Modulation Identification with Deep Learning Using Matlab

This paper contains the study of modulation identification for both analog and digital signals using convolution neural network. Modulation classification involves identifying the modulation scheme applied to a received signal. Different modulation schemes, such as amplitude modulation (AM), phase-shift keying (PSK), and quadrature amplitude modulation (QAM), encode information by varying the parameters of the carrier signal. The primary objective of using deep learning for modulation identification in communication systems is to enhance the accuracy and efficiency of identifying the modulation scheme used in received signals. Modulation identification is a crucial task in wireless communication, where different modulation schemes are employed to encode information onto carrier signals. The traditional methods of modulation classification relied on handcrafted features and expert domain knowledge, making them susceptible to noise, interference, and variations in signal characteristics. On the other hand, deep learning offers a data-driven approach that learns relevant features directly from the raw signal data, enabling more accurate and adaptive modulation recognition.