Modulation Recognition

Automatic modulation recognition (AMR) aims to identify the type of modulation used in a received signal, a crucial task in various communication systems. Current research heavily utilizes deep learning, employing convolutional neural networks (CNNs), recurrent neural networks (RNNs like LSTMs), and transformer architectures, often enhanced with attention mechanisms and data augmentation techniques to improve accuracy and robustness in challenging environments. These advancements are driven by the need for efficient and accurate signal classification in applications ranging from spectrum monitoring and cognitive radio to electronic warfare and the Internet of Things (IoT), where resource-constrained devices require lightweight models. The field is also actively exploring methods to improve model efficiency and generalization, including model compression and incremental learning for dynamic environments.