CR System with Efficient Spectrum Sensing and Optimized Handoff Latency to Get Best Quality of Service
Keywords:
Cognitive Radio, Spectrum detection, RSS, Fuzzy Logic, ANN, HandoffAbstract
A wireless communication technology called Cognitive Radio (CR) makes use of the environment. White hole detection indicates that unitized spectrum identification is crucial. Here, effective spectrum sensing is crucial. In this article, a new threshold for effective spectrum sensing is developed. The CR system is a secondary user of unutilized spectrum Unused spectrum is used by the CR system as a secondary user. Power is wasted during unnecessary handoffs. To maintain the level of service, optimized handoff is required. The second step of this paper provided a new algorithm for CR systems' handoff optimization. Rapid decision-making and preparation are undertaken as part of a proactive handoff strategy for channel allocation.
The system has considered two parameters, first one is maximum idle to busy ratio and second is minimum handoffs it saves the power to get optimized handoff delay
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J. Eric Salt, Member, IEEE, and Ha H. Nguyen, Senior Member, IEEE, “Performance Prediction for Energy Detection of Unknown Signals”, IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, VOL. 57, NO. 6, NOVEMBER 2008.
C. Cordeiro, K. Challapali, D. Birru, S. Shankar, IEEE 802.22: the first worldwide wireless standard based on cognitive radios, in Proc. IEEE DySPAN 2005, November 2005, pp. 328-337.
Shukla, A.; “Cognitive Radio Technology: A Study for Of com – Volume 1.” QINETIQ/06/00420 Issue 1.1, February 12th, 2007.
FCC Spectrum Policy Task Force, November 2002, ET Docket No. 02 135, Washington DC
XG Working Group. The XG vision. Request for comments. Version 2.0. Technical report, BBN Technologies, 2005.
L. Giupponi,Ana I. Perez-Neira, “Fuzzy-based Spectrum Handoff in C0gnitive Radio Networks”,2010
R.S.Kale, Dr.J.B.Helonde, Dr.V.M.Wadhai, “Efficient Spectrum Sensing In Cognitive Radio Using Energy Detection Method with New Threshold Formulation”, IEEE conference icmicr 2013, 4-6 June 2013
R.S.Kale, Dr.J.B.Helonde, Dr.V.M.Wadhai, “ New Algorithm for Handoff Optimization In Cognitive Radio Networks Using Fuzzy Logic and Artificial Neural Network” ERCICA 2013
S. Rajasekaran, G.A.Vijayalakshmi Pai, “Neural Networks, Fuzzy Logic, and Genetic Algorithms”
Ayman A EI Saleh, “Optimizing Spectrum Sensing Parameters for Local and Co-operative Cognative Radios” ISBN 978-89-5519-139-4 Feb. 15-18, ICACT 2009.
J. Park, “Implementation Issues of Wide Band Multi Resolution Spectrum Sensing (MRSS) Technique for Cognitive Radio(CR) System”, IEEE 2006
J. Eric Salt, Member, IEEE, and Ha H. Nguyen, Senior Member, IEEE, “Performance Prediction for Energy Detection of Unknown Signals” IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, VOL. 57, NO. 6, NOVEMBER 2008
Yonghong Zeng, Ying-Chang Liang, and Rui Zhang, “Blindly Combined Energy Detection for Spectrum Sensing in Cognitive Radio”, IEEE SIGNAL PROCESSING LETTERS, VOL. 15, 2008 649
Shunqing Zhang, Tianyu Wu, and Vincent K. N. Lau, “A Low-Overhead Energy Detection Based Cooperative Sensing Protocol for Cognitive Radio Systems” IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, VOL. 8, NO. 11, NOVEMBER 2009 5575
E. Del Re, R. Fantacci and G. Giambene, ―A Dynamic Channel Allocation Technique Based on Hopfield Neural Networks‖, IEEE Transactions on Vehicular Technology, Vol.VT-45, no.1, pp.26–32, 1995.
Shilian Zheng, Member, IEEE, Xiaoniu Yang, Shichuan Chen, and Caiyi Lou, “ Target Channel Sequence Selection Scheme for Proactive-Decision Spectrum Handoff”, IEEE COMMUNICATIONS LETTERS, VOL. 15, NO. 12, DECEMBER 2011
Ivan Christian, Sangman Moh, Ilyong Chung, and Jinyi Lee, Chosun University, “Spectrum Mobility in Cognitive Radio Networks” IEEE Communications Magazine • June 2012
Ian F. Akyildiz, Won-Yeol Lee, Mehmet C. Vuran *, Shantidev Mohanty, “NeXt generation/dynamic spectrum access/cognitive radio wireless networks: A survey ” Computer Networks 50 (2006) 2127–2159
Maurizio Murroni, “ IEEE 1900.6 : Spectrum Sensing Interfaces and Data structures for Dynamic Spectrum Access and Other Advanced Radio Communication Systems Standards: Technical Aspects and Future Outlook”, IEEE Communication Magazine Dec. 2011.
Maninder Jeet Kaur, Moin Uddin, Harsh K Verma, “Role of Cognitive Radio on 4G Communications A Review ,” Journal of Emerging Trends in Computing and Information SciencesVOL. 3, NO. 2, February 2012
Khetani, V., Gandhi, Y.., Bhattacharya, S. ., Ajani, S. N. ., & Limkar, S. . (2023). Cross-Domain Analysis of ML and DL: Evaluating their Impact in Diverse Domains. International Journal of Intelligent Systems and Applications in Engineering, 11(7s), 253–262.
Kale, R.S., Wadhai, V.M., Helonde, J.B. (2018). Novel Threshold Formulation for Energy Detection Method to Efficient Spectrum Sensing in Cognitive Radio. In: Urooj, S., Virmani, J. (eds) Sensors and Image Processing. Advances in Intelligent Systems and Computing, vol 651. Springer, Singapore. https://doi.org/10.1007/978-981-10-6614-6_3
Sandikar, R. S., et al. "New algorithm for handoff optimization in cognitive radio networks using fuzzy logic and artificial neural network." International Conference on Emerging Research in Computing, Information, Communication and Application. 2013.
Kaleem Arshid, Zhang Jianbiao, Iftikhar Hussain, Muhammad Salman Pathan, Muhammad Yaqub, Abdul Jawad, Rizwan Munir, Fahad Ahmad, “Energy efficiency in cognitive radio network using cooperative spectrum sensing based on hybrid spectrum handoff”, Egyptian Informatics Journal, Volume 23, Issue 4, 2022.
Bani, K.; Kulkarni, V. Hybrid Spectrum Sensing Using MD and ED for Cognitive Radio Networks. J. Sens. Actuator Netw. 2022, 11, 36. https://doi.org/10.3390/jsan11030036
Ms. Pooja Sahu. (2015). Automatic Speech Recognition in Mobile Customer Care Service. International Journal of New Practices in Management and Engineering, 4(01), 07 - 11. Retrieved from http://ijnpme.org/index.php/IJNPME/article/view/34
Dasi , S. ., & Rao, G. M. . (2023). Design and Analysis of Metamaterial Absorber using Split Ring Resonator for Dual Band Terahertz Applications. International Journal on Recent and Innovation Trends in Computing and Communication, 11(1), 128–132. https://doi.org/10.17762/ijritcc.v11i1.6059
Jain, V., Beram, S. M., Talukdar, V., Patil, T., Dhabliya, D., & Gupta, A. (2022). Accuracy enhancement in machine learning during blockchain based transaction classification. Paper presented at the PDGC 2022 - 2022 7th International Conference on Parallel, Distributed and Grid Computing, 536-540. doi:10.1109/PDGC56933.2022.10053213 Retrieved from www.scopus.com
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