Studies on spectrum sensing algorithms and real-time implementation for cognitive radio

Abstract

In the recent past Cognitive Radio (CR) technology has received increased at- tention to solve the spectrum scarcity problem using the opportunistic spectrum re-usage technique. It allows secondary users to use the unlicensed channels oppor- tunistically without causing interference to the licensed users. It involves mainly two functionalities namely spectrum sensing and spectrum management. Spec- trum sensing identifies vacant spectrum bands, i.e., spectrum holes for the op- portunistic use of secondary users in the network. Robust spectrum sensing is essential to avoid interference to existing ’licensed’ users and maximize the spec- trum utilization. There exist mainly two class of sensing algorithms: data aided and blind. The data aided algorithm requires the characteristics of the signal a pri- ori for successful detection. However, in the real-time radio environment, it is not always possible to know the signal characteristics a priori. Hence blind algorithms are essential for reliable spectrum sensing in the real-time environment. In the sensing domain, although energy detection is popularly being used due to its low computational complexity, its performance suffers due to noise uncer- tainty. Although it is a blind technique, but it requires information about the noise variance. The estimation accuracy of received signal noise variance affects the de- tection performance. This thesis proposes an improved energy detection technique where the threshold is adjusted with respect to the noise variance. The noise vari- ance is estimated using the Linear predictor method, i.e., the Burg method. This thesis performs a detailed performance analysis of the energy detection algorithm with noise variance estimation in the case of a single node and multi-node sensing under white and colored noise characteristics of the channel. Since the accuracy of sensing depends on the accuracy of the noise variance estimator, a detailed study of different types of noise estimator and its impact on sensing accuracy is also carried out.