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An Experimental Evaluation of LTE-A Throughput for Drones

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DOI: 10.1145/3325421.3329765

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An Experimental Evaluation of LTE-A Throughput for DronesSamira Hayat

University of KlagenfurtKlagenfurt, Austriasamira.hayat@aau.at

Christian BettstetterUniversity of Klagenfurt

Klagenfurt, Austriachristian.bettstetter@aau.at

Aymen FakhreddineUniversity of Klagenfurt

Klagenfurt, Austriaaymen.fakhreddine@aau.at

Raheeb MuzaffarLakeside Labs

Klagenfurt, Austriamuzaffar@lakeside-labs.com

Driton EminiT-Mobile AustriaVienna, Austria

driton.emini@t-mobile.at

ABSTRACTThis work presents an experimental performance study on thewireless communication of a quadrocopter connected to an LTE-Advanced network. Measurements of TCP traffic analyze how thereceived power level, signal-to-interference ratio, and throughputdepend on the flight height. An average throughput of 20Mb/s inthe downlink and 40Mb/s in the uplink is achieved at 150 m. Wealso show how the number of line-of-sight links to base stationsrises with height and leads to an increased handover rate.

CCS CONCEPTS• Networks → Network experimentation; Network perfor-mance analysis; Network measurement; Mobile networks.

KEYWORDSWireless; drones; UAV; LTE-A; throughput; experiments; handover.

ACM Reference Format:Samira Hayat, Christian Bettstetter, Aymen Fakhreddine, Raheeb Muzaffar,and Driton Emini. 2019. An Experimental Evaluation of LTE-A Throughputfor Drones. In The 5th Workshop on Micro Aerial Vehicle Networks, Systems,and Applications (DroNet’19), June 21, 2019, Seoul, Republic of Korea. ACM,New York, NY, USA, 6 pages. https://doi.org/10.1145/3325421.3329765

1 INTRODUCTIONMany drone applications require reliable wireless technology thatsupports both high data rates and wide-area coverage. High-rateuplinks are needed to send large volumes of sensor data (e.g., aerialimages) to a data center or user. Reliable downlinks are essential forreceiving commands from a ground controller. Local area commu-nication technologies operating in the unlicensed spectrum— IEEE802.11, Bluetooth, IEEE 802.15.4— do not meet the application re-quirements if many drones have to be connected over wide areas.It could be argued that cellular networks—Long Term Evolution

Permission to make digital or hard copies of all or part of this work for personal orclassroom use is granted without fee provided that copies are not made or distributedfor profit or commercial advantage and that copies bear this notice and the full citationon the first page. Copyrights for components of this work owned by others than ACMmust be honored. Abstracting with credit is permitted. To copy otherwise, or republish,to post on servers or to redistribute to lists, requires prior specific permission and/or afee. Request permissions from permissions@acm.org.DroNet’19, June 21, 2019, Seoul, Republic of Korea© 2019 Association for Computing Machinery.ACM ISBN 978-1-4503-6772-1/19/06. . . $15.00https://doi.org/10.1145/3325421.3329765

(LTE) and 5G—offer a more suitable solution to some of the chal-lenges of multi-drone systems [20]. Today’s LTE networks permitsufficient long-range connectivity for many applications, such asdelivery [15] and wide-area coverage [9]. As low latency and higherrates are expected in 5G, the real-time transfer of control commandsand coordination of large swarms could soon become a reality.

To gain a deeper insight into cellular-connected drones, an ex-perimental evaluation is performed of drones acting as mobileterminals connected to an LTE-A (Long Term Evolution Advanced)network running 3GPP (3rd Generation Partnership Project) Re-lease 13 [1]. The aim is to understand the impact of flight altitudeon throughput and handovers in a standard cellular network with-out any drone-specific enhancements. Measurements are made atdifferent flight altitudes and compared to a terrestrial baseline sce-nario. The results are promising: an average throughput of 20Mb/sin the downlink (DL) and 40 Mb/s in the uplink (UL) is obtained ata typical flight height of 150 m. These numbers are helpful for de-signers of drone applications in determining whether LTE-A meetstheir requirements.

The paper is structured as follows: Section 2 addresses relatedwork and compares it to our contributions. Section 3 describes thehardware, software, and test setup used. Section 4 reports and inter-prets the results of our experiments. Section 5 concludes. The termdrone is used as a synonym for unmanned aerial vehicle (UAV). Arelated paper by the same authors [12] studies handover challengesin an identical setup.

2 RELATEDWORK AND CONTRIBUTIONSThe performance of drone communications has mainly been studiedfor different variants of IEEE 802.11 [5, 10, 16, 27, 28]. Only a fewgroups have used drones in the context of cellular networks (see[13, 29, 30] and references therein), and a subset of these haveperformed actual experiments [2–4, 6, 7, 14, 24, 25].

There are several issues related to integrating aerial devices intocellular networks. One problem is that the power received from abase station decreases with increasing height, due to the antennadowntilt: Experiments with LTE show that the power can decreaseby more than 10 dB for a drone that ascends from the ground tohundred meters [7]. Another problem is that aerial devices establishline-of-sight links to faraway base stations, so that the number ofselectable base stations rises with increasing flight height [4, 7].This entails negative consequences for the signal-to-interferenceratio (SIR), which may “decrease up to 7 dB for UAVs at 150m

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https://doi.org/10.1145/3325421.3329765https://doi.org/10.1145/3325421.3329765

Table 1: Experimental studies on LTE

Paper Contribution Release Height Traffic Throughput UL Throughput DL

Albaladejoet al. [3]

Derived a model for throughput versus signalstrength based on experiments (ground)

8 0 m UDP - 20 Mb/s

Gangulaet al. [14]

Developed real-time relay drone positioning tooptimize throughput (two hops)

unknown - UDP - 6 Mb/s

Huanget al. [18]

Derived power model and made performancestudy in comparison to 3G and WiFi (ground)

8 0 m TCP 5.64 Mb/s 12.7 Mb/s

Van der Berghet al. [7]

Discussed interference challenges for integratingdrones into LTE

unknown 300 m - - -

Athanasiadouet al. [6]

Studied signal strength and interference 8 350 m - - -

Nguyenet al. [21]

Studied interference, interference mitigation, co-existence of ground, aerial UEs (single UE beam)

8 120 m UDP 8.6 Mb/s -

compared to ground users” [7]. The availability of line-of-sight linksto multiple base stations results in interference on both UL and DL:UL transmissions from a dronemay cause interference to both aerialand ground users being served by the base stations in the vicinityof the drone; DL transmissions from neighboring base stations onthe same channel may reduce the SIR. The throughput of LTE-connected drones has only been studied by using simulations [7,24]. For example, it is shown that a high drone density results inthroughput degradation due to interference caused by simultaneousUL transmissions. Last but not least, have drones also found theirway into 3GPP standardization: enhancements for aerial networks,which consider three-dimensional mobility, are proposed [19, 20].However, due to the lack of experimental evaluations, it is stillunclear how integrating drones into cellular networks will impactground devices [8].

The purpose of this work is to report experimental results withdrones connected to an LTE-A network. The power level, SIR,throughput, and occurrence of handovers, as a function of theflight height, are studied. Similar experiments with LTE-A havenot previously been presented, as most existing work focuses onreception power and interference (cf. Table 1). A recent study ofLTE-connected drones, carried out by Qualcomm [22], states that anexperimental analysis of throughput limits is subject to future work.

3 EXPERIMENTAL SETUPWe fly an Asctec Pelican quadrocopter in a field adjacent to theUniversity of Klagenfurt campus (see Fig. 1). The drone carriesa mobile user equipment (UE) connected to an LTE-A network.There are several base stations (called eNodeB or eNB in LTE) inthe vicinity of the campus. A Sony Xperia XZ2 H8216 smartphoneis used as the UE. It runs the Android 8.0 Oreo operating system,has two quad-core processors with 4 GB RAM, and comes with theQualcomm Snapdragon 845 chipset supporting LTE carrier aggrega-tion. The UE and SIM (subscriber identity module) are provided byT-Mobile Austria. All T-Mobile eNBs in the area of our experimentsare equipped with 3GPP Release 13 (2016). Channels with 10 MHz

bandwidth are used at 800MHz and 2.1GHz, together with chan-nels with 20 MHz at 1.8GHz and 2.6GHz. Carrier aggregation isactivated in the DL over all the spectrum layers used; the maximumaggregated bandwidth in our setup is 60 MHz. A 2 × 2 multiple-input multiple-output (MIMO) antenna configuration is used. TheeNB antennas are located at a height of 29 m, use an electric tilt ofbetween 5° and 10°, and have a maximum transmission power of20W. The modulation schemes are 256 QAM (quadrature amplitudemodulation) in DL and 64 QAM in UL.

Serving PCI

Flight Trajectory

Figure 1: Flight path and locations of serving base stations.

A custom-built tool [23], which is based on Android API (appli-cation programming interface), is installed on the UE to record themeasurements. This tool is capable of recording all the possible LTEparameters available on Android devices, along with the Global Po-sitioning System (GPS) coordinates, UE speed, and UE altitude. TheLTE parameters recorded include the following: Reference SignalReceived Power (RSRP), Reference Signal Received Quality (RSRQ),

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Reference Signal Signal-to-Noise Ratio (RSSNR), Serving PhysicalCell identity (PCI), E-UTRA Absolute Radio Frequency ChannelNumber (EARFCN), Transmission Control Protocol (TCP) DL andUL throughput, as well as information on neighboring interferingPCIs. The logs from this tool are not finely grained, i.e., the mea-surements are only logged at a per second interval. This meansthat some information, such as the impact of time-to-trigger (TTT),may be lost. Moreover, the logs are not detailed enough to allowin-depth analysis of the carrier aggregation.

The experiments are performed in an obstacle-free, open field,with line-of-sight to at least one eNB for all tests. The flight pathis a straight line, up to a distance of 300 m from the starting point,with a flight speed of 3 m/s. The path and positions of the servingPCIs are shown in Figure 1. The data is logged during both theoutward and return flight.

4 EXPERIMENTAL RESULTS & DISCUSSIONWe study the performance in terms of RSRP, SIR, RSSNR, through-put, and handovers at different flight heights. The results frommultiple repetitions of the experiment are very similar, which iswhy only the outcomes from a single run are presented. We startwith a baseline scenario close to the ground for the DL and laterextend the analysis to heights of up to 150 m and for the UL.

4.1 Baseline scenarioThe baseline scenario is a drone flying ten meters above the groundto measure performance similar to that of a terrestrial user.1 TCPtraffic in the DL is used. An LTE handover is initiated if the currentcell’s quality metric (RSRP, RSRQ) falls below that of a neighboringcell by a threshold value, termed the hysteresis margin [11], fora certain duration, referred to as TTT. Figures 2a and b illustrateRSRP and RSRQ’s effect on the choice of the cell, to determine thehandover cause in our network. The PCIs of the serving eNBs areshown in different colors. The upper plots show the two metrics forthe serving cells and the lower plots those of all cells. A handover isperformed when the difference between the RSRP of the serving celland that of a neighboring cell (with the same EAFRCN) exceeds thehysteresis margin (reportedly 5 dB). Consider the events marked bythe ellipses labeled a and b: In Event a, the RSRP of Cell 261 dropsand stays below that of Cell 263 for 45 seconds; a handover is notperformed because the hysteresis margin is not exceeded. Event bshows a ping-pong handover: the RSRP of Cell 263 falls below thatof Cell 70 by 5 dB, which triggers a handover; however, the RSRPof Cell 263 recovers well and quickly, namely to −97 dBm after sixseconds (compared to −104 dBm in Cell 70), which, in turn, triggersa handover back to Cell 263.

Although a precise relationship between RSRQ and handoverscannot be established in Fig. 2b, the logs show the following: iftwo candidate cells report the same RSRP, the cell with the higherRSRQ will be chosen for handover. This makes sense if the traf-fic pattern is bursty. RSRQ is a signal-to-interference-and-noiseratio, in which the signal power (RSRP) is averaged over a sin-gle sub-carrier, whereas interference and noise are averaged overmultiple sub-carriers. The resulting ratio fluctuates significantly. A

1As the onboard GPS suffers from an inaccuracy of about 5 m, flying at 10 m is stillsafe and enables us to capture the performance of LTE for a terrestrial user.

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Figure 3: Reference signals received power (RSRP) at a droneat different flight heights (downlink).

handover decision based solely on RSRQ would therefore lead tomany unnecessary handovers.

Figure 2c shows the measured throughput, SIR, and signal-to-noise ratio (SNR) over time. We use the RSRP of all the loggedneighboring cells with the same frequency channel (EAFRCN) toextract the SIR. An average throughput of 65Mb/s is reported for thebaseline scenario. The plots show a strong correlation between thethroughput and the RSSNR, stronger than to the SIR. For example,the throughput reaches 50Mb/s for an RSSNR value of 10 dB anddrops to 20 Mb/s for 0 dB.

4.2 Aerial scenariosWe now study RSRP, throughput, SIR, and RSSNR, as a function ofthe flight altitude. RSRQ is excluded, as its contribution is limitedto cell selection when multiple candidate cells have the same RSRP.

4.2.1 How does RSRP depend on altitude? Figure 3 plots the RSRPover time for three flight altitudes. The results shown are for the DL;experiments for the UL yield similar values. At 50 m, the number ofcells serving the flight area is the same as in the baseline scenario.The number of handovers increases with increasing height— a re-sult that is further discussed in [12]. At higher altitudes (100 and150 m), the RSRP values from the different eNBs experience signifi-cant and frequent drops (compared to the baseline and 50 m tests),which is the reason for the high handover frequency. However, thebest RSRP, over all cells, is always high. At higher altitudes, theRSRP values of adjacent cells are very close to each other; thus, theinterference is expected to increase with altitude.

These results illustrate the known consequences of antenna tilt-ing. Since the main lobe of the eNB antennas is pointed downwards,devices at higher altitudes are served by the low-power sidelobes,which have a narrow angular coverage. This results in frequentRSRP drops, which in turn leads to frequent handovers. The higheroverall RSRP at higher altitudes is due to line-of-sight connectivitywith the eNBs. With such line-of-sight connectivity, the link bene-fits from free-space radio propagation with low attenuation [19].

4.2.2 How does throughput depend on altitude? Our measurementsshow throughput values that satisfy the requirements of manydrone applications [17]. For example, at a typical flight height of150m, an average throughput of 20Mb/s in the DL and 40Mb/sin the UL is achieved. Let us first interpret the DL results (Fig. 4):The average throughput drops with altitude, and there is a strongcross-correlation between instantaneous throughput and RSSNR,which increases with altitude (see Table 2). The throughput is alsocorrelated to the SIR for high altitudes (150m). The number ofbase stations with line-of-sight links to a drone increases withaltitude [7]. The interfering signals from these base stations arethen received at the drone with an RSRP similar to that of theintended signal, which in turn lowers the SIR. If both RSSNR andSIR are high, instantaneous throughput peaks of up to 150Mb/s areachieved, even at high altitudes (see Fig. 4c). Another observationis that low (high) RSRP fluctuations from the serving cell lead tolow (high) throughput fluctuations (cp. Figs. 3 and 4).

The throughput and RSSNR for the UL are given in Figure 5.The RSSNR values are similar to those for the DL. The averagethroughput appears to be less sensitive to altitude and remains at a

Session: Cellular-connected UAVs DroNet’19, June 21, 2019, Seoul, Korea

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Table 2: Cross-correlation of throughput to SNR and SIR

Height SNR Correlation SIR Correlation10 m 0.40 0.3150 m 0.58 0.27100 m 0.59 0.36150 m 0.78 0.63

(Average values over four measurement runs)

level of about 40Mb/s for all the investigated flight heights. Suchhigh UL throughput is promising for applications in which dronessend real-time sensor data (e.g., high quality images, videos) toground entities (e.g., first responders in search and rescue missions).The high throughput fluctuations, which are problematic for appli-cations requiring low jitter, might be caused by the time-varyingdelays in our measurements.

5 CONCLUSIONS AND OUTLOOKOur performance evaluation of a drone connected to an LTE-Anetwork shows encouraging throughput results: At the flight alti-tudes commonly used for commercial drone operations, an averagethroughput of a few tens of Megabits per second can be achieved inboth uplink and downlink, and a peak throughput of 150Mb/s is ob-served in our setup. Issues with interference [7] and handovers [12]remain, which need to be addressed in future work. The high han-dover rate needs to be tackled in order to reduce the signal overheadand avoid other handover-related issues. We conjecture that ad-vanced interference management will lead to higher throughput,especially in the downlink. The optimization of antenna tilting andbeamforming [26] for drone connectivity is an issue for antennaexperts and network planners.

ACKNOWLEDGEMENTSThis work results from a collaboration between the University ofKlagenfurt, Lakeside Labs GmbH, and T-Mobile Austria GmbH.R. Muzaffar’s work is funded by the security research program KI-RAS of the Federal Ministry for Transport, Innovation, and Technol-ogy (bmvit), Austria, under grant agreement n. 854747 (WatchDog).C. Bettstetter’s work is part of the Karl Popper Kolleg on networkedautonomous aerial vehicles at the University of Klagenfurt.

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Session: Cellular-connected UAVs DroNet’19, June 21, 2019, Seoul, Korea

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https://www.3gpp.org/release-13https://www.researchgate.net/publication/333837807

Abstract1 Introduction2 Related Work and Contributions3 Experimental Setup4 Experimental Results & Discussion4.1 Baseline scenario4.2 Aerial scenarios

5 Conclusions and OutlookReferences