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

A new splitting-based displacement prediction approach for location-based services

In location-based services (LBSs), the service is provided based on the users' locations through location determination and mobility realization. Several location prediction models have been proposed to enhance and increase the relevance of the information retrieved by users of mobile information systems, but none of them studied the relationship between accuracy rate of prediction and the performance of the model in terms of consuming resources and constraints of mobile devices. Most of the current location prediction research is focused on generalized location models, where the geographic extent is divided into regular-shape cells. These models are not suitable for certain LBSs where the objectives are to compute and present on-road services. One such technique is the Prediction Location Model (PLM), which deals with inner cell structure. The PLM technique suffers from memory usage and poor accuracy. The main goal of this paper is to propose a new path prediction technique for Location-Based Services. The new approach is competitive and more efficient compared to PLM regarding measurements such as accuracy rate of location prediction and memory usage