Spectrum Inference in CRNS Section I : Introduction Section II : Preliminaries Data sources Models

Spectrum inference, also known as spectrum prediction in the literature, is a promising technique of inferring the occupied/free state of radio spectrum from already known/measured spectrum occupancy statistics by effectively exploiting the inherent correlations among them. In the past few years, spectrum inference has gained increasing attention owing to its wide applications in cognitive radio networks (CRNs), ranging from adaptive spectrum sensing, and predictive spectrum mobility, to dynamic spectrum access and smart topology control, to name just a few. In this paper, we provide a comprehensive survey and tutorial on the recent advances in spectrum inference. Specifically, we first present the preliminaries of spectrum inference, including the sources of spectrum occupancy statistics, the models of spectrum usage, and characterize the predictability of spectrum state evolution. By introducing the taxonomy of spectrum inference from a time-frequency-space perspective, we offer an in-depth tutorial on the existing algorithms. Furthermore, we provide a comparative analysis of various spectrum inference algorithms and discuss the metrics of evaluating the efficiency of spectrum inference. We also portray the various potential applications of spectrum inference in CRNs and beyond, with an outlook to the 5th generation mobile communications (5G) and next generation high frequency (HF) communications systems. Last but not least, we highlight the critical research challenges and open issues ahead. The work of G. Ding, J. Wang, and Q. Wu is supported in part by the National Natural Science Foundation of China under Grants 61501510 and 61631020, Natural Science Foundation of Jiangsu Province under Grant BK20150717, China Postdoctoral Science Funded Project, and Jiangsu Planned Projects for Postdoctoral Research Funds. The work of Y. Zou is supported in part by the National Natural Science Foundation of China under Grants 61401223 and 61522109 and the Natural Science Foundation of Jiangsu Province under Grants BK20140887 and BK20150040. The work of L. Hanzo is supported in part by the European Research Council’s Advance Fellow Grant Beam-Me-Up and the Royal Society’s Wolfson Research Merit Award. Y. Jiao and L. Hanzo are the corresponding authors. G. Ding and J. Wang are with the College of Communications Engineering, PLA University of Science and Technology, Nanjing 210007, China (email: dr.guoru.ding@ieee.org; wjl543@sina.com). G. Ding is also with the National Mobile Communications Research Laboratory, Southeast University, Nanjing 210096, China. Y. Jiao is with the School of Computer Science and Engineering, Nanyang Technological University, Singapore 639798 (email: yjiao001@ntu.edu.sg). This work was done while he was at Nanjing University of Posts and Telecommunications, Nanjing 210003, China. Y. Zou is with the School of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China (email: Yulong.Zou@njupt.edu.cn). Q. Wu is with the College of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, 211106, China (email: wuqihui2014@sina.com) Y. Yao is with the Department of Electrical and Computer Engineering, Stevens Institute of Technology, Hoboken, NJ 07030, USA (email: yyao@stevens.edu), and Northeast University, Shenyang, China. L. Hanzo is with the School of Electronics and Computer Science, University of Southampton, Southampton, UK (email: lh@ecs.soton.ac.uk).