… of deep learning in AMC. In this paper, a novel modulation classification framework is
proposed. This framework further considers the intrinsic properties of the modulated signals. It …

… Automatic modulation classification (AMC) of radar signals has long been a … -net. A residual
learning convolutional denoising autoencoder (RLCDAE) and a supplementary classification …

… Abstract—Automatic Modulation Classification (AMC) has become an important component
in communication systems for both civil and defense applications. The shortcomings of …

… DL based methods, particularly in the application of internet-of-things (IoT) networks and …
Automatic modulation classification (AMC) is a novel and promising technology in the non-…

… used by AMC, which are the in-phase/quadrature (I/Q) representation and amplitude/phase
(A/P) representation, respectively. However, most of AMC … In this article, lots of popular AMC …

… of automatic modulation classification (AMC). In this paper, a novel compressive convolutional
neural network (CCNN) is proposed for AMC… loss function of CNN network. CCNN aims at …

… Internet connectivity to things in our physical world [1]− [4]. Billions of interconnected …
Automatic modulation classification (AMC), which is the process of identifying the modulation format …

… , automatic modulation classification (AMC) is an essential technology for analyzing signals
and classifying different kinds of signal modulation … networks to construct a DBN. A robust …

… of modulation classification algorithms. Then we present the main topic of the paper, namely
AMC algorithms … In this approach a network of radios collaboratively detect the presence, as …

… Automatic modulation classification (AMC), which refers to blind recognition of a modulation
… Moreover, generative adversarial net (GAN) was also considered for AMC to generate …