Analysis Of Various Neural Network Architectures for Automatic Modulation Techniques

Wireless Communication plays a paramount role in modern communication systems. Modulation classification as a phase in the middle of demodulation and signal detection, is gaining interest. The main aim of modulation classification is to differentiate the type of modulation from the signal that is received. Here, transmitters choose freely between the different modulation types of signals. It has applications, like CR, electronics reconnaissance. The modulation recognition system consists of 3 steps, which are, signal pre-processing, feature extraction and selection of modulation algorithm. This paper reviews the different existing automatic modulation classification techniques with primary focus on the deep learning-based schemes. In this paper, different modulation techniques are taken like CNN 2-layer and 4-layer, LSTM and CLDNN. The best epoch level for training is found and the different modulation techniques are compared to see which gives the best accuracy.