System Requirements Specification for Unmanned Aerial Vehicle (UAV) to Server Communication

Abstract

Within this paper, requirements for server to Unmanned Aerial Vehicle (UAV) communication over the mobile network are evaluated. It is examined, whether a reliable cellular network communication can be accomplished with the use of current Long Term Evolution (LTE) network technologies, or, if the 5th Generation (5G) network is indispensable. Moreover, enhancements on improving the channel quality on the UAVside are evaluated. Therefore, parameters like data rate, latency, message size and reliability for Command and Control (C&C) and application data are determined. Furthermore, possible improvements regarding interference mitigation in the up-and downlink of the UAV are discussed. For this purpose, results from publications of the 3rd Generation Partnership Project (3GPP) and from surveys regarding UAVs and mobile networks are presented. This work shows that, for C&C use cases like steering to waypoints, the latency and the data rate of the LTE network is sufficient, but in terms of reliability problems can occur. Furthermore, the usability of standard protocols for computer networks like the Transmission Control Protocol (TCP) and User Datagram Protocol (UDP) is discussed. There are also multimodal implementations of these protocols like MultiPath TCP (MPTCP) which can be adapted into the UAV's communication system in order to increase reliability through multiple communication channels. Finally, applications for Long Range (LoRa) direct communication in terms of supporting the cellular network of the UAV are considered.

Enabling Technologies for the Navigation and Communication of UAS Operating in the Context of BVLOS

“…For example, with cellular connections, latency spikes can occur during the handover between two mobile radio base stations. Moreover, within the context of UAVs connected via mobile radio, there is a degradation of the QoS with rising altitudes [63]. In order to predict these fluctuations before they occur and to be able to initiate countermeasures, if necessary, a Deep Learning (DL)-based QoS prediction model is being developed.…”

“…In the Concatenator, the streams are merged with additional UAV data based on the measurement timestamps. The UAV parameter includes, for example, the height above the ground, as the reliability of the mobile radio connection generally decreases with the increasing height [63]. The parameters are then forwarded to the prediction pipeline, whereby the steps marked in green are processed on the CPU and the blue ones are deployed on the AI accelerator.…”

Tackling different aspects of drone services utilizing technologies from cross-sectional industries

“…Due to availability and ability to cover long distances, the mobile network is considered the most obvious. However, there are problems with the LTE network in particular, such as down-and up-link interference, utilization of side-lobes and an increase in handovers due to the height [10]. For this reason, the 5G network is additionally employed and models are developed that predict the QoS of the mobile network.…”