Abstract
Background: Cognitive radio can be described as a radio system where it familiarizes to the situations of the environs by some procedure like analyzing, observing and learning. Cognitive radio can basically recycle the unused white space in the license spectrum through sensing the network thus the method can achieve the maximum implementation of the radio bandwidth.
Objective: The motivation of this research is to improve the performance of spectrum sensing of a group of users in every cluster where various channel environment will be presented in comparison with the conventional weighted sensing. Methods: With the intention of lessening the fading and shadowing, co-operative detection is favored over independent detection. Clustered based cooperation has been employed to expand the sensing enactment moderately. Sensing of co-operative spectrum which is weighted has been used to boost the cluster cooperation scheme. To crack the critical problem this research proposed a weighted cluster based co-operative sensing in which the weight calculation is performed at FC reducing all available redundancies. Results: From the simulation it is found that our proposed algorithm has better probability of detection (i.e, at -6 dB of SNR, clustered, Weighted Clustered and Novel Weighted Clustered probability of detections are 67%,84% and 95% respectively). Even if Novel Weighted Clustered has the lower False Alarm probability. Conclusion: Proposed novel weighted co-operative spectrum sensing algorithm for cognitive radio network spectrum sensing scheme can attain better performance under different fading environments.Keywords: Local spectrum sensing, non-co-operative spectrum sensing, co-operative spectrum sensing (CSS), clustered CSS, weighted clustered CSS, novel weighted clustered, SNR.
Graphical Abstract
Related Journals
Recent Advances in Computer Science and Communications
Related Books
Challenges and Opportunities for Deep Learning Applications in Industry 4.0
Recent Advances in IoT and Blockchain Technology
Augmented Intelligence: Deep Learning, Machine Learning, Cognitive Computing, Educational Data Mining
Human-Computer Interaction and Beyond: Advances Towards Smart and Interconnected Environments Part II
Machine Learning and Its Application: A Quick Guide for Beginners
Computational Intelligence For Data Analysis
Applied Machine Learning and Multi-Criteria Decision-Making in Healthcare
Advanced Computing Techniques: Implementation, Informatics and Emerging Technologies
How to Design Optimization Algorithms by Applying Natural Behavioral Patterns
Human-Computer Interaction and Beyond: Advances Towards Smart and Interconnected Environments (Part I)
6