史文娟, 冯全源. 一种改进的基于支持向量机的OFDM识别算法[J]. 微电子学与计算机, 2014, 31(10): 98-102.
引用本文: 史文娟, 冯全源. 一种改进的基于支持向量机的OFDM识别算法[J]. 微电子学与计算机, 2014, 31(10): 98-102.
SHI Wen-juan, FENG Quan-yuan. Improved Automatic Modulation Recognition Algorithm for OFDM Based on SVM[J]. Microelectronics & Computer, 2014, 31(10): 98-102.
Citation: SHI Wen-juan, FENG Quan-yuan. Improved Automatic Modulation Recognition Algorithm for OFDM Based on SVM[J]. Microelectronics & Computer, 2014, 31(10): 98-102.

一种改进的基于支持向量机的OFDM识别算法

Improved Automatic Modulation Recognition Algorithm for OFDM Based on SVM

  • 摘要: 针对OFDM信号与单载波信号调制识别,提出了一种基于高阶累积量特征的改进方法.通过分析复信号幅值的归一化四阶累积量特性,以及信号的瞬时频率和功率谱特征,改进和提出新的特征参数,采用支持向量机分类器,实现了AWGN信道下包括OFDM在内的9种信号的制式自动识别.该方法具有特征参数易于提取、抗噪性好、识别准确率高的优点.利用MATLAB仿真证明在信噪比不小于7dB的情况下,OFDM信号的识别准确率达99%.

     

    Abstract: Aiming at the automatic modulation recognition of OFDM signal and Single carrier signals,this paper proposes a improved method based on high-order cumulants.By analysing the normalized fourth-order cumulant features of the complex signal amplitude,the characteristics of instantaneous frequency and power spectrum,new feature parameters are proposed.With support vector machine (SVM) classifier,the automatic identification of 9kinds of signals including OFDM in the AWGN channel is achieved.The method has lots of advantages,such as the feature parameters could be extracted easily and have good anti-noise performance,and recognition accuracy rates are also very high.When SNR is not less than 7dB,the simulation results on MATLAB indicate that identification probability of this proposed method is 99%.

     

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