S-transformation based integrated approach for spectrum estimation, storage, and sensing in cognitive radio

Abstract

Cognitive Radio (CR) uses the principle of dynamic spectrum allocation to improve the utilization of spectrum bands. The estimation of missing data is essential for maintaining an uninterrupted quality of service in the CR. However, the existing methods are not suitable for interpolating missing data in high frequency signals. The storage of spectrum occupancy information is crucial for learning the spectrum usage and prediction. The existing techniques for wideband spectrum sensing suffer from poor edge detection capabilities. This paper proposes an S-Transformation (ST) based approach to solve these problems. For missing samples, the proposed method improves the accuracy of estimation. The ST can also be used to store the spectrum occupancy information. The simulation results show that the proposed scheme outperforms others by improving the accuracy of edge detection. Further, the simple implementation of the ST in the frequency domain is an advantage for the real time application.