This paper presents an overview of feature-based (FB) methods developed for Automatic
classification of digital modulations. Only the most well-known features and classifiers are …

Signal identification is an umbrella term for signal processing techniques designed for the
identification of the transmission parameters of unknown or partially known communication …

In this work, we investigate the value of employing deep learning for the task of wireless
signal modulation recognition. Recently in [1], a framework has been introduced by …

This manuscript presents a survey on new challenges in wireless communication systems
and discusses recent approaches to address some recently raised problems by the wireless …

Modulation type is one of the most important characteristics used in signal waveform
identification. In this paper, an algorithm for automatic digital modulation recognition is …

Modulation classification, recognized as the intermediate step between signal detection and
demodulation, is widely deployed in several modern wireless communication systems …

Automatic modulation classification (AMC) is a classical topic in the signal classification field
and is often performed when the modulation type is adaptive. For typical modulation types …

Automatic modulation classification (AMC) is becoming a promising technique for future
adaptive wireless transceiver systems. The existing blind modulation classification (BMC) …

Automatic modulation classification (AMC), also termed blind signal modulation recognition,
plays a critical role in various civilian and military applications. Although existing …

This paper presents a likelihood-based method for automatically identifying different
quadrature amplitude modulations (QAM) and phase-shift keying (PSK) modulations. This …