基于MLP和PNN的调制盲分类
Blind Modulation Classification based on MLP and PNN
摘要：
在这项工作中，模式识别系统的数字调制通信信号的盲自动分类进行了研究。所提出的技术是能够辨别调制方案的类型，其最终用于解调，随后是信息抽取。该系统由两个子系统组成，即特征提取子系统（FESS）和分类器子系统（CSS）。FESS由连续小波变换（CWT）的特征生成和主成分分析（PCA）的特征子集，这是丰富的歧视性信息的选择。CSS使用所选择的特征来准确地对接收信号的调制类别进行分类。该技术使用概率神经网络（PNN）和多层感知器前向神经网络（MLPFN）的识别能力进行比较研究。PNN在分类精度以及测试和训练时间方面比MLPFN表现更好。所提出的方法是强大的相位偏移和加性高斯噪声的存在。
英文摘要：
In this work, a pattern recognition system is investigated for blind automatic classification of digitally modulated communication signals. The proposed technique is able to discriminate the type of modulation scheme which is eventually used for demodulation followed by information extraction. The proposed system is composed of two subsystems namely feature extraction sub-system (FESS) and classifier sub-system (CSS). The FESS consists of continuous wavelet transform (CWT) for feature generation and principal component analysis (PCA) for selection of the feature subset which is rich in discriminatory information. The CSS uses the selected features to accurately classify the modulation class of the received signal. The proposed technique uses probabilistic neural network (PNN) and multilayer perceptron forward neural network (MLPFN) for comparative study of their recognition ability. PNN have been found to perform better in terms of classification accuracy as well as testing and training time than MLPFN. The proposed approach is robust to presence of phase offset and additive Gaussian noise.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
