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PP-18 coneference

Cognitive Radio Networks

​Title

Bayesian Online Learning-Based Spectrum Occupancy Prediction in Cognitive Radio Networks

Abstract

Predicting the near future of primary user (PU) channel state availability (i.e. spectrum occupancy) is quite important in cognitive radio networks in order to avoid interfering its transmission by a cognitive spectrum user (i.e. secondary user (SU)). This paper introduces a new simple method for predicting PU channel state based on energy detection. In this method, we model the PU channel state detection sequence (i.e. “PU channel idle” and “PU channel occupied”) as a time series represented by two different random variable distributions. We then introduce Bayesian online learning (BOL) to predict in advance the changes in time series (i.e. PU channel state.), so that the secondary user can adjust its transmission strategies accordingly. A simulation result proves the efficiency of the new approach in predicting PU channel state availability.

Keywords

Bayesian online learning, cognitive radio, primary user, spectrum occupancy prediction

Author

Ahmed Mohammed Mikaeil
(Shanghai Jiao Tong University, China)

Ahmed Mohammed Mikaeil received his Master's degree in Electronics and Communications Engineering from Changchun University of Science and Technology, Changchun, China, in 2015.He is currently working toward his Ph.D. degree at the Department of Electronic Engineering, Shanghai Jiao Tong University. His current research focuses on 5G technologies such as SDN, NFV, cloud radio access network (C-RAN) and network slicing.  His research interests include optical and wireless convergence, modeling and simulation of communication network system and machine learning application in wireless communication and networking.