… 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 …

… , 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 …

… to require more effective and efficient wireless communications than ever before. For this …
IoT wireless communication paradigm. In this vision, IoT devices must be able to not only learn …

… An intelligent edge concept for wireless communication is elaborated in [16]. The learning-…
Few-shot learning and meta-learning are newly christened paradigms in ML that enable …

… The design paradigms of conventional wireless communication systems have to consider
the influence of various uncertain factors in hardware implementation, and compensate for …

… paradigms. … learning algorithms can improve the performance of wireless networks. In
Section VI, we introduce some typical deep learning algorithms and their applications in wireless …

… learning frameworks [23], [24], which have been extensively studied in both ML and wireless
communication … observations are analyzed within the paradigm of multiagent RL (MARL). …

… learning (DML) techniques, such as federated learning, partitioned learning, and distributed
reinforcement learning, have been increasingly applied to wireless communications. This is …