IMPROVED COOPERATIVE SPECTRUM SENSING MODEL BASED ON MACHINE LEARNING FOR COGNITIVE RADIO NETWORKS
With the development of wireless communication technology makes current spectral resources increasingly scarce. Cognitive radio (CR) technology was proposed by Joseph Mitola in 1999. It allows CR users access to the available frequencies of other authorized users under the condition of guaranteed service quality. Effective spectrum sensing technology is an important prerequisite to ensure CR users can make full use of free resources without creating interference for other users. However, detection performance is often compromised in practice by multipath fading, shadowing, and hidden primary users (PUs) problem. Cooperative spectrum sensing (CSS) takes advantage of spatial diversity and integrates CR sensing results from multiple geographic locations. This significantly improves detection performance.
This Matlab design presents a new machine learning (support vector machine (SVM))-based cooperative spectrum sensing (CSS) model, which utilizes 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 optimized 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 optimization model with the objective of minimizing the average correlation within subsets. All users are then divided into a specific number of optimized groups, only one of which is required for cooperative sensing in each time. The performances of the three algorithms were quantified using Matlab in terms of the average training time, classification speed and classification accuracy.
Reference Paper-1: Improved Cooperative Spectrum Sensing Model Based on Machine Learning for Cognitive Radio Networks
Author’s Name: Zan Li, Wen Wu1, Xiangli Liu, and Peihan Qi
Year:2018
Source: IET Journal
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