CWGAN-Based Channel Modeling of Convolutional Autoencoder-Aided SCMA for Satellite-Terrestrial Communication

Sparse code multiple access (SCMA) has excellent application prospects in satellite-terrestrial links because of its high spectral efficiency and access capacity. In the end-to-end SCMA systems, channel modeling is a fundamental task for the communication algorithm design and performance optimization, which however is very challenging as it requires in-depth domain knowledge and technical expertise in radio signal propagations, especially for modeling satellite-terrestrial fading channels. In this article, a convolutional autoencoder-aided SCMA paradigm based on the stochastic channel modeling and autoencoder structure is developed. We are the first to exploit generative adversarial network to represent the satellite-terrestrial fading channel effects for the convolutional autoencoder-aided SCMA. Specifically, convolutional neural networks (CNNs) are employed to jointly construct the encoder and decoder for SCMA to alleviate the curse of dimensionality. Furthermore, we propose a conditional Wasserstein generative adversarial network with the gradient penalty (CWGAN-GP)-based channel modeling approach to achieve approximately accurate conditional channel distribution. Particularly, the received signal corresponding to the pilot symbol is used as a part of the condition information, and the Wasserstein distance is used as a measure of the distance between the distributions. Gradient penalty is adopted to solve the problem of weight pruning forcing Lipschitz constraints, which leads to some data being unable to converge. The numerical results demonstrate the effectiveness of the proposed approach in terms of the bit error rate (BER), block error rate (BLER), and complexity in satellite-terrestrial fading channels.