论文标题
CNN使用波束形成的CSI测量的5G mMWave定位方法
A CNN Approach for 5G mmWave Positioning Using Beamformed CSI Measurements
论文作者
论文摘要
人工智能（AI）的出现影响了人类生活的各个方面。在无线电定位中可以看到AI影响的具体示例之一。在本文中，我们首次使用由光束形成通道状态信息（CSI）组成的5G新无线电（NR）指纹训练卷积神经网络（CNN）。通过观察CSI，可以表征发射器和接收器之间的多径通道，从而提供良好的时空数据来源，以找到用户设备（UE）的位置。我们从市区收集基于射线追踪的5G NR CSI。来自一个基站（BS）的信号的CSI数据是在具有已知位置训练CNN的参考点处收集的。我们通过测试来评估我们的工作：a）受过训练的网络的鲁棒性，用于估计在同一参考点上的新测量位置和b）基于CNN的位置估计的准确性，而UE则在参考点以外的其他点上。结果证明，我们针对特定城市环境的训练有素的网络可以以最小平均误差为0.98 m估算UE位置。
The advent of Artificial Intelligence (AI) has impacted all aspects of human life. One of the concrete examples of AI impact is visible in radio positioning. In this article, for the first time we utilize the power of AI by training a Convolutional Neural Network (CNN) using 5G New Radio (NR) fingerprints consisting of beamformed Channel State Information (CSI). By observing CSI, it is possible to characterize the multipath channel between the transmitter and the receiver, and thus provide a good source of spatiotemporal data to find the position of a User Equipment (UE). We collect ray-tracing-based 5G NR CSI from an urban area. The CSI data of the signals from one Base Station (BS) is collected at the reference points with known positions to train a CNN. We evaluate our work by testing: a) the robustness of the trained network for estimating the positions for the new measurements on the same reference points and b) the accuracy of the CNN-based position estimation while the UE is on points other than the reference points. The results prove that our trained network for a specific urban environment can estimate the UE position with a minimum mean error of 0.98 m.
