Abstract
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.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
