In order to avoid the undesired interference with the activities of the primary users in cognitive radio networks, the secondary users are required to be able to predict the behavior of the primary users so as to leave the channel before the arrival of such licensed owner of the spectrum. While a number of existing literatures on cognitive radio spectrum prediction employ the use of propagation curves for predicting the spectrum holes otherwise known as TV white space, these models are built based on measurements conducted in regions that are different from Nigeria, suitability in terms of usage may therefore vary due to environmental factors and terrain profile. This work evaluates the efficacy of the developed model in predicting the cognitive spectrum availability in Nigeria. Models capable of predicting spectrum occupancy in the time domain using discrete-time two-state Markov chain with an appropriate Duty Cycle (DC) model and also a modified m-bell shaped exponential equation were formulated. The result obtained in all cases considered shows that the formulated models are appropriate to be used in any environment if the parameters were carefully extracted from the data. This work has also demonstrated that the accuracy of Markov chain models depends on the level of usage of a spectrum under consideration and may therefore not give desirable results when employed in some other spectrum.
KeywordsCognitive DevicePredictive ModelSpectrum ManagementSpectrum OccupancyTV White Space
Yang, L., Cao, L. and Zheng, H. (2008) Proactive Channel Access in Dynamic Spectrum Networks. Physical Communication, 1, 103-111. https://doi.org/10.1016/j.phycom.2008.05.001
Devanarayana, C. (2011) Predictive Channel Access in Cognitive Radio Networks Based on Variable Order Markov Models. MSc Thesis. Department of Electrical and Computer Engineering University of Manitoba Winnipeg, Manitoba R3T 5V6 Canada.
Hassan, A. (2015) Brief Overview of the Cognitive Radio Technologies. International Journal of Advanced Research in Computer Science and Software Engineering, 5, 21-28.
FCC (1996) Telecommunications Act of 1996. FCC Docket 96-325. Adopted on August 1, 1996 and Released on August 8, 1996.
Omole, S. (2015) When Will Nigeria Sign off Digital TV Switchover? NewsNG, June 22, 2015. https://guardian.ng/features/media/it-is-not-news-that-nigeria-failed-to-meet-up-with-the-digital-tv-transition-deadline-of-june-17-2015-but-the-question-of-when-exactly-the-countrys-digitization-process-which-officially-be/
Lopez-Benitez, M. and Casadevall, F. (2013) Time-Dimension Models of Spectrum Usage for the Analysis, Design, and Simulation of Cognitive Radio Networks. IEEE Transactions on Vehicular Technology, 62, 2091-2104. https://doi.org/10.1109/TVT.2013.2238960
Geirhofer, S., Tong, L. and Sadler, B. (2006) A Measurement-Based Model for Dynamic Spectrum Access in WLAN Channels. IEEE Military Communications Conference, Washington, DC, 23-25 October 2006, 1-7.
Ilango, V., Uma, I. and Nitya, R. (2015) Time Series Data Mining Research Problem, Issues, Models, Trends and Tools. International Journal of Computer Engineering and Applications, 9, 3-8.
Afrin, N., Zhang, Y. and Chen, J.K. (2015) Uncertainty Analysis of Melting and Resolidification of Gold Film Irradiated by Nano-to Femtosecond Lasers Using Stochastic Method. arXivpreprint arXiv:1510.00256
Cabric, D., Mishra, S. and Brodersen, R. (2004) Implementation Issues in Spectrum Sensing for Cognitive Radios. Conference Record of the 38th Asilomar Conference on Signals, Systems and Computers, Pacific Grove, CA, 7-10 November 2004, Vol. 1, 772-776. https://doi.org/10.1109/ACSSC.2004.1399240
López-Benítez, M. and Casadevall, F. (2017) Space-Dimension Models of Spectrum Usage for Cognitive Radio Networks. IEEE Transactions on Vehicular Technology, 66, 306-320.
Lopez-Benitez, M. and Casadevall, F. (2014) Spectrum Usage in Cognitive Radio Networks: From Field Measurements to Empirical Models. IEICE Transactions on Communications, 97, 242-250.
Mehdi, G., Shahrzad, I., Reza, G. and Mohammadreza, A. (2014) A Learning Automata-Based Spectrum Prediction Technique for Cognitive Radio Networks. International Transaction of Electrical and Computer Engineers System, 2, 93-97.
Najashi, B., Wenjiang, F. and Almustapha, M. (2014) Spectrum Hole Prediction Based on Historical Data: A Neural Network Approach. arXiv Preprint Arxiv:1401.0886
Faruk, N., Ayeni, A. and Adediran, Y. (2013) On the Study of Empirical Path Loss Models for Accurate Prediction of TV Signal for Secondary Users. Progress in Electromagnetic Research (PIER) B, 49, 155-176. https://doi.org/10.2528/PIERB13011306
Wellens, M., Riihijärvi, J. and Mähönen, P. (2009) Modelling Primary System Activity in Dynamic Spectrum Access Networks by Aggregated ON/OFF-Processes. 2009 6th IEEE Annual Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks Workshops, Rome, Italy, 22-26 June 2009, 1-6. https://doi.org/10.1109/SAHCNW.2009.5172946
Kumar, P.A., Singh, S. and Zheng, H. (2006) Reliable Open Spectrum Communications through Proactive Spectrum Access. Proceedings of the First International Workshop on Technology and Policy for Accessing Spectrum, Boston, Massachusetts, USA, 5 August 2006, 5. https://doi.org/10.1145/1234388.1234393
Pla, V., Vidal, J., Martinez-Bauset, J. and Guijarro, L. (2010) Modeling and Characterization of Spectrum White Spaces for Underlay Cognitive Radio Networks. 2010 IEEE International Conference on Communications, Cape Town, South Africa, 23-27 May 2010, 1-5.
Clancy, T. and Walker, B. (2006) Predictive Dynamic Spectrum Access. Proceedings of SDR Forum Technical Conference, Orlando, Florida, USA, November 2006.
Wellens, M. and Mähönen, P. (2010) Lessons Learned from an Extensive Spectrum Occupancy Measurement Campaign and a Stochastic Duty Cycle Model. Mobile Networks and Applications, 15, 461-474. https://doi.org/10.1007/s11036-009-0199-9
Höyhtyä, M., Pollin, S. and Mämmelä, A. (2011) Improving the Performance of Cognitive Radios through Classification, Learning, and Predictive Channel Selection. Advances in Electronics and Telecommunications, 2, 28-38.
Oluwaranti, A. and Okegbile, S. (2016) Two State Markov Chain Based Predictive Model for Cognitive Radio Spectrum Availability: A Conceptual Approach. 2016 Future Technologies Conference (FTC), San Francisco, CA, USA, 6-7 December 2016, 179-186. https://doi.org/10.1109/FTC.2016.7821608
López-Benítez, M. and Casadevall, F. (2011) Discrete-Time Spectrum Occupancy model Based on Markov Chain and Duty Cycle Models. 2011 IEEE International Symposium on Dynamic Spectrum Access Networks (DySPAN), Aachen, Germany, 3-6 May 2011, 90-99. https://doi.org/10.1109/DYSPAN.2011.5936273
Umebayashi, K., Kobayashi, M. and López-Benítez, M. (2018) Efficient Time Domain Deterministic-Stochastic Model of Spectrum Usage. IEEE Transactions on Wireless Communications, 17, 1518-1527. https://doi.org/10.1109/TWC.2017.2779511
Real Statistics (2018) Two Sample Kolmogorov-Smirnov Test. Real Statistics Using Excel. http://www.real-statistics.com/non-parametric-tests
Zaiontz, C. (2014) Two Sample Kolmogorov-Smirnov Test. A Publication of Real Statistics. http://www.real-statistics.com/non-parametric-tests
Xiao, Y. (2017). A Fast Algorithm for Two-Dimensional Kolmogorov-Smirnov Two Sample Tests. Computational Statistics & Data Analysis, 105, 53-58. https://doi.org/10.1016/j.csda.2016.07.014
Neuhäuser, M., Welz, A. and Ruxton, G.D. (2017) Statistical Tests for the Comparison of Two Samples: The General Alternative. Communications in Statistics-Simulation and Computation, 46, 903-909. https://doi.org/10.1080/03610918.2014.983651