论文标题
基于深度学习的多源检测NOMA
Deep-Learning based Multiuser Detection for NOMA
论文作者
论文摘要
在本文中，我们研究了基于Welch Bound Equality扩散多个访问（WSMA）的深度学习对多链多链路检测（MUD）的应用。几个不合作用户，每个用户都有自己的签名Noma签名序列（SS），它们通过相同的资源传输。这些SS之间的相关性较低，并有助于在泥浆期间在接收器上分离用户。泥浆涉及几个子任务，例如平衡，组合，切片，信号重建和干扰取消。本文考虑的神经网络（NN）用单个黑匣子替换了这些定义明确的接收器块，即NN为这些模块提供了一次镜头近似。我们考虑两种不同的监督前馈NN实现，即深入NN和2D-Convolutional NN。将这两个NN的性能与常规接收器进行了比较。仿真结果表明，通过正确选择NN参数，与传统的泥浆方案相比，黑匣子近似可以提供更快，更高的性能，并且与基于复杂的最大值基于基于的最大可能性探测器获得的最终符号错误率几乎相同。
In this paper, we study an application of deep learning to uplink multiuser detection (MUD) for non-orthogonal multiple access (NOMA) scheme based on Welch bound equality spread multiple access (WSMA). Several non-cooperating users, each with its own preassigned NOMA signature sequence (SS), transmit over the same resource. These SSs have low correlation among them and aid in the user separation at the receiver during MUD. Several subtasks such as equalizing, combining, slicing, signal reconstruction and interference cancellation are involved in MUD. The neural network (NN) considered in this paper replaces these well-defined receiver blocks with a single black box, i.e., the NN provides a one-shot approximation for these modules. We consider two different supervised feed-forward NN implementations, namely, a deep NN and a 2D-Convolutional NN, for MUD. Performance of these two NNs is compared with the conventional receivers. Simulation results show that by proper selection of the NN parameters, it is possible for the black box approximation to provide faster and better performance, compared to conventional MUD schemes, and it achieves almost the same symbol error rate as the ultimate one obtained by the complex maximum likelihood-based detectors.
