… We cover this drawback of online learning using self-training approach. Furthermore,
blind recognition of the modulation method of intercepted signals in intelligent receiver is an …

… The likelihood-based (LB) method [3] and feature-based (FB) method [4] are two conventional
methods for automatic modulation classification. LB method mainly includes the average …

… can be classified into two categories. The first one is the decision-theoretic approach and
the … Within decision-theoretic approaches [2]–[5], the modulation classification problem was …

… FB method to classify different types of modulation signals. We use the data generated by
Matlab simulation as the input for training … Wang, ‘‘Innovative robust modulation classification …

… We begin by providing a background on the automatic modulation classification problem
in … design based machine learning methods [17]–[22]. These approaches require precise …

… ABSTRACT Automatic modulation classification (AMC) can play an important role in the
timely … based adaptive hierarchical modulation classification method to recognize M-ary QAM …

… Thus, deep learning-based methods in AMC have become more prevalent due to their
promising performance and their ability to generalize to large, complex datasets comprising a …

… are trained based on supervised learning, which needs considerable time to label training
… investigate a semi-supervised learningbased modulation classification method in the field of …

… approaches have been proposed in the literature for automatic modulation classification, …
Feature-based (FB) approaches. In the following, we have provided an overview for the FB …

… (MSN) method is proposed for robust automatic modulation classification (AMC). The
classifier directly utilizes in-phase and quadrature (I/Q) samples to identify the modulation type of …