The 5G and beyond wireless networks will be more dynamic and heterogeneous, which needs to work on multistrand waveforms. One of the most significant challenges in such a dynamic network, especially non cooperated cases, is the identification of particular modulation type, which the transmitter uses at the given time to decode the data successfully. This research proposes a modulation classification algorithm using the combination architectures of modified convolutional neural network. The proposed deep learning architecture is developed by combining the convolutional neural network, dense network, and long short-term memory network (LSTM), which is named as convolutional LSTM dense neural network (CLDNN). Moreover, the mean cumulative sum metric (MCS) is introduced in the pooling layer for improved classification accuracy. Dimensionality reduction through Principal Component Analysis is also applied to minimize the training time, so that the proposed architecture can be adopted for its practical usage. The simulation results prove that the presented CLDNN outperforms an ordinary CNN, while taking less training time.

5G及以后的无线网络将更加动态和异构，需要在多股波形上工作。在这样的动态网络中，特别是在非协作情况下，最重大的挑战之一是特定调制类型的标识，发射机在给定时间使用它来成功解码数据。该研究提出了一种使用改进的卷积神经网络的组合架构的调制分类算法。所提出的深度学习架构是通过将卷积神经网络，密集网络和长短期记忆网络（LSTM）相结合而开发的，它被称为卷积LSTM密集神经网络（CLDNN）。此外，在池化层中引入了平均累积和度量（MCS），以提高分类精度。通过主成分分析进行降维也可以最大程度地减少训练时间，因此可以将所建议的体系结构用于实际应用。仿真结果表明，本文提出的CLDNN优于普通的CNN，所需的训练时间更少。