Cesar Hernandez This email address is being protected from spambots. You need JavaScript enabled to view it.1 and Luis Tuberquia-David2

1Universidad Distrital Francisco José de Caldas, Technological Faculty, Calle 68D Bis A Sur # 49F - 70 Bloque 6, piso 1. Bogotá, Colombia
2Universidad Distrital Francisco José de Caldas, Faculty of Engineering, Carrera 7 # 40B–53. Bogotá, Colombia


 

Received: March 14, 2019
Accepted: August 6, 2019
Publication Date: December 1, 2019

Download Citation: ||https://doi.org/10.6180/jase.201912_22(4).0012  

ABSTRACT


The growing use of communication networks in radio electric spectrum applications has reached a saturation point. Spectral prediction has gained importance, as several models have been studied to improve the accuracy of predicted traffic compared to real traffic. This research aims to generate a spectrum prediction method within a radio electric space. The obtained traffic is generated with the MFHW algorithm, which creates traces based on parameters given by the user: mean, Hurst parameter and spectrum width. The results revealed marginal errors of 0.04% for the Hurst parameter and 0.085% for the estimation of the multifractal spectrum width. The MFHW algorithm builds traces with self-similarity, long-range dependence (LRD), and multifractal characteristics, inherent to the behavior of the radioelectric spectrum. Hence, the method discussed in this paper can establish a modeling framework for the generation of traces applied to spectrum prediction based on multifractal analysis.


Keywords: Traffic Generation, Multifractal, Spectrum, Wireless Communication


REFERENCES


  1. [1] Al-Tahmeesschi, A., M. López-Benítez, J. Lehtomäki, and K. Umebayashi (2017) Investigating the estimation of primary occupancy patterns under imperfect spectrum sensing, Proc. of 2017 IEEE Wireless Communications and Networking Conference Workshops, San Francisco, USA, 16. doi: 10.1109/WCNCW.2017.7919112
  2. [2] Spectrum Policy Task Force (2002) Public comment on issues related to commission’s spectrum, Federal Communications Commission, Report ET Docket No.02-135, USA.
  3. [3] Bernal, C., and C. Hernández (2019) Modelo de Decisión Espectral Para Redes de Radio Cognitiva, 1st Ed. UD, Bogotá, Colombia.
  4. [4] Ding, G., J.Wang, Q.Wu, Y. D. Yao, F. Song, and T. A. Tsiftsis (2016) Cellular-base-station assisted deviceto-device communications in TV white space, IEEE Journal on Selected Areas in Communications 34(1), 107–121. doi: 10.1109/JSAC.2015.2452532
  5. [5] Frisch, U., and G. Parisi (1985) On the singularity structure of fully developed turbulence in turbulence and predictability in geophysical fluid dynamics and climate dynamics, Proc. of 1985 International School of Physic Enrico Fermi, North-Holland, 7188.
  6. [6] Leland,W. E., and D. V.Wilson (1991) High time-resolution measurement and analysis of LAN traffic: implications for LAN interconnection, Proc. of IEEE Conference on Computer Communications, Bal Harbour, USA, 13601366. doi: 10.1109/INFCOM.1991.147663
  7. [7] Taqqu, M. S., V. Teverovsky, and W.Willinger (1997) Is network traffic self-similar or multifractal? Fractals 5, 6373. doi: 10.1142/S0218348X97000073
  8. [8] Riedi, R. H., and J. L. Vehel (1997) Multifractal properties of TCP traffic: a numerical study. Institut National de Recherche en Informatique et en Automatique 3129, 140.
  9. [9] Ma, S., and C. Ji (1998) Modeling video traffic in the wavelet domain. In INFOCOM ’98. Seventeenth Annual Joint Conference of the IEEE Computer and Communications Societies. Proceedings. IEEE. San Francisco, CA, USA. doi: 10.1109/INFCOM.1998.659655
  10. [10] Shenghui, W., and Q. Zhengding (2006) Multifractal analysis and prediction of VBR video traffic, 2006 6th International Conference on ITS Telecommunications Proceedings, 0–3, Chengdu, China: IEEE. doi: 10.1109/ITST.2006.288848
  11. [11] Hong, L.,Y. Tie, andW. Lanlan (2010) Network traffic prediction based on multifractalMLD model, 2010 International Workshop on Chaos-Fractals Theories and Applications (IWCFTA), 466–470, Kunming, Yunnan, Chin. doi: 10.1109/IWCFTA.2010.109
  12. [12] Yu, Y., M. Song, Y. Fu, and J. Song (2013) Traffic prediction in 3G mobile networks based on multifractal exploration, Tsinghua Science and Technology 18(4). doi: 10.1109/TST.2013.6574678
  13. [13] Guan, Q., F. R. Yu, S. Jiang, and G.Wei (2010) Prediction-based topology control and routing in cognitive radio mobile ad hoc networks, IEEE Transactions on Vehicular Technology 59(9), 4443–4452. doi: 10.1109/TVT.2010.2069105
  14. [14] Zhang, Z., K. Zhang, F. Gao, and S. Zhang (2015) Spectrum prediction and channel selection for sensing-based spectrum sharing scheme using online learning techniques, 2015 IEEE 26th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC), 355–359, Hong Kong, China: IEEE.
  15. [15] Chen, Y., and H. S. Oh (2016) Spectrum measurement modelling and prediction based on wavelets, IET Communications 10(16), 2192–2198. doi: 10.1109/PIMRC.2015.7343323
  16. [16] Thakur, P., A. Kumar, S. Pandit, G. Singh, and S. N. Satashia (2016) Performance improvement of cognitive radio network using spectrum prediction and monitoring techniques for spectrum mobility, Proc. of 2016 Fourth International Conference on Parallel, Distributed and Grid Computing, Waknaghat, India, 510. doi: 10.1109/PDGC.2016.7913208
  17. [17] Kim, J., S. W. Ko, H. Cha, and S. L. Kim (2017) Sense-and-predict: opportunistic MAC based on spatial interference correlation for cognitive radio networks, Proc. of 2017 IEEE International Symposium on Dynamic Spectrum Access Networks, Piscataway, USA, 110. doi: 10.1109/DySPAN.2017.7920787
  18. [18] Aref, M. A., and S. K. Jayaweera (2017) Anovel cognitive anti-jamming stochastic game, Proc. of 2017 Cognitive Communications for Aerospace Applications Workshop, Cleveland, USA, 14. doi: 10.1109/CCAAW.2017.8001605
  19. [19] Pedraza, L., F. Forero, and I. Paez (2013) Metropolitan spectrum survey in Bogota Colombia, Proc. of IEEE International Conference on Advanced Information Networking and Applications Workshops, Barcelona, Spain, 548553. doi: 10.1109/WAINA.2013.177
  20. [20] Hernández, C., and D. Giral (2015) Spectrum mobility analytical tool for cognitive wireless networks, International Journal of Applied Engineering Research 10(21), 42265–42274.
  21. [21] Kleinrock, L. (1975) Queueing Systems: Vol. I: Theory. 1st Ed. JohnWiley & Sons, Inc, New York.
  22. [22] Leland, W. E., M. S. Taqqu, W. Willinger, and D. V. Wilson (1994) On the self-similar nature of ethernet traffic, IEEE/ACM Transactions on Networking 2(1), 1–15. doi: 10.1109/90.282603
  23. [23] Millán, G. (2009) Análisis de Autosimilaridad de Tráfico Telemático Restringido al Nivel de Red,MSc. thesis, Pontificia Universidad Católica de Valparaíso, Chile.
  24. [24] Tuberquia-David, L. M., H. López, and C. Hernández (2019) A Multifractal Model for Cognitive Radio Networks, 1st Ed. UD, Bogotá, Colombia.
  25. [25] Murali, K. P., V. M. Gadre, and U. B. Desai (2003) In Multifractal Based Network Traffic Modeling, 1st Ed. Springer, Boston, USA. doi: 10.1007/978-1-4615-0499-3_1
  26. [26] López Chávez, H. I. and M. Alzate (2012) Generation of LRD traffic traces with given sample statistics, Proc. of 2012 IEEE Workshop on Engineering Applications, Bogotá, Colombia, 16. doi: 10.1109/WEA.2012.6220077
  27. [27] Riedi, R. H., M. S. Crouse, V. J. Ribeiro, and R. G. Baraniuk (1999) A multifractal wavelet model with application to network traffic, IEEE Transactions on Information Theory 45(3), 992–1018. doi: 10.1109/18.761337
  28. [28] Tuberquia-David,M., F. Vela-Vargas, H. López-Chávez, and C. Hernández (2016) Amultifractal wavelet model for the generation of long-range dependency traffic traces with adjustable parameters, Expert Systems with Applications 62, 373–384. doi: 10.1016/j.eswa.2016.05.010