Farooq Muhammad Umar Bin, Manalastas Marvin, Qureshi Haneya, Liu Yongkang, Imran Ali, Al Kalaa Mohamad Omar
AI4Networks Research Center, School of Electrical & Computer Engineering, University of Oklahoma, USA.
Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, MD, USA.
Healthcom. 2022 Oct;2022:81-87. doi: 10.1109/healthcom54947.2022.9982778. Epub 2022 Dec 21.
The fifth generation of cellular network (5G) can facilitate in-ambulance patient monitoring, diagnosis, and treatment by a remote specialist. However, 5G coverage and link quality can vary in time and location. The ambulance route selection can help meet the communication requirements of the in-ambulance applications. In this paper, we propose an innovative ambulance route selection framework which combines the communication requirements along with the network coverage and resources. The framework leverages the minimization of drive test (MDT) data to estimate the network coverage along the ambulance routes. To address the uneven distribution of location-based user-generated MDT data, we examine the performance and trustworthiness of several interpolation techniques to enrich the global MDT map for route selection. A simulated analysis shows that the proposed framework can dynamically adapt to varying application requirements as well as rapidly changing network conditions such as outages. Results also reveal that nearest neighbor and kriging interpolation techniques help complement the proposed framework by addressing the data sparsity problem.
第五代蜂窝网络（5G）能够助力远程专家对救护车内的患者进行监测、诊断和治疗。然而，5G覆盖范围和链路质量会随时间和地点而变化。救护车路线选择有助于满足救护车内应用的通信需求。在本文中，我们提出了一种创新的救护车路线选择框架，该框架将通信需求与网络覆盖范围及资源相结合。该框架利用最小化路测（MDT）数据来估计救护车路线沿线的网络覆盖范围。为解决基于位置的用户生成MDT数据分布不均的问题，我们研究了几种插值技术的性能和可信度，以丰富用于路线选择的全局MDT地图。模拟分析表明，所提出的框架能够动态适应不断变化的应用需求以及诸如中断等快速变化的网络状况。结果还表明，最近邻插值和克里金插值技术通过解决数据稀疏问题，有助于补充所提出的框架。
