Cyclostationary Feature Detection Based on Compressed Sensing and Wavelet De-Noising - Haifeng Tan; Jun Lu; Xuan Fu; Qixun Zhang (Dianxin kexue)
To improve the vacant spectrum utilization，ultra-wideband spectrum sensing is critical for cognitive radio （CR）as it enables secondary users to dynamically access the unoccupied spectrum bands.However，the fast and accurate spectrum sensing is still a challenge over an ultra-wide bandwidth in low signal to noise ratio（SNR） environment.A compressed sensing （CS）-feature detector based on wavelet de-noising was proposed to perform wideband detection in low SNR.CS was proposed to improve the efficiency of wideband spectrum sensing.And two dimensional wavelet transform was introduced to deal with the noise in spectral coherence function（SCF）by the CS process.As a result，the detection accuracy in low SNR was improved.It is found that the proposed technology can detect spectrum holes at a range of low SNR through simulation results.