Recently, deep learning (DL) is an innovative machine learning (ML) technique that has
gained the outstanding achievements in computer vision and natural language processing …

Blind modulation classification is of vital importance in spectrum surveillance applications
and future heterogeneous wireless networks. In standardized wireless systems, modulation …

Automatic modulation classification (AMC) is becoming increasingly important in spectrum
monitoring and cognitive radio. However, most existing modulation classification algorithms …

This paper develops novel deep learning-based architectures and design methodologies for
an orthogonal frequency division multiplexing (OFDM) receiver under the constraint of one …

Deep neural network (DNN) has recently received much attention due to its superior
performance in classifying data with complex structure. In this paper, we investigate …

Orthogonal frequency-division multiplexing (OFDM) is commonly deployed in Internet of
Things (IoT) systems to achieve high data rates with reasonable complexity, where …

Recently, classifying the modulation schemes of signals using deep neural network has
received much attention. In this paper, we introduce a general model of deep neural network …

Automatic modulation classification plays an important role in many fields to identify the
modulation type of wireless signals in order to recover signals by demodulation. In this …

Very recently, convolution neural network (CNN) based deep-learning (DL) models have
been used in automatic modulation classification (AMC) systems and achieved superior …

Automatic modulation classification (AMC) using convolutional neural networks (CNNs) is
an active area of research that has the potential to improve the efficiency and reliability of …