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

Joint Aerial-Terrestrial Resource Management in UAV-Aided Mobile Radio Networks

This article addresses the issue of joint aerial- terrestrial resource management in mobile radio networks supported by a UAV operating as network node and discusses the potential of true integration between the terrestrial and UAV components of the network. A simulation campaign shows that, by properly optimizing the system parameters related to the UAV flight, a single UAV can bring significant improvement in network throughput for a wide service area. The use of a joint radio resource management approach, where the UAV and terrestrial base stations operate in a coordinated manner, brings significant advantages with respect to different algorithms