Division of Computer Science and Informatics, London South Bank University, London SE1 0AA, UK.
Department of Electrical Engineering, Eindhoven University of Technology, 5612 AE Eindhoven, The Netherlands.
Sensors (Basel). 2020 Sep 25;20(19):5495. doi: 10.3390/s20195495.
In the near future, the fifth-generation wireless technology is expected to be rolled out, offering low latency, high bandwidth and multiple antennas deployed in a single access point. This ecosystem will help further enhance various location-based scenarios such as assets tracking in smart factories, precise smart management of hydroponic indoor vertical farms and indoor way-finding in smart hospitals. Such a system will also integrate existing technologies like the Internet of Things (IoT), WiFi and other network infrastructures. In this respect, 5G precise indoor localization using heterogeneous IoT technologies (Zigbee, Raspberry Pi, Arduino, BLE, etc.) is a challenging research area. In this work, an experimental 5G testbed has been designed integrating C-RAN and IoT networks. This testbed is used to improve both vertical and horizontal localization (3D Localization) in a 5G IoT environment. To achieve this, we propose the DEep Learning-based co-operaTive Architecture (DELTA) machine learning model implemented on a 3D multi-layered fingerprint radiomap. The DELTA begins by estimating the 2D location. Then, the output is recursively used to predict the 3D location of a mobile station. This approach is going to benefit use cases such as 3D indoor navigation in multi-floor smart factories or in large complex buildings. Finally, we have observed that the proposed model has outperformed traditional algorithms such as Support Vector Machine (SVM) and K-Nearest Neighbor (KNN).
在不久的将来，预计将推出第五代无线技术，提供低延迟、高带宽和单个接入点中部署的多个天线。这个生态系统将有助于进一步增强各种基于位置的场景，如智能工厂中的资产跟踪、水培室内垂直农场的精确智能管理和智能医院中的室内导航。该系统还将集成物联网 (IoT)、WiFi 和其他网络基础设施等现有技术。在这方面，使用异构物联网技术（Zigbee、Raspberry Pi、Arduino、BLE 等）的 5G 精确室内定位是一个具有挑战性的研究领域。在这项工作中，设计了一个集成 C-RAN 和物联网网络的实验 5G 测试平台。该测试平台用于改善 5G IoT 环境中的垂直和水平定位（3D 定位）。为了实现这一目标，我们提出了基于深度学习的协作架构 (DELTA) 机器学习模型，该模型基于 3D 多层指纹无线电图实现。DELTA 首先估计 2D 位置。然后，将输出递归用于预测移动站的 3D 位置。这种方法将受益于多楼层智能工厂或大型复杂建筑物中的 3D 室内导航等用例。最后，我们观察到所提出的模型优于传统算法，如支持向量机 (SVM) 和 K-最近邻 (KNN)。
