A Neuro Fuzzy with Improved GA for Collaborative Spectrum Sensing in CRN
channel selection; Cognitive radio; improved genetic algorithm; spectrum assignment; spectrum sensing
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 methods 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. 2022, Tech Science Press. All rights reserved.
Velmurugan S.; Ezhumalai P.; Mary Anita E.A.
Intelligent Automation and Soft Computing, Vol-34, No. 2, pp. 1093-1108.
All Open Access; Hybrid Gold Open Access
https://doi.org/10.32604/iasc.2022.026308 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85132673766&doi=10.32604%2fiasc.2022.026308&partnerID=40&md5=137f1ce1364c85801aaf97a05c3a0b65
Velmurugan S., Department of Computer Science and Engineering, Vel Tech Multi Tech Dr.Rangarajan Dr. Sakunthala Engineering College, Chennai, 600062, India; Ezhumalai P., Department of Computer Science and Engineering, R.M.D. Engineering College, Chennai, 601206, India; Mary Anita E.A., Department of Computer Science and Engineering, School of Engineering and Technology, Christ University, Bengaluru, 560029, India