CNN-Based End-to-End Deeper Autoencoders for Physical Layer of Wireless Communication System

In this paper, a deeper autoencoder is proposed, which is composed of systematic and strategic convolutional neural network (CNN) layers. The proposed autoencoder can function under various conditions of wireless communication sys-tems, such as AWGN, Rayleigh, and other fading environments. This model with perfect channel state information is compatible with different channel use, data rates, throughput, and variations in block length. The proposed system is an end-to-end task that can optimize the transceiver's components jointly and inherit the sophisticated features of convolutional neural networks, such as fast training convergence, generalization, classification, and feature learning. In addition, simulation results illustrate that the proposed model outperforms the existing models in terms of block error rate. Another autoencoder is proposed in this paper, where VGG-16 architecture is implemented before feeding to the convolutional layer at the decoder side. A comparison of block error rate performance shows that this scheme performs better than the human expertise models.