B C, B.Deshmukh, A.Rupa, M.V.Sirigina, R.P.Vankayala, S.K.Narasimhadhan, A.V.2026-02-062021IEEE Vehicular Technology Conference, 2021, Vol.2021-September, , p. -07400551; 15502252https://doi.org/10.1109/VTC2021-Fall52928.2021.9625552https://idr.nitk.ac.in/handle/123456789/30301This paper aims to classify signal and modulation classes of a given wireless signal with high accuracy using a model having a low number of parameters. We propose an end-to-end method to classify a wireless signal based on its signal and modulation type using a CNN-based architecture. The proposed architecture is similar to that of the LeNet-5. Firstly, we implement signal and modulation classification using a decision tree, followed by a random forest algorithm, classic examples of machine learning(ML) based algorithms. Since our dataset is a time series, we also implement using RNN-LSTM based model for the classification. The proposed model has fewer parameters than that of the CNN-based, RNN-LSTM based architectures. Moreover, it achieves better accuracy for a wide range of signal-to-noise ratios than a decision tree, random forest, RNN-LSTM based classifiers. © 2021 IEEE.Convolutional Neural Network(CNN)Modulation classificationrecurrent neural network-long short term memory(RNN-LSTM)Signal classification