… However, we believe that the application of deep learning to communication network
design and optimization offers more possibilities than such a purely data-driven approach. …

… First, they are often focused on one type of machine learning techniques (often deep
learning such as in [32], [51], and [54]) and, as such, they do not capture the rich spectrum of …

… Wireless sensor network (WSN) comprises a collection of sensor nodes employed to … This
paper presents a deep learning based distributed data mining (DDM) model to achieve energy …

… Advanced technologies are required in future mobile wireless networks to support services
with … Deep Learning (DL), one of the most exciting developments in machine learning and big …

… has investigated the opportunity of using learning [19–21] and deep learning [15, 22–24] …
real-time deep learning in the RF loop for spectrum-driven wireless networking on embedded …

… In doing so, we build on recent trends in deep learning research that combines domain
knowledge and neural networks to solve domain-specific problems. Specifically, we build a …

… In this paper, deep learning using the PyTorch framework is adopted for traffic demand … the
network planning and simulation environment. PyTorch [97] is a deep learning framework for …

… and opportunities of wireless technology from engineering … wireless communication with
the detail of how deep learning … directions for wireless technology powered by deep learning. …

… , machine learning-enabled reconfigurable wireless network solutions are required. In this
paper, we propose a hybrid deep learning model that consists of convolution neural network (…

… anomaly detection in 5G utilizing deep learning methods. … a deep learning model and
architecture we used for network … future research to enhanced deep learning algorithms/models …