Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

Future wireless networks have a substantial potential in terms of supporting a broad range of complex compelling applications both in military and civilian fields, where the users are able to enjoy high-rate, low-latency, low-cost and reliable information services. Achieving this ambitious goal requires new radio techniques for adaptive learning and intelligent decision making because of the complex heterogeneous nature of the network structures and wireless services. Machine learning (ML) algorithms have great success in supporting big data analytics, efficient parameter estimation and interactive decision making. Hence, in this article, we review the thirty-year history of ML by elaborating on supervised learning, unsupervised learning, reinforcement learning and deep learning. Furthermore, we investigate their employment in the compelling applications of wireless networks, including heterogeneous networks (HetNets), cognitive radios (CR), Internet of things (IoT), machine to machine networks (M2M), and so on. This article aims for assisting the readers in clarifying the motivation and methodology of the various ML algorithms, so as to invoke them for hitherto unexplored services as well as scenarios of future wireless networks.Comment: 46 pages, 22 fig

Collaborative Vehicular Edge Computing Networks: Architecture Design and Research Challenges

The emergence of augmented reality (AR), autonomous driving and other new applications have greatly enriched the functionality of the vehicular networks. However, these applications usually require complex calculations and large amounts of storage, which puts tremendous pressure on traditional vehicular networks. Mobile edge computing (MEC) is proposed as a prospective technique to extend computing and storage resources to the edge of the network. Combined with MEC, the computing and storage capabilities of the vehicular network can be further enhanced. Therefore, in this paper, we explore the novel collaborative vehicular edge computing network (CVECN) architecture. We first review the work related to MEC and vehicular networks. Then we discuss the design principles of CVECN. Based on the principles, we present the detailed CVECN architecture, and introduce the corresponding functional modules, communication process, as well as the installation and deployment ideas. Furthermore, the promising technical challenges, including collaborative coalition formation, collaborative task offloading and mobility management, are presented. And some potential research issues for future research are highlighted. Finally, simulation results are verified that the proposed CVECN can significantly improve network performance