论文标题
上行链路NOMA中基于深度学习的检测器的试验间隔减少
Pilot Interval Reduction by Deep Learning Based Detectors in Uplink NOMA
论文作者
论文摘要
与正交多重访问（OMA）技术相比，非正交多访问（NOMA）具有更高的光谱效率。在接收器未知的通道上行通信系统中，从不同时间间隔发送的每个用户的飞行员信号降低了NOMA的频谱效率。在这项研究中，在上行链路通信系统中，已经研究了基于DL深度学习的检测器，这些检测器已知响应从基站的用户发送的飞行员信号。它的目的是通过向用户发送单个飞行员来维持NOMA的光谱效率，从而减少DL探测器的时间间隔。
Non-Orthogonal Multiple Access (NOMA) has higher spectral efficiency than orthogonal multiple access (OMA) techniques. In uplink communication systems that the channel is not known at the receiver, pilot signals sent from each user in different time intervals have reduced the spectral efficiency of NOMA. In this study, in the uplink communication system, DL-deep learning based detectors which are known to respond to the pilot signals sent from the users at the base station have been researched. It is aimed to maintain the spectral efficiency of NOMA by sending a single pilot from users, thus reducing the time interval in the DL detectors.
