Strong convolution neural network is used in a machine learning model to classify modulation strategies.

Abstract

In the field of wireless communication, the accurate classification of modulation techniques plays a crucial role in various applications such as spectrum management, interference detection, and adaptive signal processing. This paper proposes a novel approach for classifying modulation techniques using a Robust Convolutional Neural Network (RCNN). The RCNN leverages the power of deep learning to automatically learn discriminative features from raw signal data, enabling it to effectively differentiate between different modulation schemes.