Artificial Neural Network with Firefly Algorithm-Based Collaborative Spectrum Sensing in Cognitive Radio Networks
subject: Channel Selection; Cognitive radio; Improved genetic algorithm; Spectrum assignment; Spectrum Sensing
description: Recent advances in Cognitive Radio Networks (CRN) have elevated them to the status of a critical instrument for overcoming spectrum limits and achieving severe future wireless communication requirements. Collaborative spectrum sensing is presented for efficient channel selection because spectrum sensing is an essential part of CRNs. This study presents an innovative cooperative spectrum sensing (CSS) model that is built on the Firefly Algorithm (FA), as well as machine learning artificial neural networks (ANN). This system makes use of user grouping strategies to improve detection performance dramatically while lowering collaboration costs. Cooperative sensing wasn't used until after cognitive radio users had been correctly identified using energy data samples and an ANN model. Cooperative sensing strategies produce a user base that is either secure, requires less effort, or is faultless. The suggested method's purpose is to choose the best transmission channel. Clustering is utilized by the suggested ANN-FA model to reduce spectrum sensing inaccuracy. The transmission channel that has the highest weight is chosen by employing the method that has been provided for computing channel weight. The proposed ANN-FA model computes channel weight based on three sets of input parameters: PU utilization, CR count, and channel capacity. Using an improved evolutionary algorithm, the key principles of the ANN-FA scheme are optimized to boost the overall efficiency of the CRN channel selection technique. This study proposes the Artificial Neural Network with Firefly Algorithm (ANN-FA) for cognitive radio networks to overcome the obstacles. This proposed work focuses primarily on sensing the optimal secondary user channel and reducing the spectrum handoff delay in wireless networks. Several benchmark functions are utilized We analyze the efficacy of this innovative strategy by evaluating its performance. The performance of ANN-FA is 22.72 percent more robust and effective than tha…
creator: Velmurugan S.; Ezhumalai P.; Mary Anita E.A.
source: KSII Transactions on Internet and Information Systems, Vol-17, No. 7, pp. 1951-1975.
publisher: Korean Society for Internet Information
date: 2023-01-01
rights: All Open Access; Gold Open Access
relation: ISSN: 19767277
format: Online
language: English
type: Article
coverage: Velmurugan S., Department of Information Technology, R.M.D. Engineering College, Chennai, India; Ezhumalai P., Department of Computer Science, Engg. R.M.D. Engineering College, Chennai, India; Mary Anita E.A., Department of Computer Science and Engineering, School of Engineering and Technology, Christ University, Bengaluru, India