Modulation Classification

Automatic modulation classification (AMC) aims to identify the modulation scheme used in a received signal without prior knowledge, a crucial task in various communication systems and electronic warfare. Current research focuses on improving AMC accuracy and robustness using deep learning models like convolutional neural networks (CNNs), recurrent neural networks (RNNs, including BiLSTMs), and transformer-based architectures, often incorporating techniques like attention mechanisms and mixture-of-experts models. These advancements address challenges posed by noisy channels, adversarial attacks, and limited training data, leading to more efficient and reliable signal identification in real-world applications such as cognitive radio and spectrum monitoring. The development of robust and efficient AMC methods is vital for enhancing the security and performance of modern communication networks.