The need for a cognitive radio in aeronautical mobile telemetry is motivated by the simultaneous use of three different versions of continuous phase modulation and bitrates that vary from test article to test article. The cognitive radio envisioned in this article comprises two parts: a machine learning algorithm that determines modulation type and bitrate, and a software-defined radio that performs demodulation and detection. The experiments presented in this article show that a quadratic discriminant classifier operating on power spectral density (PSD) estimates can successfully identify the modulation. Following the classifier, a Gaussian process regression model operating on the same PSD estimate can estimate the bitrate.
Cognitive Radio for Aeronautical Mobile Telemetry: A Machine Learning-Based Approach
IEEE Aerospace and Electronic Systems Magazine ; 37 , 6 ; 32-37
01.06.2022
1372513 byte
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
Aeronautical Telemetry Systems
Wiley | 2008
|FEC Systems for Aeronautical Telemetry
IEEE | 2013
|