基于高阶累积量特征学习的调制识别方法

Modulation recognition method based on high-order cumulant feature learning

自动调制分类是确保通信安全、可靠的关键技术之一。在低信噪比(low signal-to-noise ratio,Low-SNR)环境中,自动调制分类识别率低且识别类型受限。利用零均值高斯白噪声(white Gaussian noise,WGN)的高阶累积量理论值等于0的性质,在信号分析过程中,引入高阶累积量,可使系统免受WGN的影响。同时,引入深度学习网络结构完成微弱特征的表征,可有效解决调制方式受限及Low-SNR情况下的识别率下降问题。实验结果表明,所提方法在高斯信道环境下的分类精度比现有方法要高,在Low-SNR的不同信道环境下有较高的识别率,且使模型在时间、相位和频率偏移量方面具有更好的鲁棒性。

Automatic modulation classification is one of the key technologies to ensure communication security and reliability.In a low signal-to-noise ratio (Low-SNR) environment,the automatic modulation classification recognition rate is low and the recognition type is limited.By using the property that the high-order cumulant is equal to zero of zero-mean white Gaussian noise (WGN),the high-order cumulant is introduced to protect the system from WGN in the signal analysis process.Moreover,the deep learning network structure is introduced to complete the characterization of weak features,which can effectively solve the problem of the limited modulation method.And it can also solve the problem of low recognition rate under Low-SNR.The experimental results show that the classification accuracy of the proposed method is better than the existing methods in the Gaussian channel environment,and it has a higher recognition rate in different channel environments with Low-SNR.And,it makes the model’s time,phase and frequency offset more robust.