Spectrum Mapping in Large-Scale Cognitive Radio Networks With Historical Spectrum Decision Results Learning

Abstract

To mitigate the contradiction between scarcity spectrum resources and heavily wireless services, cognitive radio (CR) has been proposed to improve spectrum utilization through allowing CR users to access licensed channels opportunistically. In large-scale networks, spectrum status is not the same at different locations due to the heterogeneity of CR network (CRN). To cope with such heterogeneity, some noncooperative spectrum sensing and distributed cooperative sensing algorithms were proposed. Such spectrum decision results will be exploited in this paper, in order to draw the spectrum map of the entire largescale CRN. The proposed spectrum mapping scheme contains three processing steps, and a boundary CR users searching algorithm using kennel function based supportive vector machine is adopted to improve the performance of the proposed scheme. The simulation results show that radial basis function kennel performs the best in the proposed scheme, and when accuracy threshold θα = 0.95 and filtration threshold θf = 0.02, the proposed scheme can draw a spectrum map with the accuracy 99.3% using only about 28% CR users performing spectrum sensing. Furthermore, the number of CR users and energy detection threshold have little effects on the performance of the proposed scheme.

Keywords

• Large-scale cognitive radio networks
• spectrum map
• historical spectrum decision results learning
• least squares support vector machine
• kennel function