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Curvelet based signal detection for spectrum sensing using Principal Component of Analysis

Spectrum sensing is the ability to apprise and be aware of the parameters correlated to the radio channel. This paper, proposes a novel feature detection method based on curvelet transform for spectrum sensing. Initially, the received signal is processed by Curvelet transform and then the test statistics are obtained by examining the feature of the signal in the new domain that is compared with the threshold to decide the presence of the useful signal. To make the detection process faster Principal Component of Analysis (PCA) is used to determine an optimal feature set. The cardinal aim of this paper is to introduce a novel approach to improve the performance of signal detection which has much better performance than the spectral sensing detection methods. Simulation results shows that the proposed method significantly enhance the performance in terms of efficiency and accuracy as well as detect weak signals at very low signal-to-noise rate (SNR) and then animatedly adjust the radio operating parameters accordingly. In addition, the proposed method is low in complexity, which makes it typically suitable for real time spectrum sensing in cognitive radio system.