Electrical Engineering Graduate Department, Federal University of Pará, Belém 66075-110, Brazil.
Sensors (Basel). 2022 Jul 13;22(14):5233. doi: 10.3390/s22145233.
The 5G deployment brings forth the usage of Unmanned Aerial Vehicles (UAV) to assist wireless networks by providing improved signal coverage, acting as relays or base-stations. Another technology that could help achieve 5G massive machine-type communications (mMtc) goals is the Long Range Wide-Area Network (LoRaWAN) communication protocol. This paper studied these complementary technologies, LoRa and UAV, through measurement campaigns in suburban, densely forested environments. Downlink and uplink communication at different heights and spreading factors (SF) demonstrate distinct behavior through our analysis. Moreover, a neural network was trained to predict the measured signal-to-noise ratio (SNR) behavior and results compared with SNR regression models. For the downlink scenario, the neural network results show a root mean square error (RMSE) variation between 1.2322-1.6623 dB, with an error standard deviation (SD) less than 1.6730 dB. For the uplink, the RMSE variation was between 0.8714-1.3891 dB, with an error SD less than 1.1706 dB.
5G 部署带来了无人机 (UAV) 的使用，通过提供改进的信号覆盖，充当中继器或基站来辅助无线网络。另一种可以帮助实现 5G 大规模机器类型通信 (mMtc) 目标的技术是远程广域网 (LoRaWAN) 通信协议。本文通过在郊区、茂密森林环境中的测量活动研究了这些互补技术，即 LoRa 和无人机。通过我们的分析，下行链路和上行链路在不同高度和扩展因子 (SF) 下的通信表现出不同的行为。此外，还训练了一个神经网络来预测测量的信噪比 (SNR) 行为，并将结果与 SNR 回归模型进行了比较。对于下行链路场景，神经网络结果显示均方根误差 (RMSE) 的变化在 1.2322-1.6623dB 之间，误差标准差 (SD) 小于 1.6730dB。对于上行链路，RMSE 的变化在 0.8714-1.3891dB 之间，误差 SD 小于 1.1706dB。
