Yong Ren

TL;DR: The goal is to assist the readers in refining the motivation, problem formulation, and methodology of powerful machine learning algorithms in the context of future networks in order to tap into hitherto unexplored applications and services.

TL;DR: This paper identifies four different types of users involved in data mining applications, namely, data provider, data collector, data miner, and decision maker, and examines various approaches that can help to protect sensitive information.

TL;DR: In this article, the authors review the thirty-year history of ML by elaborating on supervised learning, unsupervised learning, reinforcement learning and deep learning and investigate their employment in the compelling applications of wireless networks, including heterogeneous networks, cognitive radios (CR), Internet of Things (IoT), machine to machine networks (M2M), and so on.

TL;DR: The FANET structure and its protocol architecture is surveyed, and a variety of distributed gateway-selection algorithms and cloud-based stability-control mechanisms are addressed, complemented by a range of open challenges.

TL;DR: A two-stage joint hovering altitude and power control solution for the resource allocation problem in UAV networks considering the inevitable cross-tier interference from space-air-ground heterogeneous networks is proposed.