Applications of Independent Component Analysis in Cognitive Radio Sensor Networks

Abstract

Independent component analysis is extensively used for blind source separation of different signals in various engineering disciplines. It has its applications in several areas of communication, multiple input multiple output, orthogonal frequency division multiplexing, wireless sensor networks, and cognitive radio networks. In this chapter, the authors discuss the general theory of independent component analysis, wireless sensor networks, cognitive radio networks, and cognitive radio sensor networks. The main focus of the chapter is the application of independent component analysis in cognitive radio networks, wireless sensor networks, and cognitive radio sensor networks. The issues and challenges of these emerging technologies are discussed while applying independent component analysis. Cognitive radio sensor network is a promising technology to efficiently resolve the issues of spectrum usage in sensor networks. The authors are the first to discuss the applications of independent component analysis in cognitive radio sensor networks. At the end of this chapter, they discuss some future research problems regarding the applications of independent component analysis in cognitive radio sensor networks.