Network Selection for Cognitive Radio Based on Fuzzy Learning

Abstract

Cognitive radio is a promising approach to ensuring the coexistence of heterogeneous wireless networks since it can perceive wireless conditions and freely switch among different network modes. When there are many network opportunities, how to decide the appropriate network selection for CR user's current service is the main problem we study in this paper. We make full use of the intelligent characteristic of CR user and propose a fuzzy learning based network selection scheme, in which the network selection choice is made based on the estimated evaluations of available networks. Multiple factors are considered when estimating these evaluations. Both the outer environment factors directly sensed by CR user (signal strength of the available network and network mode), and also the factor that cannot be determined beforehand and is learnt by our scheme (the bandwidth allocated by the optional network) are considered. From several interactions with the wireless environment, the experience of network selection behavior is accumulated which will direct our scheme to make a proper decision of the network. Two simulations verify that our scheme could not only guarantee a better bandwidth requirement of CR user compared with other three network selection methods, but also shows it to be a reasonable scheme for utilizing the available resources of these networks.