Automatic Modulation Classification Using Convolutional Neural Network with Batch Normalization: A Novel Approach
Pritam Sadhukhan, Jaydeb Bhaumik · 2024
In recent times, Deep Neural Network (DNN) has drawn a lot of interest due to its exceptional performance in identifying complex structured data. In this paper, the automatic classification of modulation types of analog and digital modulated signals is examined using the DNN method. Due to its crucial function in dynamic spectrum access, which can support fifth generation (5G) wireless communications, automatic modulation classification (AMC) is an unavoidable component of different intelligent communication systems. The AMC has been investigated for more than 25 years, but it has proven challenging to create a classifier that is effective in a variety of multipath fading scenarios and other limitations. AMC systems have recently embraced DNN or Deep Learning (DL) based approaches, and significant advancements have been noted. This paper suggests AMC approaches based on Convolutional Neural Network (CNN) with Batch Normalization layers, added after each convolutional layer and first dense layer. The RadioML2016.10a dataset has been used in this investigation which comprises of synthetic signals with 11 modulation types: AM-DSB, AM-SSB, WBFM, GFSK, CPFSK, PAM-4, BPSK, QPSK, 8-PSK, 16-QAM, and 64-QAM. Google Colaboratory has been used as the foundation for all the simulations. Batch Normalization layers have significantly improved the accuracy of the model. An accuracy of 68.54% is achieved at high SNR for the CNN based AMC model with Batch Normalization which shows 9.275% better performance than the model without Batch Normalization.