Predicting Spectral Opportunities in Cognitive Radio Network based on Neuro-Fuzzy for Bandwidth Optimization
Abstract
The most important problem in telecommunication is bandwidth limitation due to the uncontrolled growth of wireless technology. Deploying dynamic spectrum access techniques is one of the procedures provided for efficient use of bandwidth. In recent years, cognitive radio network introduced as a tool for efficient use of spectrum. These radios are able to use radio resources by recognizing surroundings via sensors and signal operations that means use these resources only when authorized users do not use their spectrum. Secondary users are unauthorized ones that must avoid from interferences with primary users transmission. Secondary users must leave channel due to preventing damages to primary users whenever these users discretion. In this article, spectrum opportunities prediction based on neuro-fuzzy network for bandwidth optimization and reducing the amount of energy by predicting spectrum holes discovery for quality of services optimization proposed in cognitive radio network. The result of the simulation represent acceptable value of SNR and bandwidth optimization in these networks that allows secondary users to taking spectrum and sending data without collision and overlapping with primary users.
References
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[2] C. M. Wu, H. K. Su, M. L. Leou, Y. C. Liaw, and C. P. Lo, “Cooperative Power and Contention Control MAC Protocol in Multichannel Cognitive Radio Ad Hoc Networks,” Eighth International Conference on In Innovative Mobile and Internet Services in Ubiquitous Computing (IMIS), pp. 305-309, 2014.
[3] H. Chen and J. S. Baras, “Distributed opportunistic scheduling for wireless ad-hoc networks with block-fading model,” IEEE Journal on Selected Areas in Communications, Vol. 31, Issue 11, pp. 2324-2337, 2013.
[4] C. Y. Zhang and K. Liu, “Modeling and Analysis of Opportunistic Spectrum Sharing Systems with Markov Approach,” Applied Mechanics and Materials, Vol. 135, pp. 621-627, 2012.
[5] X. Xing, T. Jing, Y. Huo, H. Li, and X. Cheng,“Channel quality prediction based on Bayesian inference in cognitive radio networks,” Proceedings IEEE in INFOCOM, pp. 1465-1473, 2013.
[6] B. Wang and K. J. R. Liu, “Advances in cognitive radio networks: A survey,” IEEE Journal of Selected Topics in Signal Processing, Vol. 5, Issue 1, pp. 5-23, 2011.
[7] A. C. Talay and D. T. Altilar, “RAC: Range adaptive cognitive radio networks,” Fourth International Conference on Communications and Networking in China, ChinaCOM, pp. 1-5, 2009.
[8] C. W. Wang and F. Adachi, “Load-balancing spectrum decision for cognitive radio networks,” IEEE Journal on Selected Areas in Communications, Vol. 29, Issue 4, pp. 757-769, 2011.
[9] I. Alqerm, B. Shihada, “Adaptive Decision Making Scheme for cognitive Radio Networks,” IEEE 28th International Conference on Advanced Information Networking and Applications, pp. 321 – 328, 2014.
[10] X. Xing, T. Jing, W. Cheng, Y. Huo, and X. Cheng, “Spectrum prediction in cognitive radio networks,” IEEE Wireless Communications, Vol. 20, Issue 2, pp. 90-96, 2013.
[11] A. Katidiotis, K. Tsagkaris, and P. Demestichas, “Artificial Neural Network based Learning in Cognitive Radio,” Computer and Electrical Engineering, Vol. 36, Issue 3, pp-518-535, 2010.
[12] S. Pattanayak, P. Venkateswaran, and R. Nandi, “Artificial Intelligence Based Model for Channel Status Prediction: A New Spectrum Sensing Technique for Cognitive Radio,” International Journal of Communications, Network and System Sciences, Vol. 6, Issue 3, pp. 139-148, 2013.
[13] Kunwei Lan, Hangsheng Zhao, Jianzhao Zhang, Cao Long, and Menglin Luo, “A Spectrum Prediction Approach based on Neural Networks Optimized by Genetic Algorithm in Cognitive Radio Networks,” 10th International Conference on Wireless Communications, Networking and Mobile Computing (WiCOM), pp. 131-136, 2014.
[14] V. K. Tumuluru, P. Wang, and D. Niyato, “A Neural Network Based Spectrum Prediction Scheme for cognitive Radio,” IEEE International Conference on Communictions (ICC), pp. 1-5, 2010.
[15] Vijayakumar Ponnusamy, Kottilingam Kottursamy, and Tariq Ahamed Ahanger, “Primary user emulation attack mitigation using neural network,” Computers & Electrical Engineering, Vol. 88, December 2020.
[16] Simon Haykin, “Neural Networks-A Comprehensive Foundation,” 2nd Edition, McMaster University, Hamilton, Ontario, Canada, 2012.
[17] C. Wang, K. Sohraby, R. Jana, and L. Ji “On Network Selection for Secondary Users in Cognitive Radio Networks,” INFOCOM Proceedings IEEE, pp. 2741 – 2749, 2011.
[18] Hang Su and Xi Zhang, “Cross-Layer Based Opportunistic MAC Protocols for QoS Provisionings Over Cognitive Radio Wireless Networks,” IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS, VOL. 26, NO. 1, 2008.
[19] Sami Akin and Mustafa Cenk Gursoy, “Performance Analysis of Cognitive Radio Systems Under QoS Constraints and Channel Uncertainty,” IEEE Transactions on Wireless Communications, Vol. 10 , Issue 9, pp. 2883 – 2895, 2010.
[20] Nestor D. Chatzidiamantis, E. Matskani, L. Georgiadis, I. Koutsopoulos, and L. Tassiulas, “Optimal Primary-Secondary user Cooperation Policies in Cognitive Radio Networks,” IEEE Transactions on Wireless Communications, Vol.14, Issue 6, pp. 3443 – 3455, 2014.
[21] Salah Zahed, Irfan Avan, and Andrea Cullen, “Analytical modeling for spectrum handoff decision in cognitive radio networks,” Science Direct, Simulation Modelling Practice and Theory, Vol. 38, pp. 98–114, 2014.
[22] Y. A. Al-Gumaei and K. Dimyati, “Optimal power control game for primary-secondary user in cognitive radio network,” International Journal of Physical Sciences Vol. 5, Issue 4, pp. 345-351, 2010.
[23] Ammar Alshamrani, X. S. Shen, and L. L. Xie, “QoS Provisioning for Heterogeneous Services in Cooperative Cognitive Radio Networks,” IEEE Journal on Selected Areas in Communications, Vol. 29, Issue. 4, 2011.
[24] Sheikh Fakhar Uddin and Ismail Khan Khattak, “Spectrum Selection Technique to Satisfy the QoS Requirements in Cognitive Radio Network,” Master Thesis of Electrical Engineering, School of Computing Blekinge Institute of Technology, Karlskrona, Sweden, 2012.
[25] Luca Zappaterra, “QoS-driven Channel Selection for Heterogeneous Cognitive Radio Networks,” Proceedings of the 2012 ACM conference -on CoNEXT student workshop, pp. 7-8, 2012.
[26] Rong Yu, Yan Zhang, Liu Yi, Shengli Xie, Lingyang Song, and Mohsen Guizani, “Secondary Users Cooperation in Cognitive Radio Networks: Balancing Sensing Accuracy and Efficiency,” IEEE Wireless Communications, Vol. 19, Issue 2, pp. 30-37, 2012.
[27] Daniel Willkomm and Adam Wolisz, “Efficient QoS Support for Secondary Users in Cognitive Radio Systems,” IEEE Wireless Communications, Vol. 17 , Issue 4, pp. 16-23, 2014.
[28] S. M. Kay, “Fundamentals of Statistical Signal Processing: Detection Theory”, Vol.2, Prentice Hall, 1998.
[29] H. V. Poor, “An introduction to signal detection and estimation”, Second Edition, Chapter IV: Selected Solutions, Princeton University, 2005.
[30] H. Urkowitz, “Energy detection of unknown deterministic signals,” Proceeding of the IEEE, vol. 55, no. 4, pp. 523-531, 1967.
[31] A. Sonnenschein and P.M. Fishman, “Radio metric detection of spread spectrum signals in noise of uncertainty power,” IEEE Transactions on Aerospace and Electronic Systems, Vol. 28, No. 3, pp. 664-660, 1992.
[32] H. L. Van-Trees, Kristine L. Bell, and Zhi Tian, “Detection, estimation and modulation theory”, John Wiley & Sons, 2013.
[33] W. A. Gardner, “Exploitation of spectral redundancy in cyclostationary signals,” IEEE Signal Processing Magazine, Vol. 8, Issue 2, pp. 14-36, 1991.
[34] W. A. Gardner, “Spectral Correlation of modulated signals: Part I-analog modulation,” IEEE Transactions on Communications, Vol. 35, Issue .6, pp. 584-595, 1987.
[35] W. A. Gardner, W.A. Brown, and C. K. Chen, “Spectral Correlation of modulated signals: Part II-digital modulation,” IEEE Transaction of Communications, Vol. 35, Issue .6, pp. 595-601, 1987.
[36] Y. H. Zeng, Y. C. Liang, A. T. Hoang, and R. Zhang, “A review on spectrum sensing for cognitive radio: Challenges and solutions,” EURASIP Journal on Advances in Signal Processing, pp. 1-15. 201.
Published
2020-12-08
How to Cite
Aberomand, N. (2020). Predicting Spectral Opportunities in Cognitive Radio Network based on Neuro-Fuzzy for Bandwidth Optimization: Array. Acta Universitatis Danubius. Communicatio, 14(2). Retrieved from https://dj.univ-danubius.ro/index.php/AUDC/article/view/702
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