… In this article, we will introduce the basic concept of machine learning algorithms and the …
supervised, unsupervised, and reinforcement learning. Machine learning can be widely used in …

… In Table 4, we summarize some typical applications of deep learning aided network
association algorithms. DNN algorithms are readily used for traffic control, wireless localization, …

… timeline of survey papers on the application of different ML paradigms in wireless networks.
… some typical deep learning algorithms and their applications in wireless networks. Some …

… Faced with the explosion of data demands, the caching paradigm is introduced for the future
wireless network to shorten latency and alleviate the transmission burden on backhaul [73]. …

… gration of machine learning (ML) notions across the wireless core … wireless network has
already been motivated by a number of recent wireless networking paradigms, such as mobile …

… have been conceived on machine learning paradigms. Some of them focused their scope
on a specific wireless scenario, such as WSNs [24], [25], cognitive radio networks (CRN) [26]– […

… Next-generation wireless networks are migrating from traditional … driven paradigms based
on big data and machine learning. On one hand, the ever-expanding and context-rich wireless …

… Moreover, [6] and [7] comprehensively survey the applications of DL in designing IoT and
5G cellular networks at various layers of the protocol stack, respectively. In contrast to the …

… In this chapter, the potentials of deep reinforcement learning paradigm are studied for … (ie,
COSB) in dense wireless network. An intelligent Q-learning-based resource allocation (iQRA) …

… of knowledgedriven DL for wireless network optimization, this article first proposes a holistic
framework of knowledge-driven DL in wireless networks and systematically summarizes a …