Rafhael Medeiros de Amorim

Radio Channel Modeling for UAV Communication Over Cellular Networks

The main goal of this letter is to obtain models for path loss exponents and shadowing for the radio channel between airborne unmanned aerial vehicles (UAVs) and cellular networks. In this pursuit, field measurements were conducted in live LTE networks at the 800 MHz frequency band, using a commercial UAV. Our results show that path loss exponent decreases as the UAV moves up, approximating freespace propagation for horizontal ranges up to tens of kilometers at UAV heights around 100 m. Our findings support the need of height-dependent parameters for describing the propagation channel for UAVs at different heights.

How to Ensure Reliable Connectivity for Aerial Vehicles Over Cellular Networks

Widely deployed cellular networks are an attractive solution to provide large scale radio connectivity to unmanned aerial vehicles. One main prerequisite is that co-existence and optimal performance for both aerial and terrestrial users can be provided. Today’s cellular networks are, however, not designed for aerial coverage, and deployments are primarily optimized to provide good service for terrestrial users. These considerations, in combination with the strict regulatory requirements, lead to extensive research and standardization efforts to ensure that the current cellular networks can enable reliable operation of aerial vehicles in various deployment scenarios. In this paper, we investigate the performance of aerial radio connectivity in a typical rural area network deployment using extensive channel measurements and system simulations. First, we highlight that downlink and uplink radio interference play a key role, and yield relatively poor performance for the aerial traffic, when load is high in the network. Second, we analyze two potential terminal side interference mitigation solutions: interference cancellation and antenna beam selection. We show that each of these can improve the overall, aerial and terrestrial, system performance to a certain degree, with up to 30% throughput gain, and an increase in the reliability of the aerial radio connectivity to over 99%. Further, we introduce and evaluate a novel downlink inter-cell interference coordination mechanism applied to the aerial command and control traffic. Our proposed coordination mechanism is shown to provide the required aerial downlink performance at the cost of 10% capacity degradation in the serving and interfering cells.

Pathloss Measurements and Modeling for UAVs Connected to Cellular Networks

This paper analyzes the radio channel between cellular network and Unmanned Aerial Vehichles (UAVs). The assessment is done by means of field measurements performed in a rural environment in Denmark. The tests were conducted in an operating LTE network (800 MHz), using a commercial cell phone placed inside the frame of a winged UAV. Trials were conducted with UAV flying at 5 different heights measured above ground level (20, 40, 60, 80 and 100m) and a pathloss regression line was obtained from the results. Thereafter, an analysis of downlink (DL) interference is performed for the reported measurements, which suggests that there is a height-related degradation on signal-to-interference levels. Three possible sources for this effect are also presented and discussed in this paper: expanded radio horizon at higher levels, line-of-sight (LOS) clearing and decreased obstruction of the first Fresnel zone. The importance of a better quantification of these factors are stressed as future work plans are described.

Using LTE Networks for UAV Command and Control Link: A Rural-Area Coverage Analysis

In this paper we investigate the ability of Long- Term Evolution (LTE) network to provide coverage for Unmanned Aerial Vehicles (UAVs) in a rural area, in particular for the Command and Control (C2) downlink. The study takes into consideration the dependency of the large-scale path loss on the height of the UAV, which is derived from actual measurements, and a real-world cellular network layout and configuration. The results indicate that interference is the dominant factor limiting the cellular coverage for UAVs in the downlink: outage level increases from 4.2% at 1.5m height to 51.7% at 120m under full load condition. Lower network loads or larger inter-site distances reduces the interference and thus improves the coverage significantly: outage at 120m is reduced to only 1.9% under network load of 25% for example. Similar effects are expected to be achievable by static or dynamic interference coordination schemes. In addition, ideal Interference Cancellation (IC) scheme with ability to remove completely the dominant interferer shows less effective for UAVs than for users on the ground. On the other hand, macro network diversity has very good potential for drones, as not only it improves the coverage, but also the reliability of the C2 link.

Measured Uplink Interference Caused by Aerial Vehicles in LTE Cellular Networks

Aerial users, such as unmanned aerial vehicles (UAVs), experience different radio propagation conditions than users on the ground. This is a concern regarding the integration of such users into cellular networks in the near future. This letter investigates the impact of uplink transmissions from an aerial user equipment. Full buffer transmissions were performed by a device at ground level and also flying attached to a UAV at 100 m height. The field measurements show a higher number of cells affected by the aerial transmission, with an increase of up to 7.7 dB in the interference over thermal noise in cells within 15 km of the test location. This letter also assesses two strategies to reduce the uplink interference caused by aerial users: 1) UAV’s cruise height control and 2) directional transmissions. Results show the directional transmission is a more promising technique, and has the advantage of not reducing the uplink received power.

Interference Analysis for UAV Connectivity over LTE Using Aerial Radio Measurements

The use of Unmanned Aerial Vehicles (UAV) for civilian and commercial services has experienced a significant increase in the past couple of years. Emerging UAV enabled services, however, require extended beyond-visual-line-of-sight geographical range. One key regulatory requirement for these services is that the radio communication link must reliably cover a wide(er) area, when compared to the visual-line-of-sight range radio links currently used. Standardized cellular systems such as Long Term Evolution UMTS (LTE), are an obvious candidate to provide the radio communication link to UAVs. In this paper, we use empirical measurements in live rural LTE networks to assess the impact of uplink and downlink radio interference on the UAV radio connectivity performance. Further, we provide a baseline analysis on the potential of interference mitigation schemes, needed to provide a reliable radio connectivity to the UAVs.

Machine-Learning Identification of Airborne UAV-UEs Based on LTE Radio Measurements

The overall cellular network performance can be optimized for both ground and aerial users, if different treatment is given for the two user classes. Airborne UAVs experience different radio conditions that terrestrial users due to clearance in the radio path, which leads to strong desired signal reception, but at the same time increases the interference. Based on this, one can for instance use different interference coordination techniques for aerial users as for terrestrial user and/or use specific mobility settings for each class. This paper compares three different classification algorithms, which use standard LTE measurements from the UE as input, for detecting the presence of airborne users in the network. The algorithms are evaluated based on measurements done with mobile phones attached under a flying drone and on a car. Results are discussed showing the advantages and drawbacks for each option regarding different use cases, and the compromise between specificity and sensibility. For the collected data results show reliability close to 99% in most cases and also discuss how waiting for the final decision can even improve this accuracy to values close to 100%.