中文摘要：

频谱感知技是认知无线电中的关键技术。本文提出了利用SOM-SVM模型进行频谱分类的方法。SOMSVM模型是利用SOM的聚类特点,将含有相同特征的输入样本聚集在一起,并把离聚类中心较远的输入样本舍去。经过20%的样本压缩后,将含有代表性的小样本再送入SVM进行训练。本文的样本集通过实验平台采集,验证了基于支持向量机的频谱感知方法在实际数据测试条件下也能取得很好的感知性能。仿真结果表明,SOM-SVM模型在低信噪比下,频谱检测率接近100%,检测错误率也得到了很好的改善。

英文摘要：

The technology of spectrum sensing plays a key part in cognitive radio（CR）.This paper proposed the SOMSVM spectrum sensing model.This model takes the advantage of the clustering of SOM which can contain the same input samples together and decrease the input samples near by the center of the other clustering.SVM is trained by the representative training set after compressed by 20% in SOM.In this paper,sample sets are generated by laboratory instruments,verifying that the SVM-based spectrum sensing model also can obtain good performance under the condition of actual communication environment.The simulation results show that in low SNR the probability of detection is 100% and the probability of error detection is improved by SOM-SVM spectrum sensing model.