一种小波特征与深度神经网络结合的信号制式识别算法

针对当前通信信号的制式识别算法在低信噪比情况下识别不准确的问题,提出一种新的小波特征与改进的深度神经网络结合(WL-DNN)的识别算法。该算法将生成的10种{2ASK、4ASK、2PSK、4PSK、2FSK、4FSK、OFDM、16QAM、AM、FM}含有高斯白噪声的通信信号,用小波分解重构算法提取出一类新的小波特征参数。本文测试了含有多层隐含层的改进BP神经网络作为分类器,利用弹性反向传播算法训练神经网络的参数,确定神经网络的最优超参数。仿真结果表明:在信噪比低至0 dB的情况下,单个调制信号最低识别率超过95%,平均识别率超过98%,大幅提高了制式识别在低信噪比下的识别率,由此表明了该算法的有效性和正确性。

A signal modulation recognition method based on wavelet feature and depth neural network

Aiming at the problem of inaccurate recognition of current communication signals in low signal-to-noise ratio (SNR), a recognition algorithm combining wavelet feature and depth neural network is proposed. This method generates 10 kinds of common communication signals with Gauss white noise{MASK, MPSK, MFSK, OFDM, 16QAM, AM, FM}. A new kind of wavelet characteristic parameters are extracted from the signals by using the wavelet decomposition and reconstruction algorithm. The improved BP neural network with plenty hidden layers is studied and tested as classifier. The parameters of the neural network are trained by the elastic back propagation algorithm. The optimal layers of the neural network are determined by the identification results. The simulation results show that the minimum recognition rate of single modulated signals is more than 95% and the average recognition rate is more than 98%, when the signal-to-noise ratio is as low as 0 dB, which greatly improves the recognition rate of standard recognition under low signal-to-noise ratio, thus proving the effectiveness and practicability of this method.