NDLI: Improved cooperative spectrum sensing model based on machine learning for cognitive radio networks

Content Provider	IET Digital Library
Author	Li, Zan Wu, Wen Liu, Xiangli Qi, Peihan
Abstract	This study presents a new machine learning (support vector machine (SVM))-based cooperative spectrum sensing (CSS) model, which utilises the methods of user grouping, to reduce cooperation overhead and effectively improve detection performance. Cognitive radio users were properly grouped before the cooperative sensing process using energy data samples and an SVM model. The resulting user group which participates in cooperative sensing procedures is safe, less redundant, or the optimised user group. Three grouping algorithms are presented in this study. The first grouping algorithm divides normal and abnormal users (malicious and severely fading users) into two groups. The second grouping algorithm distinguishes redundant and non-redundant users. The third grouping algorithm establishes an optimisation model with the objective of minimising average correlation within subsets. All users are then divided into a specific number of optimised groups, only one of which is required for cooperative sensing in each time. The performances of the three algorithms were quantified in terms of the average training time, classification speed and classification accuracy. Experimental results showed the proposed algorithms achieved their intended function and outperformed a conventional machine learning-based CSS model (proposed by Karaputugala et al.) in terms of security, energy consumption, and sensing efficiency.
Starting Page	2485
Ending Page	2492
Page Count	8
ISSN	17518628
Volume Number	12
e-ISSN	17518636
Issue Number	Issue 19, Dec (2018)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-com/12/19
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-com.2018.5245
Journal	IET Communications
Publisher Date	2018-09-03
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Abnormal User Cognitive Radio Cognitive Radio Network Cognitive Radio User Communication Computing Conventional Machine Learning-based CSS Model Cooperative Communication Improved Cooperative Spectrum Sensing Model Knowledge Engineering Technique Learning in AI Malicious User Nonredundant User Optimisation Model Optimised Groups Optimised User Group Radio Link And Equipment Sensing Efficiency Sensing Procedures Severely Fading User Support Vector Machine SVM Model Telecommunication Computing User Grouping
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering Computer Science Applications

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