A Residual Learning-Aided Convolutional Autoencoder for SCMA

Sparse code multiple access (SCMA) is a code-domain non-orthogonal multiple access (NOMA) technology proposed to meet the access needs of large-scale intelligent terminal devices with high spectrum utilization. To improve the accuracy and computational complexity of SCMA to accommodate the internet of things (IoT) scenario, we design a new end-to-end autoencoder combining convolutional neural networks (CNNs) and residual networks. A residual network with multitask learning improves the decoding accuracy, and CNN units are used for SCMA codeword mapping, with sparse connectivity and weight-sharing to reduce the number of trainable parameters. Simulations show that this scheme outperforms existing autoencoder schemes in bit error rate (BER) and computational complexity.

Automated summary

Sparse code multiple access (SCMA) is a technology proposed for large-scale intelligent terminal devices with high spectrum utilization. In this study, we design a new end-to-end autoencoder combining convolutional neural networks (CNNs) and residual networks to improve the accuracy and computational complexity of SCMA for the internet of things (IoT) scenario. Our scheme, with a residual network utilizing multitask learning and CNN units for SCMA codeword mapping, outperforms existing autoencoder schemes in terms of bit error rate (BER) and computational complexity according to simulations.