Evaluation Of Cnn And Cldnn Architectures On Radio Modulation Datasets
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This paper presents an evaluation of deep learning architectures designed for modulationrecognition. The evaluation inspects, whether the architectures behave in the same way as they didon the dataset they were designed on. The architectures are trained and tested on two different radiomodulation datasets. This results in proposing additional binary classification as a method to reducemisclassification of QAM modulation types in one of the datasets.
Document typePeer reviewed
Document versionFinal PDF
SourceProceedings I of the 27st Conference STUDENT EEICT 2021: General papers. s. 42-45. ISBN 978-80-214-5942-7
- Student EEICT 2021