论文标题
基于深度学习的OTDOA定位，用于NB-IOT通信系统
Deep Learning based OTDOA Positioning for NB-IoT Communication Systems
论文作者
论文摘要
定位已成为许多物联网（IoT）应用程序中的关键组成部分。主要的挑战和局限性是狭窄的带宽，低功率和低成本，从而降低了到达时间（TOA）估计的准确性。在本文中，我们考虑窄带物联网（NB-iot）的定位方案，这些方案可以受益于观察到的到达时间差异（OTDOA）。通过应用基于深度学习的技术，我们探讨了神经网络的概括和提取能力，以应对上述挑战。如数值实验中所示，与高斯 - 纽顿方法相比，所提出的算法可在不同的地点间距离情况下使用，并在不同的位置准确性（LOS）方案（LOS）方案和非线视觉（NLOS）风景中提高了位置精度。
Positioning is becoming a key component in many Internet of Things (IoT) applications. The main challenges and limitations are the narrow bandwidth, low power and low cost which reduces the accuracy of the time of arrival (TOA) estimation. In this paper, we consider the positioning scenario of Narrowband IoT (NB-IoT) that can benefit from observed time difference of arrival (OTDOA). By applying the deep learning based technique, we explore the generalization and feature extraction abilities of neural networks to tackle the aforementioned challenges. As demonstrated in the numerical experiments, the proposed algorithm can be used in different inter-site distance situations and results in a 15% and 50% positioning accuracy improvement compared with Gauss-Newton method in line-of-sight (LOS) scenario and non-line-of-sight (NLOS) scenario respectively.
