基于DropBlock双模态混合神经网络的无线通信调制识别

DropBlock based bimodal hybrid neural network for wireless communication modulation recognition

自动调制识别作为信号检测和解调的中间步骤,在无线通信系统中起着至关重要的作用。针对现有自动调制识别方法识别精度低的问题,提出了一种双模态混合神经网络(bimodal hybrid neural network,BHNN),该网络利用多个模态中包含的互补增益信息来丰富特征维度。将改进的残差网络与双向门控循环单元并行连接,构建双模态混合神经网络模型,分别提取信号的空间特征与时序特征。引入DropBlock正则化算法,有效抑制网络训练过程中过拟合、梯度消失和梯度爆炸等对识别精度的影响。以双模态数据输入,充分利用信号的空间与时序特征,通过并行连接减少网络深度,加速模型收敛,提高调制信号的识别精度。为验证模型的有效性,采用两种公开数据集对模型进行仿真实验,结果表明,BHNN在两种数据集上识别精度高、稳定性强,在高信噪比下识别精度分别可达89%和93.63%。

As an intermediate step of signal detection and demodulation, automatic modulation recognition played a momentous role in wireless communication system. Aiming at the low recognition accuracy of existing automatic modulation recognition methods, a bimodal hybrid neural network(BHNN) was proposed, which utilized complementary gain information contained in multiple modes to enrich feature dimensions. The improved residual network was connected in parallel with the bidirectional gated loop unit to construct a bimodal hybrid neural network model,and the spatial and temporal features of the signal were extracted respectively. The DropBlock regularization algorithm was introduced to effectively suppress the influence of over fitting, gradient disappearance and gradient explosion on the recognition accuracy in the process of network training. Using bimodal data input, the spatial and temporal characteristics of signals were fully utilized, and the network depth was reduced through parallel connection.The model convergence was accelerated, and the recognition accuracy of modulated signals was improved. In order to verify the effectiveness of the model, two public datasets were used to simulate the model. The results show that BHNN has high recognition accuracy and strong stability on the two datasets, and the recognition accuracy can reach 89% and 93.63% respectively under high signal-to-noise ratio.