… In Section VI, we introduce some typical deep learning algorithms and their applications in
wireless networks. Some future research ideas and our conclusions are provided in Section …

… • We adopt a decaying learning rate deep Q-network (D-DQN) based algorithm to tackle the
… shift design problem. In contrast to the conventional DQN algorithm, decaying learning rate …

… Zero-touch optimization of wireless networks using ML is another interesting aspect that is
discussed in this paper. Finally, at the end of each section, important research questions that …

… In this section, we focus on the relevant studies in wireless network scenarios. For … wireless
network scenarios. Then we discuss the papers focusing on a specific wireless network …

… Abstract— The next-generation of wireless networks will enable many machine learning (ML)
tools and applications to efficiently analyze various types of data collected by edge devices …

… Besides enhancing the traditional communication blocks, deep learning provides a new …
deep learning model can be learned directly from the data, without handcraft or ad-hoc designs, …

… Recently, the 5G wireless network was developed to support enhanced mobile broadband
(eMBB), massive machine-type communications (mMTC), and ultrareliable and low-latency …

… machines to learn from large … , deep learning studies artificial neural networks (ANNs) that
contain more than one hidden layer to “simulate” the human brain. Currently, deep learning is …

… recent studies on federated learning for wireless networks (eg, [5]). Additionally, they have
motivated studies on communication-efficient federated learning through the techniques of …

… FDL provides not only privacypreserving of UAVs’ data but also reduces both network …
wireless networks to deal with their challenges. Moreover, we address the suitable deep learning …