论文标题
基于在线学习的NLOS在5G定位中缓解错误的错误
Online Learning Based NLOS Ranging Error Mitigation in 5G Positioning
论文作者
论文摘要
第五代（5G）无线通信由于其较大的带宽和低成本而对定位很有用。但是，阻断设备之间的视线（LOS）路径的障碍物的存在将严重影响定位精度。在本文中，我们提出了一种在线学习方法，以直接在非视线（NLOS）渠道中减轻范围错误。 NLOS范围错误的分布是从接收到的原始信号中学到的，其中使用神经过程回归器（NPR）的网络精确地学习了与范围相关的信息。该网络可以用于在线学习，从而免费进行在线学习，这在计算上是有效的。仿真结果表明，所提出的方法在NLOS范围缓解误差方面优于传统技术。
The fifth-generation (5G) wireless communication is useful for positioning due to its large bandwidth and low cost. However, the presence of obstacles that block the line-of-sight (LOS) path between devices would affect localization accuracy severely. In this paper, we propose an online learning approach to mitigate ranging error directly in non-line-of-sight (NLOS) channels. The distribution of NLOS ranging error is learned from received raw signals, where a network with neural processes regressor (NPR) is utilized to learn the environment and range-related information precisely. The network can be implemented for online learning free from retraining the network, which is computationally efficient. Simulation results show that the proposed approach outperforms conventional techniques in terms of NLOS ranging error mitigation.
