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ModulationRecognition

DEEPSIG DATASET: RADIOML 2016.10A

A synthetic dataset, generated with GNU Radio, consisting of 11 modulations (8 digital and 3 analog) at varying signal-to-noise ratios. The file is formatted as a "pickle" file which can be open for example in python by using cPickle.load(...) Dataset Download: http://opendata.deepsig.io/datasets/2016.10/RML2016.10a.tar.bz2

Models

We used five different models and compared the results of each model. -Basic NN Model -Deep CNN Model -ResNet Model -LSTM model -LSTM-RestNet combined Model

Best Results was for LSTM-ResNet Model which was Overall accuracy 62% , 89% for High SNR

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Machine learning model for Modulation recognition

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