Journal of Soft Computing Paradigm
IRO Journals
Volume 1 — Issue 2 — December 2019

PERFORMANCE ENHANCEMENTS OF COGNITIVE RADIO NETWORKS USING THE IMPROVED FUZZY LOGIC

https://doi.org/10.36548/jscp.2019.2.001
P Ebby Darney
Professor, Department of EEE, SCAD CET
I. Jeena Jacob
Department of Computer Science and Engineering, GITAM University, Bangalore, India
PDF
PDF

How to Cite

Darney, P Ebby, and I. Jeena Jacob. 2019. “PERFORMANCE ENHANCEMENTS OF COGNITIVE RADIO NETWORKS USING THE IMPROVED FUZZY LOGIC”. Journal of Soft Computing Paradigm 1 (2): 57-68. https://doi.org/10.36548/jscp.2019.2.001.
ACM ACS APA ABNT Chicago Harvard IEEE MLA Turabian Vancouver AMA

Download Citation

Endnote/Zotero/Mendeley (RIS) BibTeX

Keywords

— Improved Fuzzy Logic
— Cognitive Radio Technology
— Decision Support Systems
— Channel Switching
— Throughput and Switching Rate
Published: 31-12-2019

Abstract

The rapid increase in the mobile device and the different types of wireless communication has led to the necessity of the extra spectrum allocation for the proper transmission of the information. Since the additional spectrum allocation for every network involved in the data transmission is a strenuous process, the efficient management of the spectrum allocation is preferred. The cognitive radio technology does a befitting service in the managing the allocation of the spectrum efficiently by providing the vacant spaces of the licensed users to the secondary users and vacating the secondary users when the licensed user request for the spectrum. This results in the deterioration in the performance of the secondary users due to the immediate evacuating. The conventional methods in the deciding the channel switching remains unsuitable for the cognitive radio network, so to have an effective decision on switching and selecting the channel the paper put forth the improved fuzzy logic that relies on the decision (IFDSS-GA) support system to handle both the switching of the channels and genetic algorithm to select the proper spectrum for conveyance. The evaluation of the proposed approach using the network simulator -2 determines the competency the IFDSS in terms of the throughput and switching rate.

References

  1. Baldo, Nicola, and Michele Zorzi. "Fuzzy logic for cross-layer optimization in cognitive radio networks." IEEE Communications magazine 46, no. 4 (2008): 64-71.
  2. Zhang, Hongtao, and Xiaoxiang Wang. "A fuzzy decision scheme for cooperative spectrum sensing in cognitive radio." In 2011 IEEE 73rd Vehicular Technology Conference (VTC Spring), pp. 1-4. IEEE, 2011.
  3. Baldo, Nicola, and Michele Zorzi. "Cognitive network access using fuzzy decision making." IEEE Transactions on Wireless Communications 8, no. 7 (2009): 3523-3535.
  4. Niyato, Dusit, and Ekram Hossain. "Cognitive radio for next-generation wireless networks: An approach to opportunistic channel selection in IEEE 802.11-based wireless mesh." IEEE Wireless Communications 16, no. 1 (2009): 46-54.
  5. Mathad, Anandteerth, and Mrinal Sarvagya. "Cross layer design approaches, schemes and optimization methodologies for cognitive radio networks: a survey." Quest journals, Journal of electronics and communication engineering research 1, no. 4 (2013): 15-21.
  6. Joshi, Gyanendra Prasad, Seung Yeob Nam, and Sung Won Kim. "Cognitive radio wireless sensor networks: applications, challenges and research trends." Sensors 13, no. 9 (2013): 11196-11228.
  7. Ejaz, Waleed, Najam ul Hasan, Saleem Aslam, and Hyung Seok Kim. "Fuzzy logic based spectrum sensing for cognitive radio networks." In 2011 Fifth International Conference on Next Generation Mobile Applications, Services and Technologies, pp. 185-189. IEEE, 2011.
  8. El Masri, Ali, Naceur Malouch, and Hicham Khalife. "A routing strategy for cognitive radio networks using fuzzy logic decisions." In the proceedings of the first Conference Cognitive Advances in Cognitive Radio IARIA COCORA, pp. 1-14. 2011.
  9. Matinmikko, Marja, Javier Del Ser, Tapio Rauma, and Miia Mustonen. "Fuzzy-logic based framework for spectrum availability assessment in cognitive radio systems." IEEE Journal on Selected Areas in Communications 31, no. 11 (2013): 2173-2184.
  10. PARK, Chee-Hyun, and Kwang-Seok HONG. "FOREWORDApplication of Fuzzy Logic to Cognitive Radio SystemsDynamic Spectrum Access to the Combined Resource of Commercial and Public Safety Bands Based on a WCDMA Shared NetworkDynamic Resource Allocation in OFDMA Systems with Adjustable QoSA Novel Dynamic Channel Access Scheme Using Overlap FFT Filter-Bank for Cognitive RadioPerformance Analysis of Control Signal Transmission Technique for Cognitive Radios in Dynamic Spectrum Access NetworksSpectrum Sensing Architecture and Use Case Study ...."
  11. Tripathi, Shrivishal, Ashish Upadhyay, Shashank Kotyan, and Sandeep Yadav. "Analysis and Comparison of Different Fuzzy Inference Systems Used in Decision Making for Secondary Users in Cognitive Radio Network." Wireless Personal Communications 104, no. 3 (2019): 1175-1208.
  12. Banerjee, Avik, and Santi P. Maity. "Joint cooperative spectrum sensing and primary user emulation attack detection in cognitive radio networks using fuzzy conditional entropy maximization." Transactions on Emerging Telecommunications Technologies 30, no. 5 (2019): e3567.
  13. Alhammadi, Abdulraqeb, Mardeni Roslee, Mohamad Yusoff Alias, Khalid Sheikhidris, Yong Jun Jack, Anas Bin Abas, and Kesh S. Randhava. "An intelligent spectrum handoff scheme based on multiple attribute decision making for LTE-A network." International Journal of Electrical & Computer Engineering (2088-8708) 9 (2019).
  14. Ekti, Ali Riza. "Fuzzy Logic Approach for Layered Architecture Cognitive Radio Systems." In International Telecommunications Conference, pp. 61-71. Springer, Singapore, 2019.
  15. Mathur, Chetan N., and K. P. Subbalakshmi. "Security Issues in Cognitive Radio Networks." Cognitive Networks: Towards Self-Aware Networks (2007): 271.
  16. Thakur, Prabhat, Alok Kumar, Shweta Pandit, Ghanshyam Singh, and S. N. Satashia. "Performance analysis of high-traffic cognitive radio communication system using hybrid spectrum access, prediction and monitoring techniques." Wireless Networks 24, no. 6 (2018): 2005-2015.
  17. Ali, Amjad, Ibrar Yaqoob, Ejaz Ahmed, Muhammad Imran, Kyung Sup Kwak, Adnan Ahmad, Syed Asad Hussain, and Zulfiqar Ali. "Channel clustering and QoS level identification scheme for multi-channel cognitive radio networks." IEEE Communications Magazine 56, no. 4 (2018): 164-171.
  18. Liang, Wei, Soon Xin Ng, and Lajos Hanzo. "Cooperative overlay spectrum access in cognitive radio networks." IEEE Communications Surveys & Tutorials 19, no. 3 (2017): 1924-1944.
  19. Zhao, Zhijin, Zhen Peng, Shilian Zheng, and Junna Shang. "Cognitive radio spectrum allocation using evolutionary algorithms." IEEE Transactions on Wireless Communications 8, no. 9 (2009): 4421-4425.