Methods and systems for performing radio-frequency signal noise reduction in the absence of noise models

Methods and systems for performing radio-frequency signal noise reduction in the absence of noise models

Time-varying input signals are denoised by a neural network. The neural network learns features associated with noise added to reference signals. The neural network recognizes features of noisy time-varying input signals mixed with the noise that at least partially match at least some of the feature...

Ausführliche Beschreibung

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Bibliographische Detailangaben
Hauptverfasser: Straatemeier, Logan M, Migliori, Benjamin J, Walton, Michael W, Gebhardt, Daniel J
Format: Patent
Sprache:eng
Schlagworte:
CALCULATING
COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
COMPUTING
COUNTING
ELECTRIC COMMUNICATION TECHNIQUE
ELECTRICITY
PHYSICS
TRANSMISSION
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Beschreibung
Zusammenfassung:Time-varying input signals are denoised by a neural network. The neural network learns features associated with noise added to reference signals. The neural network recognizes features of noisy time-varying input signals mixed with the noise that at least partially match at least some of the features associated with the noise. The neural network predicts denoised time-varying output signals that correspond to the time-varying input signals based on the recognized features of the noisy time-varying input signals that at least partially match at least some of the features associated with the noise.