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用于受控无人机飞行的LTE移动网络性能参数的实验研究

Experimental Study on LTE Mobile Network Performance Parameters for Controlled Drone Flights.

作者信息

Braunfelds Janis, Jakovels Gints, Murans Ints, Litvinenko Anna, Senkans Ugis, Rumba Rudolfs, Onzuls Andis, Valters Guntis, Lidere Elina, Plone Evija

机构信息

Institute of Photonics, Electronics and Telecommunications, Riga Technical University, LV-1048 Riga, Latvia.

Ventures Department, Latvijas Mobilais Telefons SIA, LV-1026 Riga, Latvia.

出版信息

Sensors (Basel). 2024 Oct 14;24(20):6615. doi: 10.3390/s24206615.

DOI:10.3390/s24206615
PMID:39460095
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11511506/
Abstract

This paper analyzes the quantitative quality parameters of a mobile communication network in a controlled drone logistic use-case scenario. Based on the analysis of standards and recommendations, the values of key performance indicators (KPIs) are set. As the main network-impacting parameters, reference signal received power (RSRP), reference signal received quality (RSRQ), and signal to interference and noise ratio (SINR) were selected. Uplink (UL), downlink (DL), and ping parameters were chosen as the secondary ones, as they indicate the quality of the link depending on primary parameters. The analysis is based on experimental measurements performed using a Latvian mobile operator's "LMT" JSC infrastructure in a real-life scenario. To evaluate the altitude impact on the selected network parameters, the measurements were performed using a drone as transport for the following altitude values: 40, 60, 90, and 110 m. Network parameter measurements were implemented in automatic mode, allowing switching between LTE4-LTE2 standards, providing the opportunity for more complex analysis. Based on the analysis made, the recommendations for the future mobile networks employed in controlled drone flights should correspond to the following KPI and their values: -100 dBm for RSRP, -16 dB for RSRQ, -5 dB for SINR, 4096 kbps for downlink, 4096 kbps for uplink, and 50 ms for ping. Lastly, recommendations for a network coverage digital twin (DT) model with integrated KPIs are also provided.

摘要

本文分析了在受控无人机物流用例场景中移动通信网络的定量质量参数。基于对标准和建议的分析，设定了关键性能指标（KPI）的值。作为主要的网络影响参数，选择了参考信号接收功率（RSRP）、参考信号接收质量（RSRQ）和信号干扰噪声比（SINR）。上行链路（UL）、下行链路（DL）和ping参数被选为次要参数，因为它们根据主要参数指示链路质量。该分析基于在实际场景中使用拉脱维亚移动运营商的“LMT”股份公司基础设施进行的实验测量。为了评估海拔高度对所选网络参数的影响，使用无人机作为运输工具，在以下海拔高度值下进行测量：40米、60米、90米和110米。网络参数测量以自动模式实施，允许在LTE4-LTE2标准之间切换，为更复杂的分析提供了机会。基于所做的分析，用于受控无人机飞行的未来移动网络的建议应符合以下KPI及其值：RSRP为-100 dBm，RSRQ为-16 dB，SINR为-5 dB，下行链路为4096 kbps，上行链路为4096 kbps，ping为50毫秒。最后，还提供了具有集成KPI的网络覆盖数字孪生（DT）模型的建议。

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2
RF-Enabled Deep-Learning-Assisted Drone Detection and Identification: An End-to-End Approach.基于射频的深度学习辅助无人机检测与识别：端到端方法。
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3
Multidimensional spatial monitoring of open pit mine dust dispersion by unmanned aerial vehicle.利用无人机对露天矿粉尘扩散进行多维空间监测。
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4
Research Regarding Different Types of Headlights on Selected Passenger Vehicles when Using Sensor-Related Equipment.关于使用传感器相关设备时，对特定乘用车的各种前照灯的研究。
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5
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Sci Rep. 2023 Feb 20;13(1):2929. doi: 10.1038/s41598-023-29950-w.
6
Experimenting Agriculture 4.0 with Sensors: A Data Fusion Approach between Remote Sensing, UAVs and Self-Driving Tractors.农业 4.0 传感器实验：遥感、无人机和自动驾驶拖拉机之间的数据融合方法。
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7
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