Reliable Long-Range Multi-Link Communication for Unmanned Search and Rescue Aircraft Systems in Beyond Visual Line of Sight Operation

Abstract

With the increasing availability of unmanned aircraft systems, their usage for search and rescue is close at hand. Especially in the maritime context, aerial support can yield significant benefits. This article proposes and evaluates the concept of combining multiple cellular networks for highly reliable communication with those aircraft systems. The proposed approach is experimentally validated in several unprecedented large-scale experiments in the maritime context. It is found that in this scenario, conventional methods do not suffice for reliable connectivity to the aircraft with significantly varying overall availabilities between 68% and 97%. The underlying work, however, overcomes the limitations of single-link connectivity by providing availability of up to 99.8% in the analyzed scenarios. Therefore, the approach and the experimental data presented in this work yield a solid contribution to search and rescue drones. All results and flight recording data sets are published along with this article to enable future related work and studies, external reproduction, and validation of the underlying results and findings.

Full text

drones
Article
Reliable Long-Range Multi-Link Communication for
Unmanned Search and Rescue Aircraft Systems in
Beyond Visual Line of Sight Operation
Johannes Güldenring * , Philipp Gorczak , Fabian Eckermann , Manuel Patchou ,
Janis Tiemann , Fabian Kurtz and Christian Wietfeld
Communication Networks Institute (CNI), TU Dortmund University, 44227 Dortmund, Germany;
philipp.gorczak@tu-dortmund.de (P.G.); fabian.eckermann@tu-dortmund.de (F.E.);
manuel.mbankeu@tu-dortmund.de (M.P.); janis.tiemann@tu-dortmund.de (J.T.);
fabian.kurtz@tu-dortmund.de (F.K.); christian.wietfeld@tu-dortmund.de (C.W.)
*Correspondence: johannes.gueldenring@tu-dortmund.de
Received: 23 March 2020; Accepted: 25 April 2020; Published: 1 May 2020


Abstract:
With the increasing availability of unmanned aircraft systems, their usage for search
and rescue is close at hand. Especially in the maritime context, aerial support can yield significant
benefits. This article proposes and evaluates the concept of combining multiple cellular networks for
highly reliable communication with those aircraft systems. The proposed approach is experimentally
validated in several unprecedented large-scale experiments in the maritime context. It is found that
in this scenario, conventional methods do not suffice for reliable connectivity to the aircraft with
significantly varying overall availabilities between 68% and 97%. The underlying work, however,
overcomes the limitations of single-link connectivity by providing availability of up to 99.8% in
the analyzed scenarios. Therefore, the approach and the experimental data presented in this work
yield a solid contribution to search and rescue drones. All results and flight recording data sets are
published along with this article to enable future related work and studies, external reproduction,
and validation of the underlying results and findings.
Keywords:
unmanned aircraft system (UAS); Unmanned Aerial Vehicle (UAV); search and
rescue (SAR); long-range communications; Long-Term Evolution (LTE); Beyond Visual Line of
Sight (BVLOS); multi-link; multi-path; multi-homing; multi-RAT; rescue robotics
1. Introduction
The recently growing availability and fast-paced development of Unmanned Aerial Vehicles (
UAV
s)
have widened their area of application. One of these fields is the support of Search and Rescue (
SAR
)
missions. When emergency calls are picked up in maritime rescue stations, time is the most critical
parameter and every second counts. The success of
SAR
missions greatly depends on the information
available to the rescue forces. The available knowledge about the situation, such as the position,
is usually very imprecise and must be clarified as quickly as possible. Even though nowadays search
and rescue strategies are highly developed, water-based searches by boat are slow and vessels may be
unable to navigate in shallow waters or coastal areas. The aerial search typically relies on helicopters,
which entail high costs. Moreover, flight operations and rescue missions are dangerous for the crew,
especially under severe weather conditions. Unmanned Aircraft Systems (
UAS
s) will close the gap
and enable fast and safe detection of missing persons and ships.
The challenges of
UAS
usage in maritime
SAR
scenarios lie in the remote control as well as
the real-time exchange of acquired information like sensor data, videos, and images. The former
Drones 2020,4, 16; doi:10.3390/drones4020016 www.mdpi.com/journal/drones

Drones 2020,4, 16 2 of 22
two require low latency and high reliability, whereas the latter one conditions a real-time and
high-throughput data flow. To tackle those challenges, the underlying publication proposes a holistic
communication framework. The solution implements a multi-homing concept by using several
Long Term Evolution (
LTE
) networks in parallel. This multi-link approach aims at mitigating minor
break-ins or total failures of individual links and performing an aggregation to increase the overall
throughput performance.
One major contribution of the underlying publication is the experimental validation in several
unprecedented large-scale experiments, where all measurements and recorded datasets have been
published (c.f. supplementary material at the end of this document). Therefore, the communication
system has been implemented into a 3.6 m wide fixed-wing plane as part of the German national
research project LARUS [
1
]. The implemented LARUS
UAS
for maritime
SAR
missions is the first civil
project of its kind. A video demonstrating the capabilities of the
UAS
can be found in [
2
]. For the
system evaluation, the
UAS
was launched from two airports located close to the coast at the Baltic sea
in Germany and several flights were conducted. A schematic illustration of the scenario and the airport
locations are depicted in Figure 1. The experiments consist of two parts: The first part primarily proofs
feasibility and analyzes and evaluates connectivity as well as aerial maritime
LTE
channel parameters.
The flights imitated realistic
SAR
scenarios and flight
2
accompanied one large-scale rescue simulation
of the German Maritime Search and Rescue Association (German: “Deutsche Gesellschaft zur Rettung
Schiffbrüchiger”, DGzRS). During those flights, real video payload was transferred between
UAS
and
the local ground control and also used for the control of the
UAS
. In the second experiment series,
the maximum performance of the communication was investigated. In order not to influence the video
stream and flight control, a manned flight was conducted.
12°E 12.5°E 13°E 13.5°E 14°E
54.25°N
54.5°N
54.75°N
UAS Coverage
UAS Coverage
Maritime SAR Station
LTE Operator 1 Cell Tower
Airport for SAR UAS
LTE Operator 2 Cell Tower
Remote SAR Coordination Center
Live Video Control
Thermal and Health Monitoring
HD Imaging
Telemetry &
UAS Payload
Figure 1.
Schematic illustration of the Search and Rescue (
SAR
) scenario depicting the Unmanned
Aircraft System (
UAS
) deployment at two different locations. The proposed multi-link approach
leverages public cellular networks to transport live video, telemetry and control, and additional
payload. The two depicted airports were actually used in the experimental validation in the remainder
of this article.
The evaluations of the experiments show that the proposed solution significantly enhances
communication quality. It is found that in the scenarios, conventional methods do not suffice for
reliable connectivity to the aircraft with significantly varying overall availabilities between 68% and
97%. Leveraging multiple
LTE
networks overcomes the limitations of single-link connectivity by

Drones 2020,4, 16 3 of 22
providing an availability of up to 99.8%. Therefore, the approach and experimental data presented in
this work yield a solid contribution to search and rescue drones.
In the following, the underlying work is structured as follows. First, relevant work is discussed
and compared with the underlying approach in Section 2. Section 3describes the system design,
evaluation, and implementations, which were conducted to enable maritime
UAV
-based
SAR
operations. This is followed by a description of the evaluated scenarios and published datasets
(Section 4). Finally, the flight experiments are evaluated and discussed in Sections 5–7. Section 6
highlights the benefits of the underlying multi-link approach.
2. Related Work
The sea provides a tough scenario due to its large search area and seclusion in connection with
heavy wind and severe weather conditions. The need for fast, reliable, and safe exploration defines
major research questions [
3
]. Missions are dangerous for manned vehicles, therefore the usage of
robotics for safe operation is close at hand [
4
]. The concepts for the usage of
UAV
s for missing person
detection [
5
] and drowning prevention [
6
] have been proposed in various publications [
7
]. Besides the
major challenge of the detection, flight operation is another big issue. Even in fully autonomous flight
operation, connectivity to the operator or other
UAS
and aerial traffic participants is necessary to
control the operation and have suitable measures for collision avoidance. Challenges, like the
UAV
outback challenge [
8
], exist as incentives to tackle those challenges in a competition form with a fully
integrated system.
Within the underlying work, we propose a holistic architecture and large-scale evaluation of all
system aspects with the main focus on the air-to-ground communication link. A comparable
UAV
project is the SEAGULL [
9
], which targets maritime surveillance or vessel and shipwrecked tracking.
However, the authors focus more on the person detection and fully autonomous flight operation.
Unfortunately, the communication link is insufficiently described only as “UVH/SATCOM” link and
lacks major details and evaluations. Another similar project is conducted in [
10
]. The authors propose
a Beyond Visual Line of Sight (
BVLOS
) system for very-low level airspace. The communication link
for reliable video and payload transfer consists of one public
LTE
and one wireless LAN in 2.4 GHz
frequency band technology. In their work, the authors evaluate only the communication link in
a ground-based experiment. The maximum achieved range is limited to 1.9 km, which is highly
insufficient for maritime communication.
Enabling reliable and robust connectivity for air-to-ground communication is challenging due
to the harsh environment and represents a key challenge of drone-based applications in disaster
management [
11
]. The study [
12
] of the National Aeronautics and Space Administration (
NASA
)
discusses methods and concepts for reliable
UAV
communication. The authors claim that the concept
under which multiple service provider networks operate at the same time is fundamental and affords
the best reliability, cost performance, and quality of service during the
UAV
’s flight. International
Civil Aviation Organization (
ICAO
) currently considers integrating multi-link communication in
its standards [
13
]. There are several conceptual, analytical and simulation-based studies for aerial
connectivity exist. In [
14
], cellular connectivity for
UAV
s, as used throughout the underlying work,
is described and analyzed in great detail.
Several competing protocols exist for the multi-link implementation. In following, the related
work on combining multiple communications paths or links uses various terminology such as
multi-path, multi-link, or multi-RAT (Radio Access Technology). We have chosen the term “multi-link”
in order to capture both the radio- as well as protocol-related aspects of our work. Performance,
maturity, and applicability between concepts and implementations vary significantly. Within the scope
of this work, Multipath TCP (
MPTCP
) [
15
] is favored, which enables smooth handovers between
different networks and communication links.
MPTCP
resides on the transportation layer and is
transparent for the upper application layers. An overview and a comparison of multi-link protocols
and their features are performed in the survey [
16
]. The comparison is based on Stream Control

Drones 2020,4, 16 4 of 22
Transmission Protocol (
SCTP
) [
17
], various
SCTP
extensions, and
MPTCP
. The study, which also
conducts an empirical performance evaluation, results in favoring
MPTCP
as it provides the broadest
feature set. A general performance evaluation of the benefits of vehicles, which are able to make use of
up to three mobile communication networks, is performed in a preliminary study [
18
]. The proposed
MPTCP
transmits subflow configuration and sequence numbers as part of the Transmission Control
Protocol (
TCP
) header. The Multi-Connection TCP (
MCTCP
) [
19
] proposes contrary behavior and
uses several individual plain
TCP
connections for each network interface. It does not modify
TCP
itself but transmits the additional protocol overhead as part of the payload. Hereby, the protocol is
able to mitigate the influence of middleboxes and firewalls, which may drop
MPTCP
’s
TCP
-header
modifications [
20
]. However, the protocol may behave unfairly to non-multi-link
TCP
connections
as congestion control is uncoupled [
21
]. Other solutions are very application- or use case-specific:
Maximum Multipath TCP (
MMTCP
) [
22
] provides a data center solution which significantly reduces
the download time of short data flows, while at the same time providing high throughput for
long data flows. Multipath UDP (
MPUDP
) [
23
] provides a multi-link transport protocol, based
on User Datagram Protocol (
UDP
), which specializes and optimizes the usage of Virtual Private
Networks (
VPN
s). ScalaNC [
24
] provides a Network Coding enhanced and
UDP
based solution for
multi-link communication. Network coding, also in combination with
MPTCP
[
25
], can improve
performance in scenarios with lossy communication links. However, due to the used cellular (4G/
LTE
)
network, packet loss is not a major issue. Network coding also requires more computational overhead,
which is unavailable on the embedded network platform.
For transmitting video payload over multiple parallel links several approaches exist in literature.
The authors of [
26
] propose quAlity-Driven MultIpath TCP (
ADMIT
), which makes use of the
MPTCP
and applies a Forward Error Correction and rate allocation. Experimental results show that the
algorithm outperforms reference protocols. A similar approach is used by Hayes et al. [
27
] proposing
Dynamic Adaptive Streaming over HTTP (
DASH
). The approach benefits from using a lower video
quality when the data rate is not sufficient. It induces an additional two-second latency on top of the
data transfer and creates additional overhead by using Hypertext Transfer Protocol (
HTTP
)-based
streaming. An improved data offloading is presented in [
28
], exchanging video latency against
optimized data rate usage, which is also non-beneficial for the real-time video stream of the
UAV
.
To solve the issue of uploading time-critical data, a deadline-constrained algorithm for video upload
from vehicles has been proposed in [
29
]. The video is evaluated on traces. In [
30
], an empirical
evaluation is performed on a proprietary multi-link transport layer protocol, but lacks a comparison to
state-of-the-art transport layer protocols. The authors of [
31
] evaluate a
UDP
-based multi-link video
transfer for remote vehicle control using a simulation, but lacks experimental evaluation.
To improve state-of-the-art networking and communication technologies, Google released the
novel Quick UDP Internet Connections (
QUIC
) [
32
] protocol as part of the Chromium web browser [
33
].
QUIC
is
UDP
-based and resides fully in the user-space. The first deployments on YouTube servers
achieved good results and caused a growing interest in the protocol. This popularity led to two
multi-link adoptions of Multipath QUIC (
MPQUIC
). The first implementation by Coninck et al. [
34
]
provides the initial architecture and design for
MPQUIC
. The implementation is compared to
MPTCP
in an emulated static environment. For
HTTP
-based data transfers,
MPQUIC
tends to be slightly faster
than
MPTCP
. However, the scenario seems beneficial to
QUIC
as it is optimized for
HTTP
and
HTTP
/2
traffic. In addition, mobility is not evaluated here. The second implementation of Viernickel et al. [
35
],
which was developed in parallel, is also evaluated in a large-scale emulated experiment. For small
HTTP
/2 downloads (10–100 KB),
MPQUIC
seems to outperform
MPTCP
. For larger downloads
(1 MB), both competitors meet at eye height, at high delay ratios
MPTCP
has a slightly better average
(median) download time. The latter publication also provides a proof of concept by conducting a
real-world experiment using an
LTE
modem and Wireless Local Area Network (
WLAN
) static setup.
Device mobility has not been investigated. Even though both
MPQUIC
solutions provide promising
results for future use in
UAV
communication, the implementations lack maturity and extensive field

Drones 2020,4, 16 5 of 22
tests in comparison to
MPTCP
.
MPQUIC
has only been tested in emulated and static scenarios.
Therefore,
MPTCP
has been used throughout the experiments of this work. A trace-driven emulated
evaluation of
MPQUIC
schedulers is provided in [
36
] and shows promising results of reduced latency.
MPTCP has proven itself in several studies and investigations [
37
]. The authors of [
38
] successfully
evaluate handovers between different networks in mobile scenarios. Simulations [
39
] using the ns-2
network simulator promise 20% increases in throughput in
UAV
communication when using
MPTCP
over ad hoc wireless networks. Different extensions and modifications exist to improve
MPTCP
itself,
the multi-link congestion control, or scheduling. ProgMP [
40
] provides an add-on, which enables the
development of own application or preference aware
MPTCP
schedulers, e.g., a low latency
MPTCP
version [41].
The underlying maritime air-to-ground communication radio properties are described in several
studies. The survey in [
42
] compares channel models especially with regard to their applicability
in
UAV
communication. An accurate analytic description for the maritime air-to-ground links is
provided by [
43
]. A long-range
LTE
communication in a maritime scenario over 180 km using a
multi-cell approach, which is comparable to the public mobile communication networks in this paper,
is performed in [
44
]. The authors have published empiric measurements of the received
LTE
radio
power that enables a detailed channel description. A very detailed channel model has been developed
by Matolak et al. [
45
]. In their work, the authors derive a two-ray channel model for maritime aerial
description, which is founded on the groundwork of the authors of [
46
–
48
]. The model is enriched
and parametrized by a large-scale measurement campaign using an aircraft. This model has been
evaluated in a preliminary study of the authors of this publication in a multi-link scenario [
49
] and
will be validated and referenced throughout the underlying work.
3. Proposed System Design, Architecture and Implementation
Several components and modules are required to enable the
UAS
to operate in
SAR
missions
and stream live video data from the
UAV
to remote operators and stakeholders. Figure 2provides
an overview of the whole multi-link air-to-ground communication system architecture. The core
component of the system is the LARUS aircraft. The
UAV
is equipped with one First-Person View (
FPV
)
camera and one High Definition (
HD
) camera. The
FPV
camera provides a continuous video stream,
which supports the drone operator on the ground in navigating the vehicle. The
HD
camera provides
high-resolution pictures of the sea surface to find missing persons and perform
SAR
tasks. The camera
is stabilized using a gimbal, which enables the rotation and movement of the camera by a remote
operator in order to take pictures of areas of interest. In addition, the
UAV
is equipped with a
monitoring module, which provides telemetry data (position, speed, altitude, etc.) and various
health information (tank filling level, battery voltage, etc.). The
UAV
is equipped with three Sierra
Wireless MC7455
LTE
modems to enable multi-link operation. Two of those modems are assigned
to two public German Mobile Network Operators (
MNO
s), in the following referred to as MNO 1
and MNO 2. The third modem is associated with an
LTE
small cell solution. This small cell solution
supports the public
MNO
in proximity to the airport, where network coverage is not always given
due to buildings and shadowing, and saves data volume during maintenance when the
UAV
is on the
ground. In the future, a long-range
LTE
solution could serve as a drop-in replacement and assist the
public
MNO
. All
UAV
components are connected via an embedded ARM-based computing platform.
The platform runs a Linux operating system and uses an
MPTCP
kernel [
50
], which enables multi-link
communication. Figure 3shows a picture of the LARUS
UAV
plane. The
UAV
has a fixed wingspan of
~3.6 m and a total weight of ~26 kg. With its combustion engine, the
UAV
is able to fly for multiple
hours and long distances. For communication, the public
LTE
antennas are located inside the tail fins
of the aircraft. The right part of Figure 3shows a cross-section of the front of the plane. Here resides
the embedded multi-link platform as well as the dedicated
LTE
modem. Figure 4shows the
UAS
during flight operation over the Baltic Sea.

Drones 2020,4, 16 6 of 22
Air-to-Ground Links LARUS UAV
Modem
Modem
Modem
Embedded
Multilink
Platform
FPV
Camera
HD
Camera
Gimbal
Health-
Monitoring
Local Ground Control
LARUS Dedicated LTE
Public LTE 2
Public LTE 1
Public Internet
Multilink Combiner
Health Management and
Status Reporting
Video Livestreaming
Mission Control
Remote Stakeholders
Remote Stakeholders
Remote Stakeholders
Maritime Rescue
Coordination Center
Rescue Forces
Figure 2. Architecture of the proposed multi-link communication.
Public LTE
antennas in tail fin
Embedded
Multilink Platform
Dedicated LTE
Figure 3.
Illustration of the LARUS Unmanned Aerial Vehicle (
UAV
) showing the location of the
communication hardware in front and tail fin. The
UAV
has a fixed wingspan for 3.6 m and a total
weight of 26 kg. With its combustion, engine it is able to fly for multiple hours. All experiments except
flight 4 were performed with the pictured LARUS UAV. Images: c
2020 LARUS project [1].
Figure 4.
The LARUS
UAV
during start (
left
) and during flight operation over the Baltic Sea near
Ribnitz-Damgarten 2 (right). Images: c
2020 LARUS project [1].
The local ground control station is equipped with an off-the-shelf desktop computer, which also
runs an
MPTCP
Linux kernel [
50
]. Here, the multi-link communication between
UAV
and ground
control terminates. On the application layer runs a video live streaming platform, which makes use of
gstreamer [
51
] for publishing and Open Broadcaster Software Studio (OBS Studio) [
52
] for video mixing.
This video module provides a low latency video of the UAV FPV camera for the local drone operator.
In addition, the video module supports rebroadcasting of the mixed stream via Real-Time Messaging
Protocol (
RTMP
) to remote stakeholders, like rescue forces or the superior Maritime Rescue and Control
Center (
MRCC
). The local ground control station is illustrated in Figure 5. It is located inside the white
van. The dedicated
LTE
is mounted on the rooftop. Inside, the
UAV
can be tracked and controlled via
various monitors. Figure 5also shows an illustration of the LARUS control and health monitoring
Graphical User Interface (
GUI
), where
SAR
missions can be conducted. The screenshot shows the
Sector Search Pattern from flight experiment
3
. All data is made available to external stakeholders via a
unified live video stream, for example, during Peenemünde flight experiment
3
(Final Demonstrator)
rescue forces on a boat set out to save persons out of the water, were able to see live images provided

Drones 2020,4, 16 7 of 22
by the plane’s cameras. In addition, the
MRCC
, which was located several hundred kilometers away,
was able to interact with the mission control.
Dedicated LTE
Monitoring and
Video Stream Mix
Health Status
Monitoring
UAV Tracking
UAV Navigation
& Control
Figure 5.
Illustration of the ground control station, which is located in the white van. The dedicated
LTE
is mounted on the rooftop. Inside, the LARUS aircraft is monitored and the final video stream
mixed for external stakeholders.
During the
UAV
experiments, the video stream was used to navigate and monitor the vehicle.
Maxing out the communication link’s performance would have interfered with the video streaming.
Therefore, an additional manned flight was conducted to assess the maximum key performance
indicators of the system. The plane for the manned flight is illustrated in Figure 6. An ultra-lightweight
plane was used for those experiments. The same hardware (antennas and multi-link platform) being
used in the
UAV
experiments was also used during the manned flight to create comparable results.
Due to the unavailability of space in the back, the antennas were placed at the front window of the
plane, which is contrary to the UAV experiments.
Onboard Live-Monitoring
and Evaluation
Public LTE
antennas
Figure 6.
In order to evaluate maximum performance without interfering video stream and
telemetry and control data flows a manned flight was conducted. The illustration shows the used
ultra-lightweight aircraft and antenna position of Peenemünde flight experiment 4 .
4. Validation Scenarios and Published Datasets
The proposed
UAS
has been evaluated in several long- and short-range flight tests. To complement
and round off the extensive
UAS
measurements a manned validation flight using a plane was
undertaken. All flight tests were conducted at different locations and areas to demonstrate that
the system is universally applicable and does not contain any spatial dependencies. Table 1provides
an overview of all available datasets. Within the scope of this work, all evaluations indicate the
underlying data by referring to the numbering system
1
to
5
as defined in the table. In addition,
Figure 7shows a map and the trajectories of all flights.

Drones 2020,4, 16 8 of 22
Table 1. List of published and evaluated datasets.
Index Location Mission Start Stop
1 Peenemünde Single Link 22 August 2019 07:17:54 22 August 2019 08:19:11
2a Peenemünde Long Range 14 September 2019 09:20:25 14 September 2019 10:04:34
2b Peenemünde SAR Mission 14 September 2019 10:15:25 14 September 2019 10:25:24
3 Peenemünde Final Demonstrator 17 October 2019 11:58:32 17 October 2019 12:58:31
4a Peenemünde Manned Validation Flight 1 17 October 2019 11:58:32 17 October 2019 12:58:31
4b Peenemünde Manned Validation Flight 2 17 October 2019 11:58:32 17 October 2019 12:58:31
5 Ribnitz-Damgarten Long Range 28 August 2019 14:07:58 28 August 2019 15:26:39
In the first evaluation area, the
UAS
was launched at Peenemünde airport (
ICAO
airport code
EDCP). The trajectory of the flight is depicted in Figure 7a. Several flights originated from this location.
The first data set
1
is a medium ranged flight with a maximum distance of 15 km above the island
Greifswalder Oie. The focus of this flight was on hardware evaluation tests, therefore only a single-link
communication was conducted. Data set
2
consists of two parts: In the first part
2a
, a long-range
distance flight in North–West direction was conducted, targeting the island Rügen. The altitude
during the long-range flight was 400 m and the maximum distance between
UAS
and ground station
was approx. 21 km. The flight tests accompanied a
SAR
exercise of the German maritime rescue
forces. Therefore, in the following part
2b
, the drone performed a Creeping Line search pattern
exploring a rectangular area below the plane. Both datasets implement full multi-link communication.
The chronologically following data set
3
contains measurements in the area around Peenemünde
airport. A whole
SAR
scenario was conducted as part of the final project presentation of the LARUS
project. Here, the
UAS
searched along the shore for a missing ship with a person floating on the open
water. Having found the ship the
UAS
performed a Sector Search pattern, which is recognizable by the
triangular-shaped trajectories. In the previous scenarios, the
UAS
communication links were used to
transport and deliver mandatory telemetry, video imaging, and mission-related information. Therefore,
the communication link’s limits could not be assessed without endangering and influencing the flight
and mission performance. To complement and round off previous measurements, one manned
validation experiment was carried out using a lightweight plane
4
. Here, the communication link’s
maximum throughput was quantified as well as network availability and roaming behavior in the
proximity of the Polish–German border.
(a) Peenemünde 1 – 3 (b) Peenemünde 4 (c) Ribnitz-Damgarten 5
Figure 7.
Trajectories of the test flights at two different locations in the Baltic Sea. Flights of dataset
4
were recorded using a manned plane, all other datasets (
1
–
3
and
5
) were conducted using the
proposed SAR UAS (c.f. Figure 3).
The second location where flight experiments were conducted lies close to Ribnitz-Damgarten
(Figure 7c), where the
UAS
was launched from the runway of an airport. The area turned out to be
more challenging in terms of communication in comparison to the first location. After the launch, the
UAS
first needs to overfly the Saaler Bodden, a lagoon-like stretch of water, before reaching the seashore
(see also Figure 1for a large scale map). Moreover, the area is less populated along the flight direction

Drones 2020,4, 16 9 of 22
towards the Danish island Falster. Therefore, the mobile communication network deployment is sparse
in the mentioned area.
5. Analysis of Public LTE Networks in Maritime Airspace
Using public
LTE
networks for aerial and maritime communication is challenging.
MNO
s usually
optimize their
LTE
networks for ground users. Therefore, antennas at the cell tower are tilted
downwards to achieve maximum gains and reduce interference with other cells. In addition, distances
between enhanced NodeB (
eNB
) and User Equipment (
UE
) are larger. This results in worse radio
channel conditions for aerial
LTE
users. Figure 8highlights this issue by comparing the channel quality
indicators of the Peenemünde flight data set
1
to a ground reference measurement. For channel
quality, the
UE
-estimated Signal-to-Interference-plus-Noise Ratio (
SINR
) is analyzed. The received
power is presented in form of the Reference Signal Received Power (
RSRP
). An estimate of the
UE
’s
power consumption is provided by the Transmission Power. The ground reference measurement
was recorded during a drive test in a car on a German highway. The reference measurement uses
the same
LTE
modem, antennas, and
MNO
s that were used in the aerial measurement campaign.
The resulting figure shows the value distribution of each indicator incorporated by the Probability
Density Function (
PDF
). For the aerial test, the
SINR
ranges from
−
10 to 5 dB with an average of
−
2.9 dB. The ground test results in a value range from
−
5 to 25 dB and a mean value of 13.5 dB.
Besides the fact that aerial values are 16.4 dB below ground average,
SINR
values below 0 dB indicate
the worst channel conditions. Communication needs to make use of the most robust modulation
and coding schemes to successfully transmit data. Additionally,
LTE
’s Hybrid Automatic Repeat
Request (
HARQ
), which can recover packets that have not been successfully transmitted, supports
the communication link. However, this results in a lower data rate and higher latency. If bad channel
conditions cannot be fully compensated, side-effects will occur, from sporadic packet losses up to the
whole communication link becoming unavailable. The poor channel quality is also recognizable in
the received power. With a difference of 11.2 dB, the average aerial
RSRP
is significantly below the
ground-based one. The difference is lower in comparison to the
SINR
because that the
RSRP
does
include only the usable signal and no interference power levels. The
UE
uses more energy for aerial
communications in terms of energy efficiency. The aerial data was transferred using an average of
20.4 dBm transmit power, whereas the ground reference transmitted on average 13.0 dBm, which is
7.4 dB less. Due to the limited resource availability in
UAV
s, the more than fivefold increased energy
consumption must be taken into account when dimensioning power supplies.
Figure 9illustrates the main channel quality indicators of the published datasets to provide
an estimate of the channel quality over time. The figure references the same data as the preceding
comparison of Figure 8. The highlighted indicators were recorded and published for each of the
previously described flights. The received power is again represented using the Received Signal
Strength Indicator (
RSSI
) and the
RSRP
, whereas
SINR
and Reference Signal Received Quality (
RSRQ
)
are used for signal quality description. Figure 9underlines the observations from the previous
distributions of Figure 8. The
RSSI
is defined as the total power the
UE
observes in the whole used
frequency band. Therefore, it incorporates signal power as well as noise and interferences. In addition,
the
RSRP
measurements reflect the average linear power of a single reference carrier. Thus, it provides
an estimate of the strength of the usable signal of the network. As it does not include interferences
and noise, it lies clearly below the
RSSI
measurements. In the underlying time-series, the difference
is on average 32 dB.
RSRP
measurements locally range down to nearly
−
100 dBm. Assuming a
typical
UE
receiver noise floor of
−
97 dBm [
53
] underlines the challenges of maritime air-to-ground
communication. The upper subplot shows the signal quality in terms of
RSRQ
and
SINR
. Both show a
strong correlation.

Drones 2020,4, 16 10 of 22
Figure 8.
Challenges of aerial communication: public networks are optimized for ground users and
antennas are usually tilted downwards. In addition to the larger distance between the cell tower
of the serving enhanced NodeB (
eNB
) to User Equipment (
UE
), the experienced channel quality for
UAS
is significantly worse than for ground users. This figure compares the channel quality indicators
Signal-to-Interference-plus-Noise Ratio (
SINR
), Reference Signal Received Power (
RSRP
), and
UE
Transmission Power of a Peenemünde flight test with a reference measurement, which was captured
during a one-hour highway drive using a car. For each indicator, the aerial-based measurements are
worse than ground reference measurements. MNO 1, Data set 1 .
Figure 9.
Time-series evaluation shows the main
LTE
radio channel indicators:
Signal-to-Interference-plus-Noise Ratio (
SINR
) and Reference Signal Received Quality (
RSRQ
)
for channel quality, Reference Signal Received Power (
RSRP
), and Received Signal Strength
Indicator (RSSI) for received power and UE transmission power. MNO 1, Data set 1 .
In order to analyze correlations and model channel conditions more precisely, Figure 10 compares
the
RSRQ
to (a)
RSSI
, (b)
RSRP
, and (c)
SINR
in the form of a scatter plot. Moreover, the ground-based
measurements serve as a comparative reference to the in-flight measurements. The evaluations
underline the bad value range of the channel quality indicators. For
RSSI
, the value range lies
between
−
50 dBm and
−
70 dBm. Data points lie on noisy lines, distinguishable by the used frequency.
Even though the Pearson correlation coefficient (c.f. Table 2) ranges up to
ρ=
0.613 for
LTE
Band
3, a direct derivation of
RSRQ
based only on
RSSI
measurements is not possible. The scatter plot
Figure 10b of
RSRQ
and
RSRP
reveals a linear correlation with the Pearson coefficient being above
ρ≥
0.88 for all used frequency bands. Measurements of the reference signal power allow direct
estimation of channel quality. This is contrary to the ground-based measurements, where nearly no
correlation can be found, which is also confirmed by low correlation coefficients between 0.25 and
0.54. This is attributed to a lot of shadowing by buildings and obstacles, reflections, and multipath
interferences, which occur during drive tests and perform a significant degradation of signal quality.

Drones 2020,4, 16 11 of 22
In summary, this leads to the conclusion that interferences with other
LTE
users and fast fading effects
are neglectable. Channel quality is therefore mainly determined by path loss.
Figure 10.
Scatter plots of in-flight recorded
RSRQ
and
RSSI
(
a
),
RSRP
(
b
), and
SINR
(
c
) measurements
in comparison to a ground reference measurement. Linear correlation between channel indicators
shows that only a few fast fading interferences and noise are present. Compare Table 2for correlation
coefficients. MNO 1 data sets 1 2 3 .
Table 2. Pearson correlation coefficients of RSRQ (c.f. Figure 10).
RSRQ Flight Tests RSRQ Drive Test (Ground)
Band 3 Band 8 Band 20 Band 3 Band 8 Band 20
RSSI 0.613 0.281 0.430 0.070 n/a 0.400
RSRP 0.931 0.938 0.878 0.252 n/a 0.543
SINR 0.933 0.889 0.897 0.560 n/a 0.729
In the next step, the path loss effects are further investigated. To derive channel characteristics
and evaluate signal attenuation, Figure 11 evaluates the received power in dependency of the distance
between
UE
and the currently connected cell (
eNB
). The analysis leverages the received signal power
(
RSRP
) of the three most frequently used
eNB
s in the 800 MHz frequency band (
LTE
Band 20). Different
sectors of each
eNB
were consolidated in the evaluation as the according antennas are located at the
same radio tower. The left subplot (a) illustrates the path loss and power, the right subplot (b) shows a
map of the
eNB
locations and positions, where the
RSRP
samples were recorded. When discussing
aerial communication, free space characteristics are usually assumed. Typically, there are no obstacles
are between sender and receiver: the direct Line of Sight (
LOS
) assumption is valid. This aspect
can be seen by the recorded
RSRP
samples of Cell Identifier (
Cell ID
)1ED4B01 (green color). In the
distance between 9 and 14 km, the signal strength decreases linearly with respect to the logarithmic
distance. The free space path loss can be modeled using the Friis Transmission Equation [
47
] with the
propagation coefficient
γ
. The propagation coefficient has a major impact on the path loss. For 1ED4B01
(green color), a propagation coefficient of
γ≈
2.0 holds true. An approximate Effective Isotropic
Radiated Power (
EIRP
) of 30 dBm is estimated for the illustrated model. With increasing distance,
the
RSRP
measurements diverge from the free space model and form uneven patterns even though the
LOS
condition is given. These effects can be explained using the maritime channel model (see previous
work of the authors, where this model is discussed in detail [
49
,
54
]). In addition to the direct
LOS
path, the maritime channel model takes a secondary, ground-reflected Non-Line of Sight (
NLOS
) path
into account. The superposition of multiple radio propagation paths leads to interferences causing the
divergences from the free space path loss model. The illustrated maritime channel model in Figure 11
uses an estimated antenna height of 100 m and measured
UAV
altitude of 400 m. The model provides
a good approximation of the measured
RSRP
. In the near field, the secondary
NLOS
path is blocked
by ground-based obstacles (e.g., trees, buildings, etc.) and the maritime two-ray model does not
apply. This can be seen in the map subplot especially for
Cell ID
1ED4B01 (green). In the first 14 km,
the NLOS is blocked and free space model applies.

Drones 2020,4, 16 12 of 22
Figure 11.
Received signal strength evaluation of the three most used
LTE
cells of MNO 1 during
Peenemünde flight
2a
. Whereas the left plot (
a
) illustrates samples of the signal strength in dependency
of the
UAS
distance, the right illustration shows the
eNB
positions as well as the location, where samples
were recorded. The signal of the
eNB
with cell id 1ED4B01 follows rural characteristics: there are no
two-ray path interferences, and therefore the signal strength can be described using the free space
model. The other two
eNB
s1BECC01 and 1C9D701 have strong variances. These valleys can be
described using the proposed maritime channel model. MNO 1, data set 2a .
Figure 12 illustrates the connectivity of the
UAV
to the different cells of each
MNO
during the
first half of the Peenemünde long-range flight
2a
. The first column (subplots a,c) indicates the cell
identifier to which the
LTE
modem of the
UAV
is currently connected to. The first five characters
of the
Cell ID
represent the identifier of the
eNB
, the last two characters indicate different sectors or
antennas of the same
eNB
. Within all figures, different antennas were grouped for each
eNB
and
share the same colors. The upper row of the cell identifier plot shows the availability of the MNO.
The availability represents whether the network was able to transport payload data. The detailed
methodology for the determination of the availability will be discussed in detail in the subsequent
section and Figure 13. Within Figure 12, the right column (b,d) shows a map of the locations of each
eNB
. The upper row (a,b) represents MNO 1 evaluations and the lower row (c,d) MNO 2 evaluations.
During the first half hour of the flight, the
UAV
is connected to 21 different
eNB
s of MNO 1 and 25
eNB
s of MNO 2. Throughout the flight, the
UAV
’s
LTE
modem performs 111 handovers between cells
of those
eNB
s, which correspond to an average length of stay of 16.2 s per cell for MNO 1. For MNO 2,
166 cell changes were conducted leading to an even smaller duration of stay of 10.8 s. Handovers in
LTE
are either initiated by the
EPC¸
or by the
UE
. However, the final handover decision remains at the
core network and aims at maintaining and maximizing the user’s Quality of Service (
QoS
) (c.f. also
resource allocation in next-generation broadband wireless access networks [
55
]). Here, it is once
again obvious that the decisions are optimized for ground users: handovers are conducted between
unsuitable cells, which leads to a back-and-forth switching between two cells. This effect can be seen,
e.g., at MNO 1 between cells 1ECD901 (purple) and 1D8C801 (black) (at time of flight 1300 s–1700 s)
or between cells 1A6CF03 (pink),1BECC02 (blue), and 18F9A01 (red) (1000 s–1300 s). Typically, the
decision policies use the UE’s measurement report, which indicates the quality of all cells the UE can
see. In the underlying scenario, typically all cells have unfavorable channel conditions. For future
work, we propose a mobility and maritime channel aware handover decision policy for
UAV
s. Hereby,
handover decisions could be improved significantly. An additional optimization would be to disable
frequency bands above 1 GHz. On a few occasions, the
UE
s were able to detect cells in
LTE
band
1 (2100 MHz) and band 7 (2600 MHz). Those cells typically have more bandwidth and most
MNO
aim at moving users there for improved load balancing. In the underlying maritime scenario, those
handovers failed on a regular basis.

Drones 2020,4, 16 13 of 22
Figure 12.
Evaluation of the
LTE
connectivity of the first part of Peenemünde long range flight. The top
row (subplots
a
and
b
) reflects MNO 1, the bottom row (
c
,
d
) MNO 2. The first column (a,c) indicates
the cell identifier to which the
UAS
is currently connected to. The second column (b,d) shows a map of
the
eNB
locations as well as the trajectory of the
UAV
. The respective color represents the association
with an
eNB
, which may have multiple sectors and/or frequencies, which are distinguishable by the
cell identifier. Data set 2a .

Drones 2020,4, 16 14 of 22
Figure 13.
Evaluation of link availability during long range flight shows the benefits of the proposed
multi-link strategy. Flight data sets 2a 2b 5 .
6. Evaluation of Multi-Link Empowered Link Availability Improvement
The following section shows the benefits of the proposed multi-link strategy in terms of link
quality and availability improvement. Availability within the scope of this section means that the
communication link is able to transmit data between the
UAS
and the ground station. For safe and
reliable flight operations, reliable communication is mandatory at all times. Despite packet loss
occurring only rarely due to
LTE
’s
HARQ
mechanism, the communication link may be unavailable if
there is no available
eNB
to connect to, the signal quality is too bad, or the
LTE
modem is performing
a handshake between two
eNB
s. To assess the system’s performance, the data of long-range flights
in Peenemünde 2 and Ribnitz-Damgarten 5 are analyzed and evaluated. Therefore, after discussing
the benefits of link quality improvement due to the multi-link architecture, the subsequent passage
first describes availability calculation methodology and afterwards evaluates and discusses multi-link
empowered link availability gains.
Figure 14 exemplifies the benefits of the heterogeneous link aggregation to the overall link quality
based on the example of the first half hour of Peenemünde long-range flight
2a
. The figure shows
a time series of the
SINR
signal quality over time for both
MNO
s. Raw samples are plotted in the
background, the bold lines show a post-processed moving average of 10 s. As discussed previously,
SINR
values below 0 dB indicate the worst channel conditions. However, the plot shows that most
of the time when one
MNO
suffers from bad channel quality, the other operator is able to assist.
This effect can be seen for a long duration of several minutes, e.g., during a time of flight between
500 s and 900 s. In general, one can say that the more heterogeneous the links are, the more gain the
multi-link approach yields.
Figure 14.
Benefits of heterogeneous multi-link aggregation: When
SINR
falls below 0 dB,
the communication link may be heavily degraded. The proposed approach boosts performance,
especially when one link is good while the other is bad. Data set 2a .

Drones 2020,4, 16 15 of 22
In the next step, the availability of both MNOs is evaluated. To achieve this, Round Trip Time (
RTT
)
measurements were conducted. Internet Control Message Protocol (
ICMP
) messages were sent over
each communication link targeting the ground control station with a static frequency of 2 Hz and a
fixed packet size of 64 Bytes. This approach allows measuring communication link latency as well as
packet losses while at the same time exposing minimal impact on the link performance. Within the
scope of the underlying evaluation, a link is defined as available, when (a) a minimum of one
ICMP
packet was successfully echoed by the ground control station, and (b) the respective
RTT
of the packet
was below or equal to 1 s. The availability for each link, therefore, refers to the ratio of time of flight in
which the
UAS
was able to communicate with the ground control via that link. The final multi-link
availability results from the combination of individual communication channels: If at least one of the
single links is available, the multi-link is also, if no single link is, the multi link is also unavailable.
Figure 15 shows a time-series example of the link availability evaluation taken from the
Peenemünde long-range flight. The first two rows represent two public German
MNO
s, the bottom
row features the resulting multi-link. Whenever the bars are colored, the communication link is
available. White gaps indicate that no connectivity and no data can be transmitted. Shorter gaps
are typically caused by non-beneficial handovers between different cells and last only a few seconds.
Larger gaps originate from network unavailability and bad channel conditions.
Figure 15.
Example of link availability increase by using multiple public
MNO
s. Shorter outages occur
due to non-seamless handovers between two
LTE
cells, larger outages are caused by general network
unavailability. The multi-link approach enhances overall link availability. Excerpt from Peenemünde
long range flight data set 2a .
The overall increase in availability is provided in the statistical evaluation in Figure 13 for the
multi-link
UAV
scenarios
2a 2b 5
. The multi-link approach yields the highest availability in all
scenarios. For Peenemünde SAR Mission, 99.8% availability was achieved, which is primarily because
the mission was flown in the proximity of the shore, where MNO 1 had a good single link performance
of 97.7%. Nevertheless, in the Peenemünde long-range scenario, the multi-link performed very well
with a similar score of 98.2%. Here, the single link availability of 90.8% of MNO 1 was lower than in
the previous flight. In the Ribnitz-Damgarten scenario, both MNO’s single link performances were
significantly lower than in the Peenemünde scenarios. This is due to the fact that the Ribnitz-Damgarten
site is more rural and less populated and contains fewer surrounding
LTE
coverage. Nevertheless,
the multi-link approach results in the highest availability of 89.3%, which is an increase of 20% for
MNO 1 and 17% for MNO 2. In conclusion, it can be stated that the multi-link approach increases the
availability in all scenarios.
7. Overall Application Layer Performance Evaluation
In the following section, the multi-link approach is assessed from the application layer perspective.
The multi-link aggregation is performed using the
MPTCP
transport player protocol.
MPTCP
works
transparently for the application layer and enables smooth and seamless handovers between different
links and interfaces, e.g., when one link is unavailable.

Drones 2020,4, 16 16 of 22
Figure 16 shows a time-series evaluation of the effective throughput of the
UAV
during
Peenemünde flights
2
. The throughput was recorded at the
UAV
using the open source software
Bandwidth Monitor NG (bwm-ng), which captures the data rates for each interface. Throughout the
experiment, the
MPTCP
scheduler “Lowest Round-Trip-Time First (LRF)” was used. The plot shows
the data rates for the public MNOs 1 and 2 as well as for a local dedicated
LTE
network and the
total payload data rate. The bold lines represent a rolling mean of 5 s of the raw samples (thin lines).
The dedicated
LTE
is a small cell solution located at the airport to allow easy maintenance when the
UAV
is at the airport, where public MNO coverage is not always available—especially in shadowing
of buildings. Due to the transparent nature of
MPTCP
, in the future, this
LTE
small cell can be replaced
by a full long-range
LTE
solution, which could assist as a third aerial data link. During the experiments,
a video stream with an approximate data rate of 500 kbps as well as telemetry and control messages
with approx. 100 kbps (including the previously described
ICMP
packets) were transmitted. The plot
illustrates the smooth handover between the data links, which is enabled by
MPTCP
. When one link is
unavailable, data is outsourced on the other link.
Figure 16.
Evaluation of throughput over time for the Peenemünde flight tests
2
. A live video stream
payload (~500 kbps) and telemetry and control messages (~100 kbps) were continuously streamed from
the
UAS
to the ground.
MPTCP
handles seamless and smooth handovers between different networks.
Data set 2 .
Another important key performance indicator for aerial communication is latency. As previously
described,
ICMP
data packets were used to trace the communication links availability and
RTT
.
Figure 17 shows a time-series evaluation of the RTT measurement (a) as well as a histogram with the
PDF
. The bold lines represent again a moving average of 5 s of the raw data (thin lines). Taking the
logarithmic representation of the y-axis of the time-series plot into account, it can be seen that despite
some spikes latency resides most of the time below 80 ms. Average (median)
RTT
for MNO 1 is
with 45.4 ms comparable to MNO 2’s slightly better performance of 41.3 ms. With 80% percentiles of
58.5 ms (MNO 1) and 56.0 ms (MNO 2), the latency for both public MNO is below 60 ms. However,
when considering the 99th quantile, the upper bounds are 11,223.0 ms for MNO 1 and 6788.0 ms
for MNO 2.
During the
UAV
experiments (all data sets except
4
) the communication links were not maxed
out to avoid interferences on the video stream and telemetry and control data flows. In order to assess
maximum throughput performance, a manned flight was conducted (dataset
5
). Figure 18 presents
the evaluation of the experiment. The left subplot (a) illustrates the throughput over each individual
link as well as the sum of all links. Subplot (b) shows the statistical boxplot evaluation. To evaluate
maximum performance, an iPerf -like set-up was used: over a
TCP
socket randomly generated data
was sent. Whenever sending was possible the data was sent over the socket. The outgoing buffer
was always filled. Again, the bwm-ng tool was used to record the throughput for each interface.

Drones 2020,4, 16 17 of 22
During the experiment, no useful payload (e.g., video stream or telemetry and control data) was
transmitted, which is contrary to the previous experiments. MNO 1 achieved with 10.6 Mbps a higher
throughput than MNO 2 with 6.6 Mbps during the experiments. The peak data rate was 42 Mbps for
the single links, being the rare exception right at the beginning of the flight. The theoretic maximum
achievable throughput in
LTE
uplink with the underlying hardware is at 50 Mbps. The first part
of this manned flight is comparable to the route of Peenemünde long-range flight
2
. Afterwards,
the network performance in direction to the Polish border has been evaluated, where network coverage
of the German
MNO
s was not always given. As the antennas were mounted in flight direction,
improved performance is assumed with an optimized antenna positioning. On the way back, when
approaching Peenemünde airport, the MNO 2
LTE
modem tried to attach to a Polish
MNO
several
times. However, the roaming attempts were unsuccessful. MNO 1 recovered and maintained a good
throughput of ~20 Mbps. Despite being in MNO 2 network coverage, the
LTE
modem of MNO 2 was
unable to directly reattach to the network and pick up service. The flight was completed performing
SAR search patterns similar to Peenmünde flight 2b .
Figure 17.
Evaluation of the latency key-performance indicator based on
ICMP RTT
measurements
over each individual data link. Data sets 2 .
Figure 18.
Maximum application layer throughput during manned flight show
LTE
maritime network
performance. Dataset 4 .
Excluding the gap at the Polish border, the overall throughput performance was very good.
MPTCP
enables a smooth and transparent handover between different communication links. The data
rate is sufficient to transfer high data payload like video streams, high-resolution camera images as
well as telemetry and control data.
8. Conclusions
Within the scope of this paper, a reliable long range multi-link communication system for
unmanned aerial search and rescue missions has been proposed. The proposed system architecture
has been implemented and the communication module leverages multiple
LTE
modems and networks

Drones 2020,4, 16 18 of 22
together with
MPTCP
for multi link aggregation. The system has been evaluated in several flight
tests using an
UAS
in several scenarios and all recorded data sets have been published alongside this
publication. The evaluation of the datasets provides a comparison of the network channel quality in
the air in comparison to ground-based measurements: Aerial conditions are worse than ground-based
conditions as public networks are not optimized for
UAS
. However, it could be shown that channel
models can provide a more accurate path loss estimate due to less shadowing effects in channel
propagation of the
UAS
. By measuring the
RTT
of all communication links, the evaluation has shown
that the proposed multi-link strategy can significantly increase the communication link availability
in maritime scenarios. The multi-link approach also enables smooth handovers between different
networks. This allows seamless streaming of constant bitrate payloads like video streams. In a
supplementary measurement campaign, a manned plane has been used to investigate the maximum
data rate of the system without interfering with the
UAS
’s stability. The experiments underline the
benefits of
MPTCP
: the average and maximum throughput is significantly increased in comparison
to single link scenarios. For future work, satellite-based Internet will be included in the multi-link
maritime scenarios as well as the multi-link approach will be integrated in a multi-link system to be
used for mixed vehicle (ground and aerial) rescue robotics scenarios. In those future scenarios, 5G and
WiFi6 links will also be considered.
Author Contributions: Conceptualization: C.W. and J.T.; methodology: C.W., J.G., and J.T.; software: M.P., P.G.,
F.E., and J.G.; formal analysis, J.G. and J.T.; investigation: M.P., P.G., F.E., F.K., and J.G.; data curation: J.G.; writing:
J.G.; visualization: J.T. and J.G.; supervision and project administration: J.T., C.W. All authors have read and
agreed to the published version of the manuscript.
Funding:
This work has been supported by German Federal Ministry of Education and Research (BMBF)
for the projects LARUS (Supporting Maritime Search and Rescue Missions with Unmanned Aircraft Systems,
13N14133) and A–DRZ (Establishment of the German Rescue Robotics Center, 13N14857) as well as the Deutsche
Forschungsgemeinschaft (DFG) within the Collaborative Research Center SFB 876 “Providing Information by
Resource–Constrained Analysis”, projects A4 and B4.
Acknowledgments:
We would like to thank all partners of the LARUS project for their excellent cooperation,
in the context of the experiments especially Thomas Lübcke and the voluntary workers of the DGzRS for their
contribution to the SAR experiments, Michael Schmidt and Stefan Flemming (HAVS) for provisioning of the UAS
and implementation of the communication equipment inside of the plane; Thomas Lamla for the airport access;
Robert Falkenberg (TU Dortmund), Bundesnetzagentur and Telekom Deutschland for providing the SAR BOS
LTE frequency for the dedicated LTE network. Veronika Pillmann for her support in grammar and spell checking.
Conflicts of Interest: The authors declare no conflict of interest.
Abbreviations
The following abbreviations are used in this manuscript:
ADMIT quAlity-Driven MultIpath TCP
BVLOS Beyond Visual Line of Sight
Cell ID Cell Identifier
DASH Dynamic Adaptive Streaming over HTTP
EIRP Effective Isotropic Radiated Power
eNB enhanced NodeB
FPV First-Person View
GUI Graphical User Interface
HARQ Hybrid Automatic Repeat Request
HD High Definition
HTTP Hypertext Transfer Protocol
ICAO International Civil Aviation Organization
ICMP Internet Control Message Protocol
LOS Line of Sight
LTE Long Term Evolution
MCTCP Multi-Connection TCP
MMTCP Maximum Multipath TCP
MNO Mobile Network Operator
MPQUIC Multipath QUIC
MPTCP Multipath TCP
MPUDP Multipath UDP
MRCC Maritime Rescue and Control Center
NASA National Aeronautics and Space Administration

Drones 2020,4, 16 19 of 22
NLOS Non-Line of Sight
PDF Probability Density Function
QoS Quality of Service
QUIC Quick UDP Internet Connections
RSRP Reference Signal Received Power
RSRQ Reference Signal Received Quality
RSSI Received Signal Strength Indicator
RTMP Real-Time Messaging Protocol
RTT Round Trip Time
SAR Search and Rescue
SCTP Stream Control Transmission Protocol
SINR Signal-to-Interference-plus-Noise Ratio
TCP Transmission Control Protocol
UAS Unmanned Aircraft System
UAV Unmanned Aerial Vehicle
UDP User Datagram Protocol
UE User Equipment
VPN Virtual Private Network
WLAN Wireless Local Area Network
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Video of the LARUS Project. 2020 Available online: https://www.youtube.com/watch?v=kJ9ABk8Rr4M
(accessed on 10 March 2020).
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