… Machine learning (ML) and wireless communications are two … The main premise of ML is to
enable computers to learn and … toward less conventional communication paradigms, we are …

… This paper contributes to developing a distributed version of SCNs in cooperative learning
paradigm. The proposed algorithm aims to deal with large-scale datasets which are stored …

… ) is expected to deeply integrate with wireless communications for resource management
in variable environments. In particular, deep reinforcement learning (DRL) is an important tool …

… paradigms (centralized vs. distributed) for AI-based networking. To achieve the best results,
we propose a hybrid ML paradigm that … In the proposed paradigm, we deploy centralized AI …

… scalable user scheduling paradigm based on unsupervised learning to enhance spatial …
paradigm is generalizable to a variety of different simple and scalable unsupervised learning …

… to optimize the wireless communication parameters settings to achieve … learning, we
overview various types of learning paradigms and introduce a couple of popular machine learning …

… wireless communication performance. In a reinforcement learning scenario like our
proposed Q-learning method, even with unknown CSI, the algorithm can still learn the optimal …

… , it is inevitable that adversaries will explore launching new attacks particularly on DL models,
thus leading to a new paradigm called adversarial machine learning (AML). AML studies …

… INTRODUCTION A cognitive radio (CR), as defined in [1], is an intelligent wireless
communication system capable of using methodologies of understanding and learning to adapt its …

… The applications of game theory to wireless communications mostly refer … paradigm of
docition to the distributed Q-learning in order to improve the precision and speed up the learning …