Wireless signal recognition plays an important role in cognitive radio, which promises a
broad prospect in spectrum monitoring and management with the coming applications for …

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

Modulation classification is one of the key tasks for communications systems monitoring,
management, and control for addressing technical issues, including spectrum awareness …

Deep learning (DL) has recently aroused substantial concern due to its successful
implementations in many fields. Currently, there are few studies on the applications of DL in …

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

Automatic modulation classification (AMC) is a critical algorithm for the identification of
modulation types so as to enable more accurate demodulation in the non-cooperative …

Automatic modulation classification (AMC) can generally be divided into knowledge-based
methods and data-driven methods. In this paper, we explore combining the knowledge …

Automatic Modulation Classification (AMC) is an intermediate step between signal detection
and demodulation. It is a very important process for a receiver that has no, or limited …

In this paper, likelihood-based algorithms are explored for linear digital modulation
classification. Hybrid likelihood ratio test (HLRT)-and quasi HLRT (QHLRT)-based …

In this work, the Transformer Network (TRN) is applied to the automatic modulation
classification (AMC) problem for the first time. Different from the other deep networks, the …