Sparse code multiple access (SCMA) has exhibited superiority in spectrum efficiency, which is particularly essential in the Internet of Things (IoT) system where a steadily increasing number of device connections are accommodated. However, the computational complexity of the conventional message passing algorithm (MPA) for the multiuser SCMA detection increases exponentially with the degree of resource nodes (RNs). To address this issue, two low complexity MPA schemes are proposed by utilizing the sparse feature of codewords. First, a sorted MPA (SMPA) detector is introduced to reduce the message exchanging from RNs to variable nodes (VNs) by dropping the redundant superposed constellation points outside a belief interval. Next, in order to further speed up the sorting process of the Euclidean distances between the received signal and codeword combinations, a deep neural network aided MPA (DNNMPA) is proposed, in which, the DNN behaves as a function approximator to generate the belief interval and operates in parallel with the initialization procedure before iterative message passing. Simulation results illustrate that the proposed SMPA and DNNMPA detectors significantly reduce the computational complexity of the conventional MPA one, but with comparable decoding capabilities, for the uplink SCMA system.

中文翻译：

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用于上行链路 SCMA 系统的深度神经网络辅助低复杂度 MPA 接收器

稀疏代码多址（SCMA）在频谱效率方面表现出优势，这在容纳设备连接数量稳步增加的物联网（IoT）系统中尤为重要。然而，用于多用户 SCMA 检测的传统消息传递算法 (MPA) 的计算复杂度随着资源节点 (RN) 的程度呈指数增长。为了解决这个问题，利用码字的稀疏特征提出了两种低复杂度的 MPA 方案。首先，引入了排序 MPA (SMPA) 检测器，通过丢弃置信区间之外的冗余叠加星座点来减少从 RN 到可变节点 (VN) 的消息交换。下一个，为了进一步加快对接收信号和码字组合之间欧几里德距离的排序过程，提出了一种深度神经网络辅助 MPA（DNNMPA），其中，DNN 表现为一个函数逼近器来生成置信区间并进行运算与迭代消息传递之前的初始化过程并行。仿真结果表明，对于上行链路 SCMA 系统，所提出的 SMPA 和 DNNMPA 检测器显着降低了传统 MPA 检测器的计算复杂度，但具有相当的解码能力。