Abstract
Spectrum occupancy information is necessary in a cognitive radio network (CRN) as it helps in modeling and predicting the spectrum availability for efficient dynamic spectrum access (DSA). However, in a CRN, it is difficult to ascertain a priori the pattern of the spectrum usage of the primary user due to its stochastic behavior. In this context, the spectrum occupancy prediction proves to be very useful in enhancing the quality of experience of the secondary user. This paper investigates the practical prowess of various time-series modeling approaches and the machine learning (ML) techniques for predicting spectrum occupancy, based on a spectrum measurement campaign conducted in Jaipur, Rajasthan, India. Moreover, the comparison analysis conducted between the above two approaches highlights the trade-off in terms of the respective performance depending upon the nature of the spectrum occupancy data. Nevertheless, prediction through ML-based recurrent neural network proves to perform reasonably well, thereby providing an accurate future spectrum occupancy information for DSA.
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The associate editor coordinating the review of this paper and approving it for publication was G. R. Ding.
Anirudh Agarwal [corresponding author] received his B.Tech. degree in 2014, and is pursuing his Ph.D. degree in Electronics & Communications Engineering from The LNM Institute of Information Technology (LNMIIT), Jaipur, India. He has been actively involved in the Indian Government project, “Mobile Broadband Service Support Over Cognitive Radio Networks”, funded by ITRA-MediaLab Asia, Ministry of Electronics & IT, Govt. of India. He has coauthored one book chapter and many papers in refereed journals and recognized conferences. His research interests include mobile and wireless communication systems, with special emphasis on applied machine learning based quality of experience of CR users provisioning, spectrum occupancy modeling and dynamic spectrum access in CR networks.
Aditya S. Sengar received his B.Tech. degree in 2014, M.Tech. degree in 2016 and is pursuing his Ph.D. degree in Electronics & Communications Engineering from The LNM Institute of Information Technology (LNMIIT), Jaipur, India. He has co-authored many papers in refereed journals and recognized conferences. His research interests include cognitive radio systems, device-to-device communication, 5G wireless networks.
Ranjan Gangopadhyay received his Ph.D. degree from IIT, Kharagpur. He served as a professor and Head of the Department of E & ECE, IIT Kharagpur. After superannuation (2006), he joined the G. S. Sanyal School of Telecommunication, IIT, Kharagpur as an Emeritus Professor and worked there for two years before joining The LNM Institute of Information Technology, Jaipur as a Distinguished Professor. Prof. Gangopadhyay has also served as a visiting professor in University of Parma (Italy), Scuola Superiore Sant’Anna, Pisa (Italy), British Telecom (UK), University of Ottawa, Chonbuk National University (South Korea) and Central Laboratory (Japan). His research interests include Photonics communication and wireless technology.
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Agarwal, A., Sengar, A.S. & Gangopadhyay, R. Spectrum Occupancy Prediction for Realistic Traffic Scenarios: Time Series versus Learning-Based Models. J. Commun. Inf. Netw. 3, 44–51 (2018). https://doi.org/10.1007/s41650-018-0013-6
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DOI: https://doi.org/10.1007/s41650-018-0013-6