… sections we consider supervised learning, unsupervised learning, and … aided wireless
systems equipped with machine learning. We introduced the major families of machine learning …

… data-driven wireless communications to inspire future research. … learning frameworks and
specify the learning protocol for distributed devices. Scalable learning speeds up the learning …

… of using DL for wireless communications are investigated. … -based block design rule of wireless
communications in the past … on how to apply DL for wireless communications by inducing …

… Wireless communication and computation technologies are becoming increasingly complex
and dynamic due to the sophisticated and ubiquitous Internet of things (IoT) applications. …

… ment to conventional communications and emergency communications, coastal mobile
communications mixing with cellular or other wireless communication technologies have unique …

… network paradigms are then … learning and other meritorious variants are provided. Besides,
we discuss the potential applications of distributed learning in wireless communications. In …

… Recently, the field of deep learning (DL) has been flourishing in order to enable MI capabilities
in wireless communications technologies. It is believed by researchers that WLANs can …

… , and on-device learning paradigms has led to a layered … between communication and learning
in edge learning systems… for wireless communication in edge learning, collectively called …

… learning based on artificial neural networks will be an indispensable tool for the design and
operation of future wireless communication … general machine learning paradigm, followed by …

… (AI) and communication [1]. As a prominent branch of AI, deep learning (DL), also known as
neural networks, has garnered considerable attention in the field of wireless communication …