基于小波降噪的压缩感知—循环平稳检测技术

Cyclostationary Feature Detection Based on Compressed Sensing and Wavelet De-Noising

认知无线电技术通过次级用户动态接入空闲频谱来提高空闲频谱资源的利用效率,是认知无线电的重要环节。在低信噪比环境下,如何快速精确地进行频谱感知是频谱感知面临的重大挑战。提出了一种基于小波降噪的压缩感知—循环平稳特征检测器来实现低信噪比环境下的频谱检测。采用压缩感知技术提高了频谱感知的效率,并进一步利用小波变换技术降低了压缩感知过程中引入的压缩噪声,提高了低信噪比环境下的频谱感知准确度。仿真结果证明,提出的基于小波降噪的压缩感知技术能够实现低信噪比环境下的频谱空洞检测。

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.