基于随机森林的认知网络主用户信号调制类型识别算法

针对低信噪比情况下主用户信号调制类型识别率低的问题，提出了一种使用信号循环谱中特征参数作为样本参数的基于随机森林的认知网络信号类型识别算法，通过使用训练完成的随机森林对主用户信号类型识别，有效抑制了采用ANN和SVM进行识别所造成的误差影响，提高了低信噪比下信号识别的精确度，实现了不同调制类型信号的有效检测与识别.实验结果表明:所提出的算法有较高的主用户信号调制类型识别精度，进而验证了算法的有效性.

Primary User Signal Recognition Algorithm based on Random Forest in Cognitive Network

A novel approach to signal recognition based on random forests， which uses signal cyclic spectrum feature parameters as sample parameters， was introduced to solve the problem of the low accuracy of the primary user signal type identification in low signal-to-noise ratio(SNR). By utilizing the proposed algorithm， the detecting signal types were identified by the trained random forests. The errors using artificial neural network(ANN)and support vector machine(SVM) were restrained. The accuracy of signal type identification was improved in low SNR and effective signal detection and recognition was achieved to different modulated signal. Simulations showed the validity and superiority of the proposed algorithm.