A novel and efficient end-to-end learning model for automatic modulation classification is
proposed for wireless spectrum monitoring applications, which automatically learns from the …

Existing specific emitter identification (SEI) methods based on hand-crafted features have
drawbacks of losing feature information and involving multiple processing stages, which …

Traditional likelihood-based and handcrafted feature-based methods for overlapped signals
automatic modulation classification (OS-AMC) suffer from the uncertainty of the overlapped …

Automatic modulation classification is a desired feature in many modern software-defined
radios; however, classification performance degrades with decreasing signal to noise ratios …

Automatic Modulation Classification (AMC) is significant for the practical support of a
plethora of emerging spectrum applications, such as Dynamic Spectrum Access (DSA) in 5G …

In this paper, we propose an effective feature-based automatic modulation classification
(AMC) method using a deep neural network (DNN). In order to classify the modulation type …

Automatic modulation classification (AMC), which plays critical roles in both civilian and
military applications, is investigated in this paper through a deep learning approach …

Deep learning has recently attracted much attention due to its excellent performance in
processing audio, image, and video data. However, few studies are devoted to the field of …

With the rapid emergence of advanced technologies for wireless communications, automatic
modulation classification (AMC) has been deployed in the physical layer to blindly identify …

Due to the implementation and performance limitations of centralized learning automatic
modulation classification (CentAMC) method, this paper proposes a decentralized learning …