Blindly decoding a signal requires estimating its unknown transmit parameters,
compensating for the wireless channel impairments, and identifying the modulation type …

Automatic modulation classification is a task that is essentially required in many intelligent
communication systems such as fibre‐optic, next‐generation 5G or 6G systems, cognitive …

Blind modulation classification (MC) is an integral part of designing an adaptive or intelligent
transceiver for future wireless communications. Blind MC has several applications in the …

In this paper, we propose high performance blind modulation classification (BMC) technique
based on deep neural network (DNN) for fading channels. First, we provide the large and …

Modulation identification of the transmitted signals remain a challenging area in modern
intelligent communication systems like cognitive radios. The computation of the distinct …

We demonstrate a novel method for the automatic modulation classification based on a
deep learning autoencoder network, trained by a nonnegativity constraint algorithm. The …

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

We consider the problem of recovering channel code parameters over a candidate set by
merely analyzing the received encoded signals. We propose a deep learning-based …

Deep learning (DL) is a new machine learning (ML) methodology that has found successful
implementations in many application domains. However, its usage in communications …

Automatic modulation classification (AMC) aims to identify the modulation format of the
received signals corrupted by the noise, which plays a major role in radio monitoring. In this …