论文标题
下行链路SCMA网络的新型多任务学习授权代码本设计
A Novel Multi-Task Learning Empowered Codebook Design for Downlink SCMA Networks
论文作者
论文摘要
稀疏代码多重访问（SCMA）是一种有希望的代码域非正交多访问（NOMA）方案，用于实现大规模的机器类型通信。在SCMA中，在过去的几年中，良好的稀疏代码书和有效的多源解码的设计引起了极大的研究关注。本文旨在利用深度学习，共同设计借助自动编码器的下行链接SCMA编码器和解码器。我们介绍了一种基于端到端学习的新颖新颖的SCMA（E2E-SCMA）设计框架，根据该框架，可获得改进的稀疏代码书和低复杂的解码器。与常规的SCMA方案相比，我们的数值结果表明，所提出的E2E-SCMA导致错误率和计算复杂性方面的显着提高。
Sparse code multiple access (SCMA) is a promising code-domain non-orthogonal multiple access (NOMA) scheme for the enabling of massive machine-type communication. In SCMA, the design of good sparse codebooks and efficient multiuser decoding have attracted tremendous research attention in the past few years. This paper aims to leverage deep learning to jointly design the downlink SCMA encoder and decoder with the aid of autoencoder. We introduce a novel end-to-end learning based SCMA (E2E-SCMA) design framework, under which improved sparse codebooks and low-complexity decoder are obtained. Compared to conventional SCMA schemes, our numerical results show that the proposed E2E-SCMA leads to significant improvements in terms of error rate and computational complexity.
