中文摘要：

认知无线电系统中，压缩感知理论已广泛运用于宽带频谱检测。但是，压缩感知中的重构问题造成频谱检测算法计算复杂度高，且在低信噪比下检测效果不佳。本文提出了采用支持向量机的宽带频谱感知算法，该算法利用支持向量机建立频谱检测分类器，代替信号的重构与检测过程。根据系统对实时性的要求，分别设计了多级二元分类器感知算法和单级多元分类器感知算法。前者适用于分级数有限且实时性要求不高的场景，后者可大幅降低系统的算法复杂度，降低感知时间，适用于实时检测系统。仿真结果表明，与基于重构的能量检测算法相比，本文提出的两种算法均可以有效改善系统对噪声的鲁棒性，提高在较小信噪比下的检测性能。

英文摘要：

Compressed sensing （CS）has been extensively used for spectrum sensing in cognitive radio （CR）system. However,in these CS-based spectrum sensing schemes,the reconstruction algorithms of compressed sensing cause high computational cost and bad detection performance in the low SNR.This paper proposes two compressed wideband spectrum sensing algorithms,which utilize Support Vector Machines （SVM）to establish spectrum detection classifiers instead of sig-nal reconstruction and decision-making.The single-level multi-classifier sensing algorithm and the multi-level binary classi-fier sensing algorithm are designed respectively to meet the different requirements of real-time applications.The multi-level binary classifier sensing algorithm is suitable for scenarios with low real-time requirements which contain few spectrum channels.The single-level multi-classifier sensing algorithm with reduced computational complexity,is optimized for real-time detection systems.Simulation results show that the proposed algorithms outperform the classical energy detection algo-rithm based on CS.The proposed algorithms present a better robustness performance to noise and enhance the performance in the case of low SNR.