Indonesian Journal of Electrical Engineering and Computer Science Vol. 27, No. 2, August 2022, pp. 811~819 ISSN: 2502-4752, DOI: 10.11591/ijeecs.v27.i2.pp811-819  811 Journal homepage: http://ijeecs.iaescore.com Machine learning model to classify modulation techniques using robust convolution neural network Nadakuditi Durga Indira, Matcha Venu Gopala Rao Department of Electronics and Communication Engineering, Koneru Lakshmaiah Education Foundtion (KLEF), Vaddeswaram, India Article Info ABSTRACT Article history: Received Jun 24, 2021 Revised May 30, 2022 Accepted Jun 9, 2022 In wireless communications receiver plays a main role to recognize modulation techniques which were used at the transmitter. While transferring information from transmitter to receiver, the receiver must retrieve original information. In order to achieve this goal we introduced a neural network architecture that recognizes the types of modulation techniques. The applications of deep learning can be categorized into classification and detection. The CNN architecture is used to perform feature extraction based on the layers to build a model which classifies the input data. A model that classifies the radio communication signals using deep learning method. The robust c (RCNN) is used to train the modulated signals; the transformations are used to help the neural network which estimate the signal to noise ratio of each signal ranges from -20dB to 18dB with loss and accuracy of 89.57% at SNR 0dB. Keywords: Deep learning Machine learning Neural network architecture Batch normalization Robust CNN This is an open access article under the CC BY-SA license. Corresponding Author: Nadakuditi Durga Indira Department of Electronics and Communication Engineering, Koneru Lakshmaiah Education Foundtion (KLEF) Green fields, Vaddeswaram, Guntur, Andhra Pradesh, 522502, India Email: durga.indira@gmail.com 1. INTRODUCTION Modulation identification is an important basic function of receiver. It has various applications in cognitive radar, software define radio (SDR), and spectrum management, to identify communications and radar waveforms. It’s necessary to classify or identify them by the type of modulation [1]–[4]. In this paper model describes the generation of dataset in GNU Radio using GNU Radio channel model blocks and then slice each time series signal up into a test and training set using based on signal to noise radio (SNR) values ranges from -20dB to 18dB. The dataset consisting of 11 modulation types: 8 digital and 3 analog modulations [5]–[7]. All of these are widely used in wireless communication systems. These consists of binary phase shift keying (BPSK), quadrature amplitude modulation (QPSK), phase shift keying (8PSK), quadrature amplitude modulation (16QAM), binary frequency shift keying (BFSK), CPFSK, and PAM4 for digital modulations, and WBFM, AM-SSB, and AM-DSB for analog modulations [8]–[10]. Figure 1. Shows the Constellation diagram of modulation techniques. Traditional modulation identification methods require basic knowledge of signals and channel parameters [11]. These can be inaccurate and might need frequent changes. Since the environment changes, this leads to a new modulation identification methods using deep neural networks (DNN) [12], [13]. The total dataset is stored as a python pickle file. This data set is available as a pickled Python format at http://radioml.com [14] which consists of time windowed examples, corresponding modulation class and SNR labels.