Measuring Low Earth Orbit (LEO) Satellite Networks: The Impact of Weather on the Link Quality
Eric Lanfer1, Dominic Laniewski1 and Nils Aschenbruck1
1Institute of Computer Science, Osnabrück University, Osnabrück, Germany
In recent months, the rise of Low Earth Orbit (LEO) satellite networks, such as Starlink, gave a boost to the Internet connection for many people who were only poorly connected. Especially in remote regions, the bandwidths by using a LEO satellite network increased by a hundredfold compared to legacy copper links or GEOsatellite links. However, the signals for LEO wireless links have to propagate more than 340 to 550 km up to the low earth orbit satellites, whereby they are exposed to various environmental influences. We ran two internet measurement campaigns on analyzing the impact of environmental influences such as weather. In this presentation, we present and discuss the results of our measurements. We show that precipitation is affecting the link and is causing drops, especially in the down link bandwidth. Moreover, we discuss the influence of clouds on the signal.
Presentation slides:
Sub6 GHz Non-Line-of-Sight Wireless Signals for Rainfall Monitoring
Xichuan Liu1,2 and Kang Pu1,2
1College of Meteorology and Oceanography, National University of Defense Technology, Changsha, China
2Key Laboratory of High Impact Weather (Special), China Meteorological Administration, Changsha, China
To explore the potential of Sub6 GHz signal for rainfall measurement, we first investigate four rain-induced mechanisms: raindrop group extinction effect, wet antenna transmittance effect, wet surface reflectance effect, and wet wall transmittance effect. Secondly, we carried out a field experiment using two commercial smartphones which can record the downlink signals (RSRP, RSRQ, RSSI, and SINR) from the base station (BS). At last, we propose a rainfall inversion model based on a double hidden layer backward propagation neural network, the field results show that the dry period (rainy period) can be identified with more than 95% (around 90%) recall and precision, and light rain, middle rain and heavy rain can be recognized with near or greater than 80% recall and precision.
