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 …

We experimentally demonstrate the use of deep neural networks (DNNs) in combination
with signals' amplitude histograms (AHs) for simultaneous optical signal-to-noise ratio …

The growing network density and unprecedented increase in network traffic, caused by the
massively expanding number of connected devices and online services, require intelligent …

Automated Modulation Classification (AMC) has been applied in various emerging areas
such as cognitive radio (CR). In our paper, we propose a deep learning-based AMC method …

This letter proposes a novel modulation recognition algorithm for very high frequency (VHF)
radio signals, which is based on antinoise processing and deep sparse-filtering …

Automatic modulation classification (AMC) lies at the core of cognitive radio and spectrum
sensing. In this Letter, the authors propose a novel convolutional neural network (CNN) …

Due to the characteristics of the underwater acoustic (UWA) channel, the process at the
receiver is complicated to match the channel. To simplify receiver design and match UWA …

Deep learning (DL) is applied to predict signal-to-noise ratio (SNR) in de facto LTE and 5G
systems in a non-data-aided (NDA) manner. Various channel conditions and impairments …

With the recently explosive growth of deep learning, automatic modulation recognition has
undergone rapid development. Most of the newly proposed methods are dependent on …

Neural network shows great potential in modulation classification because of its excellent
accuracy and achievability but overfitting and memorizing data noise often happen in …