基于生成对抗与卷积神经网络的调制识别方法

自动调制识别在频谱监测和认知无线电中占有重要地位。针对现有调制识别算法在低信噪比条件下识别率低的问题, 提出一种基于生成对抗网络(generative adversarial network, GAN)和卷积神经网络(convolutional neural network, CNN)的数字信号调制识别方法。在利用平滑伪Wigner-Ville分布将调制信号转换为时频图像(time-frequency images, TFIs)后, 在经典GAN中嵌入了剩余密集块(residual dense block, RDB)结构, 保证了对TFIs的去噪和修复。通过对经典的剩余网络(residual network, ResNet)模型微调, 满足了TFIs的识别与分类。仿真结果表明, 所提方法在低信噪比情况下有效地降低了噪声对TFIs的干扰, 提高了识别性能。

Modulation recognition method based on generative adversarial and convolutional neural network

Automatic modulation recognition occupies an important position in spectrum monitoring and cognitive radio. Aiming at the low recognition rate problem of existing modulation recognition algorithms under the condition of low signal to noise ratio, a digital signal modulation recognition method combined generative adversarial network(GAN) and convolutional neural network(CNN). After using the smooth pseudo Wigner-Ville distribution to convert the modulated signal into time-frequency images(TFIs), the residual dense block(RDB) structure is embeded in the classic GAN network to guarantees the denosing and repairmen of TFIs. By fine-tuning the classic residual networkl(ResNet) model of CNN network, the recognition and classification of TFIs is satisfied. The simulation results show that the proposed method effectively reduces the interference of noise on TFIs and improves the recognition performance under the condition of low signal to noise ratio.