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A Neuro Fuzzy with Improved GA for Collaborative Spectrum Sensing in CRN

S. Velmurugan1,*, P. Ezhumalai2, E. A. Mary Anita3

1 Department of Computer Science and Engineering, Vel Tech Multi Tech Dr.Rangarajan Dr. Sakunthala Engineering College, Chennai, 600062, India
2 Department of Computer Science and Engineering, R.M.D. Engineering College, Chennai, 601206, India
3 Department of Computer Science and Engineering, School of Engineering and Technology, Christ University, Bengaluru, 560029, India

* Corresponding Author: S. Velmurugan. Email: email

Intelligent Automation & Soft Computing 2022, 34(2), 1093-1108. https://doi.org/10.32604/iasc.2022.026308

Abstract

Cognitive Radio Networks (CRN) have recently emerged as an important solution for addressing spectrum constraint and meeting the stringent criteria of future wireless communication. Collaborative spectrum sensing is incorporated in CRNs for proper channel selection since spectrum sensing is a critical capability of CRNs. According to this viewpoint, this study introduces a new Adaptive Neuro Fuzzy logic with Improved Genetic Algorithm based Channel Selection (ANFIGA-CS) technique for collaborative spectrum sensing in CRN. The suggested method’s purpose is to find the best transmission channel. To reduce spectrum sensing error, the suggested ANFIGA-CS model employs a clustering technique. The Adaptive Neuro Fuzzy Logic (ANFL) technique is then used to calculate the channel weight value and the channel with the highest weight is selected for transmission. To compute the channel weight, the proposed ANFIGA-CS model uses three fuzzy input parameters: Primary User (PU) utilization, Cognitive Radio (CR) count and channel capacity. To improve the channel selection process in CRN, the rules in the ANFL scheme are optimized using an updated genetic algorithm to increase overall efficiency. The suggested ANFIGA-CS model is simulated using the NS2 simulator and the results are investigated in terms of average interference ratio, spectrum opportunity utilization, average throughput, Packet Delivery Ratio (PDR) and End to End (ETE) delay in a network with a variable number of CRs.

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Cite This Article

S. Velmurugan, P. Ezhumalai and E. A. Mary Anita, "A neuro fuzzy with improved ga for collaborative spectrum sensing in crn," Intelligent Automation & Soft Computing, vol. 34, no.2, pp. 1093–1108, 2022. https://doi.org/10.32604/iasc.2022.026308



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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