Motorola Solutions, Inc.

Rodger W Caruthers

Bruce D Mueller

Robert T Croswell

Nathanael P Kuehner

Duane S Andres

Stephen J Govea

Randall Brace

Gregory M Agami

David N Taylor

Micah D Silberstein

Joseph P Heck

Raul Salvi

Nicholas Giovanni Cafaro

Geetha B Nagaraj

Christopher Calvo

Nicholas G Cafaro

A device, system and method for radio-frequency emissions control is provided. The device comprises: a communication unit configured to communicate via main radio channels and a control channel, the main radio channels contributing to radio-frequency (RF) emissions; and a controller interconnected with the communication unit. The controller is configured to: receive, via the communication unit communicating over the control channel, an RF emissions control command to reduce the RF emissions emitted by the communication unit; and in response to receiving the RF emissions control command, control one or more of the communication unit and activity on the main radio channels to reduce the RF emissions.

Device, system and method for radio-frequency emissions control

Systems and methods for classifying radio frequency signal modulations include receiving, at a consolidated neural network, a complex quadrature vector of interest representative of a baseband signal derived from a radio frequency signal, generating multiple data representations of the vector of interest, providing each data representation to one of multiple parallel neural networks in the consolidated neural network, and receiving, from the consolidated neural network, a classification result for the baseband signal. The consolidated neural network may be trained to classify baseband signals with respect to known modulation types by receiving complex quadrature training vectors, each including samples of a baseband signal derived from a radio frequency signal of known modulation type, comparing a classification result for the training vector to the known modulation type to determine modulation classification performance, and modifying a configuration parameter of the consolidated neural network dependent on the determined modulation classification performance.

Systems and methods for modulation classification of baseband signals using multiple data representations of signal samples

Systems and methods for classifying baseband signals include receiving, at a pre-processing stage of a neural network whose objective is modulation classification performance, a complex quadrature vector of interest including a plurality of samples of a baseband signal derived from a radio frequency signal of an unknown modulation type, providing the vector of interest to a plurality of FIR filters, each of which outputs a respective intermediate filtered version of the vector of interest, combining the outputs of two or more of the FIR filters to produce a filtered version of the vector of interest, including applying respective weightings to the outputs of the FIR filters, and providing the filtered version of the vector of interest to an analysis stage of the neural network for classification with respect to a plurality of known modulation types. The neural network may apply attention-based selection to learn the filters and respective weightings.

Systems and methods for modulation classification of baseband signals using attention-based learned filters

Systems and methods for classifying baseband signals with respect to modulation type include receiving, at a consolidated neural network whose objective is modulation classification performance, a complex quadrature vector of interest including multiple samples of a baseband signal derived from a radio frequency signal of unknown modulation type, generating multiple data representations of the vector of interest, providing each data representation to one of multiple parallel neural networks in the consolidated neural network, and receiving a classification result for the baseband signal based on combined outputs of the parallel neural networks. The consolidated neural network may be trained to classify baseband signals with respect to known modulation types by receiving complex quadrature training vectors, each including samples of a baseband signal derived from a radio frequency signal of known modulation type, and comparing a classification result for the training vector to the known modulation type to determine modulation classification performance.

A device, system and method for frequency hopping control is provided. The device comprises: a communication unit configured to communicate via main radio channels and a control channel; and a controller interconnected with the communication unit. The controller is configured to: receive, via the communication unit communicating over the control channel, a frequency hopping control command comprising a list of one or more hopping frequencies for frequency hopping; and in response to receiving the frequency hopping control command, control the communication unit to communicate via the main radio channels using frequency hopping according to the one or more hopping frequencies.

Device, system and method for frequency hopping control

Systems and methods for processing radiofrequency signals using modulation duty cycle scaling. One system includes a first receive path configured to directly sample a first signal in a first frequency range. The system includes a second receive path configured to convert a second signal in a second frequency range. The second receive path includes a receive modulator operating over a duty cycle. The receive modulator is configured to adjust the duty cycle by a predetermined scaling factor.

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Des Plaines, IL, United States of America

Rodger W Caruthers