基于深度神经网络的增强型SCMA检测器
An Enhanced SCMA Detector Enabled by Deep Neural Network
摘要：
通过对消息传递算法（MPA）的学习过程进行展开，提出了一种基于深度神经网络的稀疏码多址（SCMA）信号检测方法。如果将权值作为神经网络的参数，则MPA可以转化为稀疏连接的神经网络。神经网络可以离线训练，然后部署用于在线检测。通过进一步细化因子图边对应的网络权值，该方法实现了更好的性能。此外，基于深度神经网络的检测在计算上是高效的，因为在新兴的人工智能（AI）芯片中启用了网络中的高度可重构计算。
英文摘要：
In this paper, we propose a learning approach for sparse code multiple access (SCMA) signal detection by using a deep neural network via unfolding the procedure of message passing algorithm (MPA). The MPA can be converted to a sparsely connected neural network if we treat the weights as the parameters of a neural network. The neural network can be trained off-line and then deployed for online detection. By further refining the network weights corresponding to the edges of a factor graph, the proposed method achieves a better performance. Moreover, the deep neural network based detection is a computationally efficient since highly paralleled computations in the network are enabled in emerging Artificial Intelligence (AI) chips.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
