Currently, sparse code multiple access (SCMA) is a commonly used multiple-access technique, and it is a strong candidate for implementation as part of the fifth generation (5G) of wireless mobile communications. Although several design methods are available for SCMA codebooks, we propose a new method that optimizes point-to-point distances within the same codeword and from codebook-to-codebook for the same carrier based on singular value decomposition (SVD). A neural network-based receiver is proposed for detecting and decoding SVD–SCMA codewords. The simulation results show an improvement in the bit error rate (BER) compared to that for methods such as low-density signatures (LDS), SCMA, and multidimensional SCMA (MD-SCMA).

中文翻译：

---

使用奇异值分解的稀疏代码多访问代码簿设计

目前，稀疏码多址接入 (SCMA) 是一种常用的多址接入技术，是第五代 (5G) 无线移动通信的一部分实施的有力候选者。尽管有几种设计方法可用于 SCMA 码本，但我们提出了一种新方法，该方法基于奇异值分解 (SVD) 优化同一码字内以及同一载波的码本到码本的点对点距离。提出了一种基于神经网络的接收器来检测和解码 SVD-SCMA 码字。仿真结果表明，与低密度签名 (LDS)、SCMA 和多维 SCMA (MD-SCMA) 等方法相比，误码率 (BER) 有所提高。