Automatic Modulation Classification (AMC) is a technique used in wireless communication systems to identify the type of modulation being used in a received signal. This is important because different modulation schemes encode information in different ways, and a receiver needs to know the modulation type to properly demodulate the signal and extract the data.

Here's a breakdown of AMC:

• Applications:Cognitive Radio Networks: AMC helps identify unused spectrum bands for efficient communication. Military and Electronic Warfare: Recognizing communication types used by adversaries. Spectrum Monitoring and Regulation: Ensuring proper usage of allocated frequencies.
• Types of AMC Algorithms:Likelihood-based (LB): These algorithms compare the received signal with pre-defined models of different modulation schemes. Feature-based (FB): These algorithms extract features from the signal (like amplitude variations) and use them to classify the modulation type.
• Recent Advancements:Deep Learning: Deep learning architectures, especially Convolutional Neural Networks (CNNs), are showing promising results in AMC due to their ability to automatically learn features from the received signal.

Here are some resources for further reading:

• A research paper on Deep Learning for AMC: https://www.mdpi.com/2079-9292/11/19/3018
• Another paper on a robust AMC method using CNNs: https://ieeexplore.ieee.org/document/9691687