In this paper, a deep convolutional neural network-based symbol detection and
demodulation is proposed for generalized frequency division multiplexing with index …

Automatic modulation recognition is a promising solution for identifying and classifying
signals received in heterogeneous wireless networks. In dynamic and autonomous …

This letter proposes a cost-efficient convolutional neural network (CNN) for robust automatic
modulation classification (AMC) deployed for cognitive radio services of modern …

Automatic Modulation Classification (AMC) is widely used in many aspects and occupies a
critical position in non-cooperative communication. Recently, deep learning (DL) based …

Carrier frequency offset (CFO) arises from the intrinsic mismatch between the oscillators of a
wireless transmitter and the corresponding receiver, as well as their relative motion (ie …

Signal modulation identification (SMI) plays a very important role in orthogonal frequency-
division multiplexing (OFDM) systems. Currently, SMI methods are often implemented via …

Over the past five years, there has been a focus on creating deep learning architectures for
radio modulation recognition. Many of these architectures are either convolutional neural …

Automatic modulation classification (AMC) is a significant part of cognitive communication
systems. In early researches, likelihood-based (LB) and feature-based (FB) solutions were …

Abstract Modern Machine Learning (ML) techniques offer numerous opportunities to enable
intelligent communication designs while addressing a wide range of problems in …

Automatic modulation classification (AMC) is an essential factor in dynamic spectrum access
to fulfill the spectrum demand of 5G wireless communications for achieving high data rate …