=Paper=
{{Paper
|id=Vol-2815/CERC2020_paper18
|storemode=property
|title=A Close Look on the Corona Impact on Surveillance Radar Channel Loads
|pdfUrl=https://ceur-ws.org/Vol-2815/CERC2020_paper18.pdf
|volume=Vol-2815
|authors=Roman Raekow,Michael Kuhn,Bernd-Ludwig Wenning
|dblpUrl=https://dblp.org/rec/conf/cerc/Raekow0W20
}}
==A Close Look on the Corona Impact on Surveillance Radar Channel Loads==
https://ceur-ws.org/Vol-2815/CERC2020_paper18.pdf
COVID-19 Research and Smart Healthcare
A Close Look on the Corona Impact on
Surveillance Radar Channel Loads
Roman Raekow1,2 , Michael Kuhn2 , and Bernd-Ludwig Wenning3
1
Deutsche Flugsicherung GmbH, Langen, Germany roman.raekow@dfs.de
2
h da Hochschule Darmstadt, Germany michael.kuhn@h-da.de
3
CIT Cork Institute of Technology, Cork, Ireland
BerndLudwig.Wenning@cit.ie
Abstract. Civil aviation surveillance is carried out on two radio chan-
nels and has seen a growing demand in the last decades. Not only that
more and more applications have been added to the channels but the
number of flights had been growing constantly. This was true until the
corona crisis almost brought the air traffic over Europe to a standstill
and by mid of April 2020 the number of flights dropped to around 12%
compared to the year before [3]. In this paper the consequences for the
civil aviation surveillance channels and the success rates for telegrams
on those channels are discussed.
Keywords: Surveillance Radar · Mode-S · ADS-B · Civil Aviation ·
COVID-19 · Corona
1 Background
The world’s first air traffic control tower on Croydon Airport is celebrating
its 100th anniversary this year. From the very beginning, air traffic controllers
needed to be aware of the positions of the surrounding aircraft in order to sep-
arate them safely. While the air traffic controller of Croydon Airport queried
their pilots to state their positions, modern surveillance is nowadays able to
retrieve a wide range of information from aircraft and can therefore deliver an
accurate air situation with high update rates. This technical improvement allows
more aircraft to manoeuvre in a dense airspace while maintaining a high level
of safety. Radar stations send out interrogations on a 1030 MHz channel and
receive replies from the aircraft that are sent back on a 1090 MHz channel. The
most common setup is to have a grid of secondary surveillance radar stations
that query surrounding aircraft for their altitude, their ID and further necessary
information needed by the air traffic control. The position is calculated by the
round-trip time of the signal and the angle of the radar-station under which the
interrogation was carried out. Aircraft equipped with a transponder receive these
requests and answer on the 1090 MHz channel with the queried data. Besides
that, there are other applications that are using these channels, for example the
Traffic Collision Avoidance System (TCAS) that is used by aircraft to query
the surrounding planes in order to be aware of possible unintended approaches.
Copyright © 2020 for this paper by its authors.
Use permitted under Creative Commons License 293 CERC 2020
Attribution 4.0 International (CC BY 4.0).
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Another common application is Automatic Dependent Surveillance Broadcast
(ADS-B) that is using the aircraft transponder to send out information about
the aircraft in a pseudo-spontaneous manner without an interrogation on the
1030 MHz channel. ADS-B has seen a growing popularity in the last years and
will soon be mandatory in the European airspace.
All these applications are carried out in parallel on the two surveillance radar
channels that are used in civil aviation. Over the last years the need to measure
the load of these channels has become more and more important for the air
navigation service providers (ANSP) around the world. [10, 2, 7]
1.1 Channels
The communication is carried out on two channels, 1030 MHz and 1090 MHz,
that each have their dedicated purpose:
The 1030 MHz channel is used for interrogations. These interrogations can
be one of four possible protocols, that either query all surrounding aircraft or
interrogate single aircraft for specific information. These queries are either sent
out by secondary surveillance radars or TCAS equipped aircraft [1]. However,
this report focuses on the the 1090 MHz channel only.
The 1090 MHz channel is used for the replies to interrogations sent through
the 1030 MHz channel. It is usually more crowded than the 1030 MHz channel,
since a single interrogation can result in multiple replies. Additionally, some
applications like ADS-B only use the 1090 MHz channel. The information is
transmitted using three types of telegrams:
Mode A/C Reply is the reply to an A or a C interrogation by a radar-station,
respectively. It can either return the altitude (Mode-C) or a flight-id of the
responding aircraft (Mode-A). It encodes a four-digit octal number using 12 Bit.
Together with the necessary framing pulses it has a duration of 20.3 µs on the
channel.
Mode-S Short Reply is one of the possible reply types for Mode-S interrogations.
This telegram is also used by TCAS and transports small portions of data. It
consists of a preamble, a data field and a checksum field. The short reply lasts
64 µs on the channel and has a payload of 56 Bit.
Mode-S Long Reply is the second type of Mode-S replies. This Extended Length
Message (ELM) is used to retrieve more detailed data from the aircraft. Its
structure is the same as Mode-S Short Replies but it has a longer payload of
112 Bit and has a duration 120 µs on the channel.
To measure on that channel, a measurement system had been created that
uses affordable hardware and is able to monitor the receivable traffic on a time
span of various days [12]. This system does now allow a comparisons of the
channel loads, prior and during the corona Crisis
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A Close Look on the Corona Impact on Surveillance Radar Channel Loads 3
2 Measurement System and Setup
The system setup as described in our previous work [12] consists of a standard
receiver that is used for multiple applications by ANSP. A software defined radio
(SDR) sends well-known telegrams to the receiver by coupling the telegrams into
the radio frequency (RF) channel on the input of the receiver.
Antenna
trigger send
Tx Coupler ATC
PC SDR RF
USB Receiver
LAN
receive
Fig. 1. The principle of the measurement system
A standard PC is used to trigger the SDR and receive the data from the
mixture of the test telegrams and the real RF environment as shown in figure 1.
This data is used to create a database of the success rates of the test telegrams in
the current environment. The results are recorded in a continuous manner con-
taining the rates of successfully decoded telegrams and the environment under
which the data has been recorded. This approach has already been described
in previous works [11, 15] but the advantage of using a SDR is to be able to
adaptively change the constellation of telegram-types, rates and levels with re-
spect to the current RF environment and therefore be able to gain knowledge
about lesser known telegram constellations when environmental changes on the
channel are detected.
This setup is under continuous improvement and will be expanded through-
out the work on this research. Currently a team of students from Hochschule
Darmstadt (h da) is working on an even more cost-efficient approach which
shall be achieved by the deployment of open-source tools in combination with
cheaper hardware (see Section 4).
Figure 2 indicates the measurement location in Germany in the area of Frank-
furt International Airport (EDDF). The system was using a 90° segment antenna
heading north-west. It is one of the most crowded areas in the German airspace
and thus features high channel loads.
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Fig. 2. An indication of the measurement location, and all flights between 14th and
21st April in the area of Frankfurt.
3 Results
The results shown in this paper have been recorded in two time windows. The
first measurement session has been carried out from the 5th to the 12th of Febru-
ary 2020. The second session was carried out from the 15th to the 25th of April
2020. During this period the number of telegrams and the success rate have been
recorded together with a wide range of additional information like the number
of planes in view and the types of telegrams that were seen in each time frame.
3.1 Time Variance
The number of receivable telegrams varies very strongly throughout each day
and shows strong impacts on the channel. There is a clear recurring behaviour
in Figure 3 over each day. The communication increases as soon as Frankfurt
airport opens. At the beginning of each day there is a small peak that is followed
by a little drop. That is when the intercontinental flights arrive in Frankfurt in
the early morning. The second and largest peak of the day is around 6am to
8am local time where most of the departures are taking place. There is also a
weekly dependency on the traffic, on Friday the 7th of February the ”departure
peak” is the largest in the entire record as this is usually one of the busiest days
of the week.
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Decodable telegrams (30min avg.)
4000
Pkts/s
2000
0
05 06 07 08 09 10 11 12
Feb
2020
Number of planes (30min sum)
No. Planes
40
20
0
06 07 08 09 10 11 12
Feb
2020
time
Fig. 3. The number of telegrams and planes over several days in February
3.2 Special Events and the Corona-Impact
In Figure 3 one can also see that special events do have a direct impact on the
channel utilisation. There is a sharp drop in the traffic from noon on the 8th of
February until the 11th . This drop was caused by storm Ciara that hit Germany
during that time and led to a large number of cancelled flights. On the 12th of
February the traffic is back to a normal level causing a equally normal amount of
packets on the channel. This is one of the strongest declines since the outbreak
of the Eyjafjallajökull in April 2010 [4, 8] where for a period of 4 days almost
100% of the flights were cancelled. However, this drop in flights was very soon
surpassed by the lockdown due to the corona crisis [5].
Figure 4 shows the traffic during a week in April 2020, where most of the
flights were cancelled due to the shutdowns associated with the corona virus. The
figure displays the same axis as Figure 3 but with data recorded in April 2020.
It shows that almost all of the characteristics have changed. Not only has the
total amount of packets per second dropped significantly, but also the day and
night periods differ only very slightly and due to the low number of aircraft the
graph is very unstable. As shown in Table 1 the decrease of communication on
the 1090 MHz channel is very obvious. While the minimum of packets per second
has not changed significantly due to low numbers of flights in the night during
the observation period in February, the maximum in April reaches only around
32% compared to February. The mean over the entire inspection period drops to
around 23% compared to pre-corona times. The distribution of the telegrams has
also changed completely as there is only a very small difference between daytime
and nightly traffic which can be seen in the low standard deviation (std. dev.).
One can see the strong relationship between the actual number of flights and
the telegrams that were received on that channel. The recording of our data is
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Decodable telegrams (30min avg.)
1000
Pkts/s
500
16 17 18 19 20 21
Apr
2020
Number of planes (30min sum)
10
No. Planes
5
0
16 17 18 19 20 21 22
Apr
2020
time
Fig. 4. The number of telegrams and planes over several days in April
supported by the observations from the opensky network, a research project
that enables full exploration on MODE-S and ADS-B data [13].
The data from the network is gained from a distributed set of sensors that
listen on the 1090 MHz channel and feed decodable telegrams to a central server.
Due to ADS-B the network is able to monitor a certain amount of flights and
track their movement in the airspace. Currently, more than 60% of all flights
over Europe are equipped with ADS-B [14]. It will become mandatory for all
aircraft with a maximum takeoff mass (MTOM) greater than 5700 kg or a max-
imum airspeed capability greater than 250 knots by end of October 2025 [6].
Overall, this already gives a valued estimate from a different source to prove the
measurements from the described setup. The figures show a strong correlation of
aircraft identified by the opensky network from or to Frankfurt international
airport (EDDF) to the number of telegrams detected by the measurement setup.
While the number of telegrams increases by a factor of 90 between day and night,
there are approximately 80 times more flights during the day and none during
the night. Eventually, this may be caused by other aircraft not heading to nor
departing from EDDF. However, this correlation needs to be investigated, as it
could also imply a saturation on the channel not allowing to decode all telegrams
during the day peak anymore.
Table 1. Comparison of the datasets
February April Relative
min 54 Pkt/s 62 Pkt/s 114%
max 4863 Pkt/s 1575 Pkt/s 32%
mean 2223 Pkt/s 525 Pkt/s 23%
std. dev. 1262 Pkt/s 258 Pkt/s 20%
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The corona lockdown gives the opportunity to gain new insights into the
surveillance radar channels as only the number of planes has changed in this crisis
whereas other parameters like the number of radar stations and interrogation
pattern kept unchanged. The corona dataset from April allows a comparison of
the impact of the number of aircraft in an otherwise unchanged environment.
The contribution of the planes to the decodable traffic on the channel can be
calculated by the number of received telegrams divided by the number of planes
which gives the rate packets per aircraft.
In February the maximum amount of packets per second (fpkt ) as an average
during one hour was fpkt = 4863/s. In this hour 84 planes were heading to or
departing from Frankfurt (fF RA = 84/h), while in April the maximum average
of packets per second was fpkt = 1575/s with fF RA = 16/h. So, in February the
system was able to detect ≈ 2.08 · 105 P kt/P lane while in April a maximum of
≈ 3.54 · 105 P kts/P lane where received.
In the high traffic environment the contribution to the number of decodable
telegrams per plane is much lower than in the low traffic environment. However,
this stands in strong contradiction to the fact that all planes are interrogated
in the same manner. Additionally, the TCAS system that queries surrounding
planes for their positions should add an increasing amount of telegrams per
planes the denser the airspace is being used.
The number of packets per second is not growing linearly with an increasing
number of aircraft on the surveillance radar channels.
3.3 Channel Load
In order to calculate the impact of traffic on the 1090 MHz channel it is necessary
to inspect the decoding probability during changing channel loads. However, the
system is only able to see the channel throughput (T pCh ) on the receiver side
and does not know the real amount of telegrams that were transmitted on the
channel.
The channel throughput equates to
1
T pCh = · (NAC · TAC + NSS · TSS + NSL · TSL ) (1)
t∆
This rate is calculated by the number of decodeable telegrams Nx of each type
of telegram x ∈ {M ode A/C, M ode S Short, M ode S Long} times the duration
these telegrams occupy the channel Tx (see also section 1.1) divided by the time
of observation t∆ . A value of T pCh = 1 would refer to a 100% channel usage
where the channel is permanently occupied.
Figure 5 shows the channel throughput for changing numbers of aircraft
that were in sight during the 6th of February and the 16th of April. Each point
represents a 15 minute time interval throughout the day. With growing traffic,
the channel usage is increasing very fast and varies between 0.10 and 0.35 for
the majority of the time. During that time 50 to 90 aircraft where sight for the
February data set. In this region it seems like there is already some saturation on
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Channel Throughput vs. Number of Transponders (15min avg.)
February
0.35 April
0.30
Channel Throughput T pCh
0.25
0.20
0.15
0.10
0.05
0.00
0 20 40 60 80
No. of deteced Transponders
Fig. 5. The channel throughput on two days in February and April
the channel as the T pCh increases faster than the number of planes. This topic
needs further inspection in the future. However, this implies that the channel
is already near or beyond its maximum throughput. The decodable channel
throughput of around 0.30 is already more than the maximum that could for
example be achieved with the ALOHA access protocol where the maximum is
reached around 0.18 [9]. In contrast, the channel access on the 1090 MHz channel
is a mixture of a pseudo-random access by individual aircraft (e.g. ADS-B and
TCAS) plus a mixture of scheduled access like Mode-S and Mode A/C replies
to multiple surrounding radar stations that can be scheduled and adapted in a
coordinated manner by the air navigation service provider.
Each of these applications have measures to reduce the channel load: For ex-
ample Mode-S radar stations can coordinate the interrogations and interchange
data for individual aircraft in order to reduce interrogations of the aircraft.
Additionally, there are measures to reduce the impact of the spontaneous trans-
mitted packets. For example, TCAS is reducing the transmission power for the
interrogations, if a high number of surrounding planes is detected thus reduc-
ing the range of interrogations and the number of replies. The higher decodable
throughput rate shows that these measures have a valuable effect on the channel
performance and lead to a channel load that is higher than a random access
channel would be.
Figure 6 shows the number of telegrams per identified transponder for the
February and April data set. This is calculated by the number of identified
aircraft transponder IDs divided by the number of telegrams per second. This
is done because it is not always possible to identify the sending aircraft only by
listening to its replies. Therefore, only individual aircraft IDs have been used to
calculate the amount of aircraft even though it might not have been possible to
determine their position or altitude during the reception. Figure 6 clearly states
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Packets per Aircraft (30min avg.)
50
40
30
Pkts per Aircraft
00:00 03:00 06:00 09:00 12:00 15:00 18:00 21:00
06-Feb
time
100
75
50
25
0
00:00 03:00 06:00 09:00 12:00 15:00 18:00 21:00
16-Apr
time
Fig. 6. The number of telegrams per plane.
that in average there were less telegrams per plane received in February with a
high amount of flights.
There are several possible reasons why this is the case. As of yet, the recorded
data is not sufficient to answer this completely but together with the lost test
telegrams it stands to reason that more and more telegrams are not decodeable as
the channel is already close to its maximum throughput capability. To determine
the reason for this behaviour further research is necessary. However, it can be
observed that the number of decodable messages doesn’t increase linearly with
the number of planes. This implies that the channel is crowded and telegrams
will be lost due to collisions. The more crowded the airspace the lower the rate
of detected telegrams per aircraft. The details of these relations are scope of
our future research where we will investigate the rate between successfully and
falsely decoded telegrams with respect to the channel load.
3.4 Training samples
Training samples in the form of test telegrams play a key role in these measure-
ments. We assume that the distortions, interference and other disruptions have
the same effect on natural telegrams as they have on the test telegrams. So test
telegrams are used in order to gain and improve the knowledge of the charac-
teristics on that channel. The test telegrams are continuously injected into the
channel with changing power levels (see section 2).
It is therefore possible to see the impact of changing radio environments on
the test telegrams with changing levels. Figure 7 shows the median of the success
rate for a single day in the February dataset. The figure clearly shows the strong
inverse proportionality. The higher the number of telegrams per second the lower
the chance for the test telegrams to survive. For this chart, only telegrams with
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100
4000
90
80
Test-Pkt Success/%
3000
70
Pkt/s
2000 60
50
1000
40
30
0
00:00 03:00 06:00 09:00 12:00 15:00 18:00 21:00
06-Feb Total Packets
time Test-Packets <-80dBm
Fig. 7. Success rate and packet load on 6th of February
levels around -80 dBm have been taken into account which is the approximate
average of all received telegrams.
Figure 8 shows a similar result for the April dataset but the overall success
rate is much higher than in February. The success rates throughout the day are
around the values that were achieved during the night in February.
Figure 9 is showing the success rate for the 6th of February and indicates that
the level of the test telegrams plays a crucial role in the success rate. The graph
shows three lines throughout the day. The light-green line shows the average
success rate for telegrams with high levels. It shows that the high levelled test
telegrams hardly suffer from the increasing communication on the channel (refer
to Figure 7) but even these high levelled telegrams suffer from that communi-
cation and drop from 100% to around 90% during the day. For lower levelled
telegrams (sl < −80 dBm) the success rate is not only in general worse, but
the impact of the radio environment is significantly stronger than for the high
levelled telegrams.
4 Conclusion and Future improvements
As the described setup looks promising, a team of students from Hochschule
Darmstadt (h da) is currently working on an even more reduced measurement
system. It uses a Realtek SDR RTL2832U based system that was designed for
Digital Video Broadcasting–Terrestrial (DVB-T) reception. It is able to decode
the 1090 MHz packets and transport them to a PC via USB Interface. A wide
range of Open-Source software is already available for that. The test data gen-
erator is realised with an Adalm Pluto® SDR by Analog Devices. This SDR
evaluation board is able to send on the 1090 MHz channel and synthesise the
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96
900
94
800
92
Test-Pkt Success/%
700
90
600
Pkt/s
88
500
86
400
84
300
82
200
00:00 03:00 06:00 09:00 12:00 15:00 18:00 21:00
16-Apr Total Packets
time Test-Packets <-80dBm
Fig. 8. Success rate and packet load on 16th of April
Mode-S Test-Telegrams in Radio Environment (30min median)
100
90
80
Test-Pkt Success/%
70
60
50
40
30
00:00 03:00 06:00 09:00 12:00 15:00 18:00 21:00
06-Feb
Test Levels > -80dBm
time Test Levels < -80dBm
Test Mean
Fig. 9. Success rate for different levels on 6th of February
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needed test telegrams. These devices are very affordable and can be used out
of the box to generate the test data on the 1090 MHz channel. Together with a
standard PC this setup should be sufficient to measure on the channels. As the
losses of the receiver shall be estimated by machine learning, the impact of de-
coding capabilities are expected to be relatively low as long as a reasonable rate
of the traffic can be received. The quantification of the losses and in a second
step the extrapolation to the real amount of data should be possible with this
equipment just with the professional Air Traffic Control receiver.
The comparison between the two approaches will be further investigated. If
it turns out that the RTL2832U and the Adalm Pluto® can be used to create
a semi-supervised data generator for a machine learning based correction, the
system can be set up at multiple locations over Germany in order to create a
wide range of data with changing characteristics. This might also lead to the
conclusion, whether there is a need for detailed monitoring with a high spatial
resolution or if there are little advantages for such a distributed measurement
network.
This data could then be joined with the data from the opensky-network
and used to create a large scale radio field monitor. Overall, the entire com-
munication structure on the channel has changed since the corona lock-down
stopped the majority of flights over Europe. This involuntarily allows a view
on the surveillance radar channels and the communication that could not be
expected before.
The described measurement setup delivers results that strongly relate to
the results of the opensky network and the reported decline of flights by the
European authorities [5] but also allows new insights on the number of telegrams
per aircraft and the saturation on the channels. These new insights do also raise
new questions that need to be investigated in future works.
The low number of aircraft have a significantly higher probability to success-
fully transmit their results. As the number of telegrams dropped the channel is
less occupied. This shows that the effects of interference and disruptions decline
as the number of aircraft in the surrounding airspace is reduced.
The measurement system is able to show the effects on the surveillance radar
channel in total and can give new and deeper insights into the communication
on this channel. It has been shown that the number of aircraft does not linearly
correspond to the traffic on the surveillance radar channels, thus a measurement
and observation system is necessary to assess the channels’ states and the re-
maining capacity. This is one crucial part to maintain a secure surveillance in
civil aviation that will hopefully recover to its old strength very soon.
Acknowledgement
The authors would like to thank Deutsche Flugsicherung GmbH for supporting
this work.
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