communication systems testing and evaluation, system deployment, emitter localization, and other purposes.

Classes IPC  ?

• H04W 24/00 - Dispositions de supervision, de contrôle ou de test
• H04W 4/00 - Services spécialement adaptés aux réseaux de télécommunications sans filLeurs installations
• H04L 43/08 - Surveillance ou test en fonction de métriques spécifiques, p. ex. la qualité du service [QoS], la consommation d’énergie ou les paramètres environnementaux
• H04W 64/00 - Localisation d'utilisateurs ou de terminaux pour la gestion du réseau, p. ex. gestion de la mobilité
• G06N 20/00 - Apprentissage automatique
• H04B 7/06 - Systèmes de diversitéSystèmes à plusieurs antennes, c.-à-d. émission ou réception utilisant plusieurs antennes utilisant plusieurs antennes indépendantes espacées à la station d'émission

Abrégé

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.

Classes IPC  ?

• H04B 17/11 - SurveillanceTests d’émetteurs pour l’étalonnage
• G06N 20/00 - Apprentissage automatique
• H04B 17/391 - Modélisation du canal de propagation
• H04W 24/08 - Réalisation de tests en trafic réel

Abrégé

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.

Classes IPC  ?

• H04B 17/336 - Rapport signal/interférence ou rapport porteuse/interférence
• G06N 20/00 - Apprentissage automatique
• H04B 7/185 - Stations spatiales ou aériennes
• H04B 17/318 - Force du signal reçu
• H04B 17/345 - Valeurs d’interférence

Abrégé

A method for processing radio frequency (RF) signals is provided. The method includes receiving one or more RF signals from one or more antenna channels. The method includes obtaining, from the one or more RF signals, a plurality of unlabeled data samples. The method includes generating an input tensor representation of the plurality of data samples. The method includes pretraining a first machine learning network using the input tensor representation to obtain one or more embeddings. The method includes training a second machine learning network using the one or more embeddings. The second machine learning network is configured to perform one or more signal processing tasks. Also provided is a system having an antenna array and one or more processors.

Classes IPC  ?

• H04B 1/16 - Circuits
• G06N 3/0455 - Réseaux auto-encodeursRéseaux encodeurs-décodeurs
• G06N 3/0895 - Apprentissage faiblement supervisé, p. ex. apprentissage semi-supervisé ou auto-supervisé

Abrégé

A method includes obtaining samples of radio-frequency (RF) uplink data signals received wirelessly at a radio unit of a radio access network, the RF uplink data signals including a first RF uplink data signal received from a user device; providing the samples of the RF uplink data signals as input to at least one machine learning model; in response to providing the samples of the RF uplink data signals as input to the at least one machine learning model, obtaining based on an output of the at least one machine learning model, recovered data of the RF uplink data signals; and sending the recovered data of the RF uplink signals to a destination device.

Classes IPC  ?

• H04B 7/06 - Systèmes de diversitéSystèmes à plusieurs antennes, c.-à-d. émission ou réception utilisant plusieurs antennes utilisant plusieurs antennes indépendantes espacées à la station d'émission
• G06N 20/20 - Techniques d’ensemble en apprentissage automatique
• H04B 17/309 - Mesure ou estimation des paramètres de qualité d’un canal
• H04B 7/0413 - Systèmes MIMO
• H04B 17/373 - Prédiction des paramètres de qualité d’un canal
• H04L 25/02 - Systèmes à bande de base Détails

Abrégé

A method includes obtaining samples of radio-frequency (RF) uplink data signals received wirelessly at a radio unit of a radio access network, the RF uplink data signals including a first RF uplink data signal received from a user device; providing the samples of the RF uplink data signals as input to at least one machine learning model; in response to providing the samples of the RF uplink data signals as input to the at least one machine learning model, obtaining based on an output of the at least one machine learning model, recovered data of the RF uplink data signals; and sending the recovered data of the RF uplink signals to a destination device.

Classes IPC  ?

• G06N 3/045 - Combinaisons de réseaux
• G06N 3/08 - Méthodes d'apprentissage
• H04L 25/02 - Systèmes à bande de base Détails
• H04L 27/26 - Systèmes utilisant des codes à fréquences multiples

Abrégé

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.

Classes IPC  ?

• H04B 1/04 - Circuits
• G06N 3/04 - Architecture, p. ex. topologie d'interconnexion
• G06N 3/08 - Méthodes d'apprentissage
• H04W 88/08 - Dispositifs formant point d'accès

Abrégé

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.

Classes IPC  ?

• H04W 24/02 - Dispositions pour optimiser l'état de fonctionnement
• H04B 17/336 - Rapport signal/interférence ou rapport porteuse/interférence

Abrégé

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.

Classes IPC  ?

• H04W 72/541 - Critères d’affectation ou de planification des ressources sans fil sur la base de critères de qualité en utilisant le niveau d’interférence
• H04B 1/10 - Dispositifs associés au récepteur pour limiter ou supprimer le bruit et les interférences
• G06N 20/00 - Apprentissage automatique
• H04W 24/02 - Dispositions pour optimiser l'état de fonctionnement
• H04W 48/16 - ExplorationTraitement d'informations sur les restrictions d'accès ou les accès
• H04W 72/27 - Canaux de commande ou signalisation pour la gestion des ressources entre points d’accès
• H04W 72/54 - Critères d’affectation ou de planification des ressources sans fil sur la base de critères de qualité

Abrégé

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.

Classes IPC  ?

• G06N 20/00 - Apprentissage automatique
• G01S 5/02 - Localisation par coordination de plusieurs déterminations de direction ou de ligne de positionLocalisation par coordination de plusieurs déterminations de distance utilisant les ondes radioélectriques
• G01S 5/04 - Position de source déterminée par plusieurs radiogoniomètres espacés
• G01S 7/40 - Moyens de contrôle ou d'étalonnage
• G01S 13/42 - Mesure simultanée de la distance et d'autres coordonnées
• G06N 3/08 - Méthodes d'apprentissage
• G06N 3/084 - Rétropropagation, p. ex. suivant l’algorithme du gradient
• G06N 20/20 - Techniques d’ensemble en apprentissage automatique
• H04B 17/318 - Force du signal reçu
• H04B 17/336 - Rapport signal/interférence ou rapport porteuse/interférence
• H04B 17/391 - Modélisation du canal de propagation

Abrégé

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.

Classes IPC  ?

• H04B 17/11 - SurveillanceTests d’émetteurs pour l’étalonnage
• H04W 24/08 - Réalisation de tests en trafic réel
• H04B 17/391 - Modélisation du canal de propagation
• G06N 20/00 - Apprentissage automatique

Abrégé

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.

Classes IPC  ?

• G06N 3/08 - Méthodes d'apprentissage
• H04B 17/391 - Modélisation du canal de propagation

Abrégé

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.

Classes IPC  ?

• G06N 20/00 - Apprentissage automatique
• H04B 17/391 - Modélisation du canal de propagation
• G06N 3/08 - Méthodes d'apprentissage

Abrégé

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.

Classes IPC  ?

• G01S 3/02 - Radiogoniomètres pour déterminer la direction d'où proviennent des ondes infrasonores, sonores, ultrasonores ou électromagnétiques ou des émissions de particules sans caractéristiques de direction utilisant des ondes radio
• G06N 3/02 - Réseaux neuronaux
• G01S 3/06 - Moyens pour accroître la directivité effective, p. ex. en combinant des signaux ayant des caractéristiques de directivité différemment orientées ou en affinant la forme d'onde enveloppe du signal provenant d'une antenne directionnelle rotative ou oscillante
• G01S 5/02 - Localisation par coordination de plusieurs déterminations de direction ou de ligne de positionLocalisation par coordination de plusieurs déterminations de distance utilisant les ondes radioélectriques
• G01S 5/06 - Position de source déterminée par coordination d'un ensemble de lignes de position définies par des mesures de différence de parcours
• G06F 30/20 - Optimisation, vérification ou simulation de l’objet conçu
• G06N 3/08 - Méthodes d'apprentissage
• G06N 3/10 - Interfaces, langages de programmation ou boîtes à outils de développement logiciel, p. ex. pour la simulation de réseaux neuronaux
• G01S 3/14 - Systèmes pour déterminer une direction ou une déviation par rapport à une direction prédéterminée
• G06N 20/00 - Apprentissage automatique

Abrégé

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.

Classes IPC  ?

• H04W 24/02 - Dispositions pour optimiser l'état de fonctionnement
• H04W 24/08 - Réalisation de tests en trafic réel

Abrégé

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.

Classes IPC  ?

• H04B 17/345 - Valeurs d’interférence
• H04B 7/185 - Stations spatiales ou aériennes
• H04B 17/318 - Force du signal reçu
• H04B 17/336 - Rapport signal/interférence ou rapport porteuse/interférence
• G06N 20/00 - Apprentissage automatique

Abrégé

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.

Classes IPC  ?

• H04W 56/00 - Dispositions de synchronisation
• G06N 3/044 - Réseaux récurrents, p. ex. réseaux de Hopfield
• G06N 3/08 - Méthodes d'apprentissage
• G06N 3/084 - Rétropropagation, p. ex. suivant l’algorithme du gradient
• G06N 20/00 - Apprentissage automatique
• H04B 17/391 - Modélisation du canal de propagation
• H04L 5/00 - Dispositions destinées à permettre l'usage multiple de la voie de transmission
• H04L 41/14 - Analyse ou conception de réseau
• H04W 16/22 - Outils ou modèles de simulation de trafic
• H04W 72/0453 - Ressources du domaine fréquentiel, p. ex. porteuses dans des AMDF [FDMA]

Abrégé

Methods, systems, and apparatus, including computer programs encoded on computer-storage media, for detecting and classifying radio signals. The method includes obtaining one or more radio frequency (RF) snapshots corresponding to a first set of signals from a first RF source; generating a first training data set based on the one or more RF snapshots; annotating the first training data set to generate an annotated first training data set; generating a trained detection and classification model based on the annotated first training data set; and providing the trained detection and classification model to a sensor engine to detect and classify one or more new signals using the trained detection and classification model.

Classes IPC  ?

• G01R 29/08 - Mesure des caractéristiques du champ électromagnétique
• G06N 3/08 - Méthodes d'apprentissage
• B64C 39/02 - Aéronefs non prévus ailleurs caractérisés par un emploi spécial

Abrégé

Methods, systems, and apparatus, including computer programs encoded on computer-storage media, for detecting and classifying radio signals. The method includes obtaining one or more radio frequency (RF) snapshots corresponding to a first set of signals from a first RF source; generating a first training data set based on the one or more RF snapshots; annotating the first training data set to generate an annotated first training data set; generating a trained detection and classification model based on the annotated first training data set; and providing the trained detection and classification model to a sensor engine to detect and classify one or more new signals using the trained detection and classification model.

Classes IPC  ?

• G06N 20/00 - Apprentissage automatique
• G10L 25/18 - Techniques d'analyse de la parole ou de la voix qui ne se limitent pas à un seul des groupes caractérisées par le type de paramètres extraits les paramètres extraits étant l’information spectrale de chaque sous-bande
• G10L 25/51 - Techniques d'analyse de la parole ou de la voix qui ne se limitent pas à un seul des groupes spécialement adaptées pour un usage particulier pour comparaison ou différentiation
• H04R 29/00 - Dispositifs de contrôleDispositifs de tests

Abrégé

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.

Classes IPC  ?

• H04B 17/345 - Valeurs d’interférence
• H04B 7/185 - Stations spatiales ou aériennes
• G06N 20/00 - Apprentissage automatique
• H04B 17/336 - Rapport signal/interférence ou rapport porteuse/interférence
• H04B 17/318 - Force du signal reçu

Abrégé

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.

Classes IPC  ?

• H04B 17/11 - SurveillanceTests d’émetteurs pour l’étalonnage
• H04W 24/08 - Réalisation de tests en trafic réel
• H04B 17/391 - Modélisation du canal de propagation
• G06N 20/00 - Apprentissage automatique

Abrégé

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.

Classes IPC  ?

• H04B 1/04 - Circuits
• G06N 3/08 - Méthodes d'apprentissage
• G06N 3/04 - Architecture, p. ex. topologie d'interconnexion
• H04W 88/08 - Dispositifs formant point d'accès

Abrégé

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.

Classes IPC  ?

• G06F 9/455 - ÉmulationInterprétationSimulation de logiciel, p. ex. virtualisation ou émulation des moteurs d’exécution d’applications ou de systèmes d’exploitation
• H04W 16/18 - Outils de planification de réseau
• G06F 9/50 - Allocation de ressources, p. ex. de l'unité centrale de traitement [UCT]
• G06F 17/11 - Opérations mathématiques complexes pour la résolution d'équations
• H04W 16/22 - Outils ou modèles de simulation de trafic

Abrégé

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.

Classes IPC  ?

• H04B 17/391 - Modélisation du canal de propagation
• H04L 27/00 - Systèmes à porteuse modulée
• G06N 3/04 - Architecture, p. ex. topologie d'interconnexion
• G06N 3/08 - Méthodes d'apprentissage
• G06N 20/00 - Apprentissage automatique

Abrégé

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.

Classes IPC  ?

• H04B 17/391 - Modélisation du canal de propagation
• H04L 27/00 - Systèmes à porteuse modulée
• G06N 3/04 - Architecture, p. ex. topologie d'interconnexion
• G06N 3/08 - Méthodes d'apprentissage
• G06N 20/00 - Apprentissage automatique

Abrégé

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.

Classes IPC  ?

• H04L 27/26 - Systèmes utilisant des codes à fréquences multiples
• H04L 27/28 - Systèmes utilisant des codes à fréquences multiples à émission simultanée de fréquences différentes, chacune représentant un élément de code

Abrégé

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.

Classes IPC  ?

• H04B 17/345 - Valeurs d’interférence
• H04B 7/185 - Stations spatiales ou aériennes
• H04B 17/318 - Force du signal reçu
• H04B 17/336 - Rapport signal/interférence ou rapport porteuse/interférence
• G06N 20/00 - Apprentissage automatique

Abrégé

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.

Classes IPC  ?

• G06N 3/088 - Apprentissage non supervisé, p. ex. apprentissage compétitif
• G06N 3/045 - Combinaisons de réseaux

Produits et services

downloadable software for detecting signal information incorporating machine learning; downloadable software development kits; recorded software for detecting signal information incorporating machine learning software as a service featuring software for detecting signal information incorporating machine learning

Produits et services

downloadable software for communications signal processing incorporating machine learning; recorded software for communications signal processing incorporating machine learning; downloadable software development kits software as a service featuring software for communications signal processing incorporating machine learning

Abrégé

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.

Classes IPC  ?

• G06N 7/00 - Agencements informatiques fondés sur des modèles mathématiques spécifiques
• G06F 16/906 - GroupementClassement
• G06N 3/04 - Architecture, p. ex. topologie d'interconnexion
• G06N 20/00 - Apprentissage automatique

Abrégé

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.

Classes IPC  ?

• H04W 24/02 - Dispositions pour optimiser l'état de fonctionnement
• H04W 24/08 - Réalisation de tests en trafic réel
• G06N 20/00 - Apprentissage automatique
• H04W 24/04 - Configurations pour maintenir l'état de fonctionnement

Abrégé

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.

Classes IPC  ?

• H04L 12/24 - Dispositions pour la maintenance ou la gestion
• H04W 16/22 - Outils ou modèles de simulation de trafic
• H04B 17/391 - Modélisation du canal de propagation
• G06N 3/08 - Méthodes d'apprentissage
• H04W 56/00 - Dispositions de synchronisation
• G06N 20/00 - Apprentissage automatique
• H04L 5/00 - Dispositions destinées à permettre l'usage multiple de la voie de transmission
• H04W 72/04 - Affectation de ressources sans fil
• H04L 41/14 - Analyse ou conception de réseau
• G06N 3/04 - Architecture, p. ex. topologie d'interconnexion

Abrégé

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.

Classes IPC  ?

• H04B 1/04 - Circuits
• G06N 3/04 - Architecture, p. ex. topologie d'interconnexion
• G06N 3/08 - Méthodes d'apprentissage
• H04W 88/08 - Dispositifs formant point d'accès

Abrégé

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.

Classes IPC  ?

• G01S 5/00 - Localisation par coordination de plusieurs déterminations de direction ou de ligne de positionLocalisation par coordination de plusieurs déterminations de distance
• G01S 5/02 - Localisation par coordination de plusieurs déterminations de direction ou de ligne de positionLocalisation par coordination de plusieurs déterminations de distance utilisant les ondes radioélectriques
• G01S 11/06 - Systèmes pour déterminer la distance ou la vitesse sans utiliser la réflexion ou la reradiation utilisant les ondes radioélectriques utilisant des mesures d'intensité
• G06N 20/00 - Apprentissage automatique

Abrégé

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.

Classes IPC  ?

• G01S 5/00 - Localisation par coordination de plusieurs déterminations de direction ou de ligne de positionLocalisation par coordination de plusieurs déterminations de distance
• G01S 5/02 - Localisation par coordination de plusieurs déterminations de direction ou de ligne de positionLocalisation par coordination de plusieurs déterminations de distance utilisant les ondes radioélectriques
• G01S 11/06 - Systèmes pour déterminer la distance ou la vitesse sans utiliser la réflexion ou la reradiation utilisant les ondes radioélectriques utilisant des mesures d'intensité
• G06N 20/00 - Apprentissage automatique

Abrégé

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.

Classes IPC  ?

• G06N 3/08 - Méthodes d'apprentissage
• G06N 3/10 - Interfaces, langages de programmation ou boîtes à outils de développement logiciel, p. ex. pour la simulation de réseaux neuronaux
• H04W 72/06 - Affectation de ressources sans fil sur la base de critères de classement des ressources sans fil
• H04W 72/08 - Affectation de ressources sans fil sur la base de critères de qualité
• H04W 72/10 - Affectation de ressources sans fil sur la base de critères de priorité

Abrégé

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.

Classes IPC  ?

• G06N 3/08 - Méthodes d'apprentissage
• H04W 16/22 - Outils ou modèles de simulation de trafic
• H04W 56/00 - Dispositions de synchronisation
• G06N 20/00 - Apprentissage automatique
• H04L 5/00 - Dispositions destinées à permettre l'usage multiple de la voie de transmission
• H04W 72/04 - Affectation de ressources sans fil
• H04B 17/391 - Modélisation du canal de propagation
• H04L 12/24 - Dispositions pour la maintenance ou la gestion

Abrégé

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 are performed 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.

Classes IPC  ?

• G06F 9/46 - Dispositions pour la multiprogrammation
• H04W 16/18 - Outils de planification de réseau
• G06F 9/50 - Allocation de ressources, p. ex. de l'unité centrale de traitement [UCT]
• G06F 17/11 - Opérations mathématiques complexes pour la résolution d'équations
• H04W 16/22 - Outils ou modèles de simulation de trafic

Abrégé

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.

Classes IPC  ?

• G06F 9/46 - Dispositions pour la multiprogrammation
• H04W 16/18 - Outils de planification de réseau
• G06F 9/50 - Allocation de ressources, p. ex. de l'unité centrale de traitement [UCT]
• G06F 17/11 - Opérations mathématiques complexes pour la résolution d'équations
• H04W 16/22 - Outils ou modèles de simulation de trafic

Produits et services

Research and development of computer software