Abstract
Spectrum management always appears as an essential part of modern communication systems. Handoff is initiated when the signal strength of a current user deteriorates below a certain threshold. In cognitive radio network, the perception of handoff is different due to the presence of two categories of users: certified/primary user and uncertified/secondary user. The reason for the spectrum handoff arises when the primary user (PU) returns to one of its band used by the secondary user. The spectrum handoff is of two types: reactive handoff and proactive handoff. There are certain limitations in reactive handoff, such as it suffers from prolonged handoff latency and interference. In the proactive handoff, the operation of handoff is planned and implemented by predicting the emergence of primary user based on the historical data usage. Therefore, proactive handoff boosts the performance of a cognitive radio network. In this work, a spectrum prediction technique is proposed for ensuring the spectrum mobility using machine learning. Machine learning techniques such as decision tree, random forest, stochastic gradient classifier, logistic regression, multilayer perceptron, and support vector machine are researched and implemented. The performance of different techniques is compared, and the accuracy of prediction is measured.
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References
Cabric, D., Mishra, S.M., Brodersen, R.W.: Implementation issues in spectrum sensing for cognitive radios, Vol. 771, pp. 772–776 (2004)
Haykin, S.: Cognitive radio: brain-empowered wireless communications. IEEE J Sel Areas Commun 23(2), 201–220 (2005)
Dehalwar, V., Sunita Kolhe, M.K.: Cognitive radio application for smart grid. Int. J. Smart Grid Clean Energy 1(1) (2012)
Dehalwar, V., Kalam, A., Kolhe, M.L., Zayegh, A.: Compliance of IEEE 802.22 WRAN for field area network in smart grid, pp. 1–6 (2016)
Dehalwar, V., Kalam, A., Zayegh, A.: Infrastructure for real-time communication in smart grid, pp. 1–4 (2014)
Lee, W.Y., Akyildiz, I.F.: Spectrum-aware mobility management in cognitive radio cellular networks. IEEE Trans. Mob. Comput. 11(4), 529–542 (2012)
Mishra, A., Dehalwar, V., Jobanputra, J.H., Kolhe, M.: Spectrum hole detection for cognitive radio through energy detection using random forest. In: Proc. International Conference on Emerging Technology (INCET), IEEE, 05/06/2020 2020 pp. Pages
Wyglinski, A.M., Hou, N.: Cognitive radio communications and networks principles and practice (2010)
Ridouani, M., Hayar, A., Haqiq, A.: Perform sensing and transmission in parallel in cognitive radio systems: spectrum and energy efficiency. Digit. Signal Proc. 62, 65–80 (2017)
Christian, I., Moh, S., Chung, I., Lee, J.: Spectrum mobility in cognitive radio networks. IEEE Commun. Mag. 50(6), 114–121 (2012)
Ali, A., Hamouda, W.: Advances on spectrum sensing for cognitive radio networks: theory and applications. IEEE Commun. Surv. Tutor. 19(2), 1277–1304 (2017)
Yang, L., Cao, L., Zheng, H.: Proactive channel access in dynamic spectrum networks. Phys. Commun. 1(2), 103–111 (2008)
Tumuluru, V.K., Wang, P., Niyato, D.: A neural network based spectrum prediction scheme for cognitive radio, pp. 1–5 (2010)
Xing, X., Jing, T., Cheng, W., Huo, Y., Cheng, X.: Spectrum prediction in cognitive radio networks. IEEE Wirel. Commun. 20(2), 90–96 (2013)
Xing, X., Jing, T., Huo, Y., Li, H., Cheng, X.: Channel quality prediction based on Bayesian inference in cognitive radio networks, pp. 1465–1473 (2013)
İ, B., Talay, A.Ç., Altilar, D.T., Khalid, M., Sankar, R.: Impact of mobility prediction on the performance of Cognitive Radio networks, pp. 1–5 (2010)
Wen, Z., Luo, T., Xiang, W., Majhi, S., Ma, Y.: Autoregressive spectrum hole prediction model for cognitive radio systems, pp. 154–157 (2008)
Wang, C., Wang, L.: Modeling and analysis for proactive-decision spectrum handoff in cognitive radio networks, pp. 1–6 (2009)
Zhang, Y.: Spectrum handoff in cognitive radio networks: opportunistic and negotiated situations, pp. 1–6 (2009)
Shawel, B.S., Woledegebre, D.H., Pollin, S.: Deep-learning based cooperative spectrum prediction for cognitive networks, pp. 133–137 (2018)
Supraja, P., Pitchai, R.: Spectrum prediction in cognitive radio with hybrid optimized neural network. Mobile Netw. Appl. 24(2), 357–364 (2019)
Couturier, S., Krygier, J., Bentstuen, O.I., Le Nir, V.: Challenges for network aspects of cognitive radio (2015)
Plata, D.M.M., Reátiga, Á.G.A.: Evaluation of energy detection for spectrum sensing based on the dynamic selection of detection-threshold. Procedia Eng. 35, 135–143 (2012)
Navada, A., Ansari, A.N., Patil, S., Sonkamble, B.A.: Overview of use of decision tree algorithms in machine learning, pp. 37–42 (2011)
Wang, X., Liu, Z., Wang, J., Wang, B., Hu, X.: A spectrum sensing method for cognitive network using Kernel principal component analysis and random forest, pp. 5682–5687 (2014)
Chen, C.C.M., Schwender, H., Keith, J., Nunkesser, R., Mengersen, K., Macrossan, P.: Methods for identifying SNP interactions: a review on variations of logic regression, random forest and Bayesian logistic regression. IEEE/ACM Trans. Comput. Biol. Bioinf. 8(6), 1580–1591 (2011)
Ettaouil Mohamed, L.M., Ghanou, Y., Abdellah, B.: Architecture optimization model for the multilayer perceptron and clustering (2013)
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Wajhal, G., Dehalwar, V., Jha, A., Ogura, K., Kolhe, M.L. (2021). Proactive Handoff of Secondary User in Cognitive Radio Network Using Machine Learning Techniques. In: Pandian, A.P., Palanisamy, R., Ntalianis, K. (eds) Proceedings of International Conference on Intelligent Computing, Information and Control Systems. Advances in Intelligent Systems and Computing, vol 1272. Springer, Singapore. https://doi.org/10.1007/978-981-15-8443-5_2
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