Sensors, Vol. 22, Pages 5522: Reliable Aerial Mobile Communications with RSRP & amp; RSRQ Prediction Models for the Internet of Drones: A Machine Learning Approach

Sensors, Vol. 22, Pages 5522: Reliable Aerial Mobile Communications with RSRP & RSRQ Prediction Models for the Internet of Drones: A Machine Learning Approach Sensors doi: 10.3390/s22155522 Authors: Mehran Behjati Muhammad Aidiel Zulkifley Haider A. H. Alobaidy Rosdiadee Nordin Nor Fadzilah Abdullah The unmanned aerial vehicle (UAV) industry is moving toward beyond visual line of sight (BVLOS) operations to unlock future internet of drones applications, including unmanned environmental monitoring and long-range delivery services. A reliable and ubiquitous mobile communication link plays a vital role in ensuring flight safety. Cellular networks are considered one of the main enablers of BVLOS operations. However, the existing cellular networks are designed and optimized for terrestrial use cases. To investigate the reliability of provided aerial coverage by the terrestrial cellular base stations (BSs), this article proposes six machine learning-based models to predict reference signal received power (RSRP) and reference signal received quality (RSRQ) based on the multiple linear regression, polynomial, and logarithmic methods. In this regard, first, a UAV-to-BS measurement campaign was conducted in a 4G LTE network within a suburban environment. Then, the aerial coverage was statistically analyzed and the prediction methods were developed as a function of distance and elevation angle. The results reveal the capability of terrestrial BSs in providing aer...
Source: Sensors - Category: Biotechnology Authors: Tags: Article Source Type: research