Blind Modulation Identification Using Machine Learning And Deep Learning Algorithms

Blind modulation identification is essential in wireless communication to improve spectrum utilization by automatically recognizing the modulation type of received signals. Recent advances in machine learning and deep learning have led to more robust Automated Modulation Classification (AMC) techniques capable of handling channel impairments. Various classifiers such as Decision Tree, Bagging, KNN, and Deep Learning can be used to classify higher-order digital modulation schemes. While Decision Trees offer simplicity and interpretability with an accuracy of around 82%, Deep Learning achieves the highest performance with about 92% accuracy, demonstrating its superior ability to learn complex signal patterns.