… usage of deep learning (DL) in ultra-wideband (UWB) Wireless Body Area Networks (WBANs) …
The effectiveness of the proposed deep feedforward neural network was checked on the …

… in areas such as image, voice, and text showing strong performance, both deep learning
and the physical layer of wireless communication technology are studied in combination with …

… LTE-advanced wireless networks. Section 3 delineates the deep learning–based prediction
… of LSTM trained model with Deep-DRX execution on real wireless traffic traces. Finally, we …

… Our proposed approach adopts the deep learning network to approximate both the actor
function and critic function, and the optimal policy will be learned after a finite number of …

… in wireless communication. Inspired by digital signal processing theories, we propose frequency
learning attention networks … -spectral attention mechanism for learning-based frequency …

… To address the novel types of attacks, deep learning has been surveyed in this article
with novel challenges. Finally, we have also presented several future research directions. …

… By using a deep learning module, we are able to train our algorithm on real data captured
from different unknown sources with different network technologies. By creating this dataset, …

… In this paper, we present a wireless device … several deep learning algorithms is used to
train and learn to distinguish among ZigBee devices. Proposed model is supervised learning …

This paper investigates a deep reinforcement learning (DRL)-based MAC protocol for
heterogeneous wireless networking, referred to as a Deep-reinforcement Learning Multiple …

… to wireless networks, providing experimental basis for the development of the wireless …
Deep learning models for wireless signal classification with distributed low-cost spectrum …