脉冲噪声环境下的调制识别算法  

Modulation Recognition Algorithm in Impulsive Noise Environments

作　　者：李欣然 李春国[1] 明钰婷 潘怡瑾 LI Xinran;LI Chunguo;MING Yuting;PAN Yijin(School of Information Science and Engineering,Southeast University,Nanjing,Jiangsu 211111,China;College of Electronic Engineering,National University of Defense Technology,Hefei,Anhui 230037,China)

机构地区：[1]东南大学信息科学与工程学院,江苏南京211111 [2]国防科技大学电子对抗学院,安徽合肥230037

出　　处：《信号处理》2024年第8期1497-1507,共11页Journal of Signal Processing

基　　金：重庆自然科学基金联合基金项目(CSTB2023NSCQ-LZX0121)。

摘　　要：自适应调制可以根据信道的变化动态地调整调制方式,从而最大化利用信道传输能力。调制识别是在自适应调制后在接收端判别出调制方式,并且为解调器正确选择解调算法的关键步骤。传统调制识别算法大多适用于加性高斯白噪声信道场景,然而在很多通信场景中除了高斯噪声外,还存在具备脉冲特性的非高斯噪声,目前最常用的噪声模型包括米德尔顿A类(Middleton Class A)噪声模型,对称α稳定分布(symmetricαstable,SαS)噪声模型,高斯混合模型,其中最典型的米德尔顿A类噪声模型。过往研究成果在遇到脉冲噪声条件下,通常较难实现有效调制识别,因此本文将米德尔顿A类噪声系统中的调制识别方案作为主要研究方向。考虑到这些脉冲噪声的概率密度函数较为复杂,本文提出了一种依靠卷积神经网络实现调制识别的算法。为了充分提取信号的调制特征,本文采用双层卷积块结构,每个卷积块内部包含卷积层、归一化层、激励层和池化层四部分,经过两次卷积的结果送入全连接层和Softmax层,最终得到调制模式的概率值,进而有效地识别出发送端的调制方式。在实际仿真过程中,随着训练的进行,接收端准确率逐步提升。通过观察BPSK、QPSK、8PSK、16QAM这四种调制方案在不同信噪比下的识别准确率,发现所提出的方案的调制识别准确性始终优于单个卷积块网络,在1 dB左右的SNR的条件下便可以达到接近100%的训练准确率。实验结果表明,此方案的识别准确率优于其他识别方案,且在另外两种典型噪声模型条件下仍具有较高识别准确,充分验证了算法的有效性和实用价值。Adaptive modulation dynamically adjusts the modulation mode according to the variations in channels to maximize the transmission capacity of channels.Modulation recognition is the key step to identifying the modulation mode and selecting the demodulation algorithm for the demodulator after adaptive modulation.Under a low signal-tonoise(SNR),the recognition capability of multicarrier composite modulation signals must be enhanced.Traditional modulation recognition algorithms are mostly based on additive Gaussian white noise(AWGN)channel scenarios.However,in many communication scenarios,in addition to Gaussian noise,non-Gaussian noise with pulse characteristics occurs.Currently,the most commonly used noise models are Middleton Class A noise,symmetric stable(symmetricαstable,SαS)noise,and Gaussian mixture models,among which the Middleton Class A noise model is the most typical.Existing literature shows that achieving effective modulation recognition under the presence of pulse noise is often difficult.Therefore,this paper primarily studies a modulation recognition scheme for Middleton Class A noise systems.Considering the complex probability density function(PDF)of these types of impulse noise,a modulation recognition algorithm based on a convolutional neural network is proposed.To fully extract the modulation characteristics of signal transmission,this paper adopts a double-layer convolution block structure.Each convolutional block contains four parts:convolutional,normalization,activation,and pooling layers.After two convolutional blocks,the results are sent to a fully connected layer before passing a Softmax layer.Finally,the modulation modes of transmit signals are obtained.In an actual simulation process,95%of the data samples were selected as the test set,and the remaining samples were used as the training set.A total of 5000 frames were used in the experiment,each consisting of 200 complex signals.The signal in each frame adopts the same modulation mode,whereas different frames adopt different modulation mod

关 键 词：调制识别 脉冲噪声 米德尔顿A类噪声 卷积神经网络 

分 类 号：TN914[电子电信—通信与信息系统]