IN DEGREE PROJECT INFORMATION AND COMMUNICATION TECHNOLOGY,SECOND CYCLE, 30 CREDITS

, STOCKHOLM SWEDEN 2018

Aircraft to Aircraft Connectivity Analysis

VASILEIOS MEGAS

KTH ROYAL INSTITUTE OF TECHNOLOGYSCHOOL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCE

Aircraft to Aircraft Connectivity Analysis

VASILEIOS MEGAS

Master’s Degree ProjectStockholm, Sweden 2018

TRITA-EECS-EX-2018:713

Abstract

It is very common to feel the need to be connected, especially in cases wherewe are idle, such as in airplanes. Aircraft connectivity has already been im-plemented by using direct air-to-ground communication and satellite communi-cation. However, since the current solutions are not satisfying, more ways toprovide connectivity to aircraft are being researched. One solution is to useaircraft as relay nodes and forward signals to aircraft which are out of range ofthe ground base stations, creating an ad-hoc network.

This thesis aims in evaluating the performance of such networks over theNorth Atlantic ocean in terms of connectivity and achievable data rate by meansof simulations. First, an aircraft mobility model is created by analyzing pastflight data. Then, the topology of the network is created with the purposeof maximizing the number of connected aircraft and minimizing interference.Finally, the available bandwidth is allocated to all connected aircraft in a max-min way. The simulations were repeated to evaluate the impact of: threshold indata rates to assume connectivity, maximum number of connections per aircraft,maximum antenna steering angle, beamwidth and bandwidth.

Our results show that aircraft to aircraft connectivity is feasible if all airlinescooperate, and more than 80% of the aircraft can find a path to a base station,when the aircraft density is higher than 1 aircraft per approximately 150000km2, which is 88% of a 24 hours period. The median data rate of the connectedaircraft, achieved in the middle of the ocean is limited to 25 Mbps with a 20MHz bandwidth, which is not high enough to provide access to demandingapplications, such as video streaming. Increasing the bandwidth to 200 MHzcan increase the data rate to more than 184 Mbps, for 50% of the connectedaircraft, which surpasses the performance of current satellite communicationsystems.

Sammanfattning

Det ar ganska vanligt att kanna behovet av att sammankopplas, sarskilt i falldar vi ar inaktiva, exempelvis i flygplan. Flygplansanslutning har redan imple-menterats genom att utnyttja direkt luft till markkommunikation och satellit-kommunikation. Men eftersom de befintliga losningarna inte ar tillfredsstallande,undersoks fler satt att tillhandahalla anslutning till flygplan. En losning ar attanvanda flygplan som rela knutpunkter och framatsignaler till flygplan som lig-ger utanfor markbasen, skapar ett adhocnatverk.

Den har avhandlingen syftar till att utvardera prestanda for sadana natverkover Nordatlanten nar det galler anslutning och uppnaelig datahastighet ge-nom simuleringar. For det forsta frambringas en flygplansmobilitet genom attanalysera tidigare flygdata. Darefter astadkommas natets topologi med syftetatt maximera antalet anslutna flygplan och minimera storningar. Slutligen till-delas den tillgangliga bandbredden till alla anslutna flygplan pa maximal vag.Simuleringarna upprepas for att utvardera effekten av: troskeln i datahastighe-ter for att mottaga anslutning, maximalt antal anslutningar per flygplan, maxantennstyrvinkel, stralbredd och bandbredd.

Vara resultat visar att flygplan till flygplansforbindelser ar genomforbartom alla flygbolag samarbetar och mer an 80% av flygplanet kan sammankoppla,nar flygplansdensiteten ar hogre an 1 flygplan per ungefar 150000 km2. Mediandatahastigheten uppnadd i mitten av havet ar begransad till 25 Mbps med en 20MHz bandbredd, vilket inte ar tillrackligt hog for att bringa tillgang till kravandeapplikationer, sasom videostreaming. Okning av bandbredd till 200 MHz kan okadatahastigheten mer an 184 Mbps, for 50% av de anslutna flygplanen, vilketovertraffar prestanda for de nuvarande satellitkommunikationssystemen.

Acknowledgment

First of all, I want to thank KTH and the ICARO-EU project for giving methe chance to work on this thesis. I would also like to thank my supervisors, Dr.Cicek Cavdar, Dr. Mustafa Ozger and Dr. Michal Vondra whose suggestionsand comments were vital for the project.

I would also like to acknowledge Airbus Defence and Space for hosting thisthesis at their site in Munich. More specifically, Dr. Dominic Schupke andSandra Hofmann, who provided me with guidance and knowledge on this topic.

Finally, I want to thank my parents for their full time support throughoutall the years of my life.

Contents

1 Introduction 11.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.2 Ad-Hoc Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . 31.3 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51.4 Problem Definition and Contribution . . . . . . . . . . . . . . . . 61.5 Organization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

2 Communication Parameters 102.1 Frequency Bands . . . . . . . . . . . . . . . . . . . . . . . . . . . 102.2 Maximum Line of Sight Distance . . . . . . . . . . . . . . . . . . 112.3 Modulations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122.4 Receiver Noise . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122.5 Path Loss . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 142.6 Antennas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

2.6.1 Antenna Gain and Size . . . . . . . . . . . . . . . . . . . 152.6.2 Placement . . . . . . . . . . . . . . . . . . . . . . . . . . . 162.6.3 Connections Per Aircraft . . . . . . . . . . . . . . . . . . 172.6.4 Maximum Steering Angle . . . . . . . . . . . . . . . . . . 17

2.7 Interference . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 182.8 Assumptions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

3 System Model 203.1 North Atlantic Tracks . . . . . . . . . . . . . . . . . . . . . . . . 203.2 Aircraft Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 213.3 Aircraft Generation and Mobility Model . . . . . . . . . . . . . . 213.4 Base Station Positions . . . . . . . . . . . . . . . . . . . . . . . . 24

4 Algorithm Design 254.1 Topology formation . . . . . . . . . . . . . . . . . . . . . . . . . . 25

4.1.1 Available Links Between Aircraft . . . . . . . . . . . . . . 264.1.2 Interference Model . . . . . . . . . . . . . . . . . . . . . . 274.1.3 Link Selection . . . . . . . . . . . . . . . . . . . . . . . . . 28

4.2 Bandwidth Allocation . . . . . . . . . . . . . . . . . . . . . . . . 294.2.1 Maximum Flow . . . . . . . . . . . . . . . . . . . . . . . . 294.2.2 Maximum Flow With Node Demands . . . . . . . . . . . 31

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5 Results 365.1 General Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . 365.2 Data Rate Threshold . . . . . . . . . . . . . . . . . . . . . . . . . 395.3 Maximum Number of Connections Per Aircraft . . . . . . . . . . 405.4 Antenna Steering Capability . . . . . . . . . . . . . . . . . . . . . 415.5 Beamwidth . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 415.6 Closed Network of Aircraft . . . . . . . . . . . . . . . . . . . . . 435.7 Theoretical Ideal Case . . . . . . . . . . . . . . . . . . . . . . . . 445.8 Increased Bandwidth and Comparison With SATCOM . . . . . . 44

6 Conclusions and Future Work 47

List of Figures

1.1 Air-to-Ground Communication methods. . . . . . . . . . . . . . . 31.2 Ad Hoc network in different moments. . . . . . . . . . . . . . . . 4

2.1 Specific attenuation due to atmospheric gases. . . . . . . . . . . . 112.2 Maximum line of sight distance. . . . . . . . . . . . . . . . . . . . 122.3 Achievable data rates for different SINR values. . . . . . . . . . . 132.4 Attenuation due to path loss and gases comparison. . . . . . . . 142.5 Maximum altitude difference. . . . . . . . . . . . . . . . . . . . . 162.6 Cumulative distribution function of minimum distances between

aircraft. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 172.7 Interference caused by link (u, v). . . . . . . . . . . . . . . . . . . 18

3.1 Aircraft traces of all available data . . . . . . . . . . . . . . . . . 223.2 Average number of total aircraft and new aircraft in the simulated

model. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 233.3 Base stations’ locations and coverage. . . . . . . . . . . . . . . . 24

4.1 Link feasibility. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 264.2 Link quality. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 274.3 Topology formation process. . . . . . . . . . . . . . . . . . . . . . 304.4 Graph examples for data rate calculation. . . . . . . . . . . . . . 314.5 Comparison between LP and MCRA. . . . . . . . . . . . . . . . . 35

5.1 Reference case results. . . . . . . . . . . . . . . . . . . . . . . . . 375.2 Connectivity and data rate during flight. . . . . . . . . . . . . . . 385.3 Connected aircraft distances and data rates. . . . . . . . . . . . . 385.4 Connectivity and data rates vs. aircraft density. . . . . . . . . . . 395.5 Connectivity and data rates for different data rate thresholds. . . 405.6 Connectivity and data rates for different number of maximum

connections. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 405.7 Connectivity for different maximum steering angle values. . . . . 425.8 Data rates for different beamwidth. . . . . . . . . . . . . . . . . . 425.9 Connectivity and data rates for closed network of aircraft. . . . . 435.10 Standard deviation and example for ideal case. . . . . . . . . . . 455.11 Median data rate vs longitude for reference and ideal cases. . . . 455.12 Data rates for increased bandwidth. . . . . . . . . . . . . . . . . 46

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List of Tables

1.1 Advantages and disadvantages of Internet access methods. . . . . 2

2.1 Spectral efficiency and data rate for different SINR values. . . . . 132.2 Antenna parameters. . . . . . . . . . . . . . . . . . . . . . . . . . 15

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List of Abbreviations

DA2GC: Direct Air-to-Ground CommunicationSATCOM: Satellite CommunicationEAN: European Aviation NetworkA2A: Aircraft to AircraftRTT: Round Trip TimeLEO: Low Earth OrbitMEO: Medium Earth OrbitGSO: Geosynchronous OrbitGEO: Geostationary OrbitHEO: Highly Elliptical OrbitMANET: Mobile Ad-hoc NetworkAANET: Aeronautical Ad-hoc NetworkmmWave: Millimeter waveTWTA: Traveling Wave Tube AmplifiersNAT-OTS: North Atlantic Organised Track SystemMIST: Minimum Interference Spanning TreeMCRA: Maximum Concurrent Rate AlgorithmLP: Linear Program

Chapter 1

Introduction

Mobile Internet connectivity is growing fast. More than 95% of the popu-lation in developed countries had a mobile-broadband subscription in 2017 [1].Subscriptions in developing and the least developed countries are respectivelyincreasing by 33% and 50% annually [1]. This fact, combined with the increasein flight passengers by 4.5% to 7.2% [2], [3] each year, created a demand for highdata rate in-flight Internet connectivity. This has already been implemented byDirect Air-to-Ground Communication (DA2GC) between ground base stationsand aircraft or by Satellite Communication (SATCOM). Current solutions arenot satisfying. The European Aviation Network (EAN) uses a combination ofDA2GC and SATCOM and can provide data rates up to 75 Mbps/cell [4]. Thissolution does not provide enough capacity for high-speed Internet connectionfor over a hundred passengers. Ways to improve it are still being researched.

DA2GC is cheaper and has less delay than SATCOM but can only be usedfor aircraft above mainland due to the limited range of base stations. A way toextend the DA2GC network is creating an ad-hoc network where the aircraftserve as nodes to transmit signals from the base stations to aircraft outside theirrange. Aircraft to Aircraft (A2A) connectivity is an idea whose capabilities,limits and problems need to be identified before it is implemented. After thisstep, a business model can be created to determine if it is worth implementingor not. This thesis focuses on evaluating A2A in terms of connectivity andachievable data rates by doing simulations.

1.1 Background

DA2GC works by deploying ground base stations and establishing a connec-tion to aircraft directly. It offers a good solution for providing Internet accessto aircraft, because deployment of ground base stations is relatively cheap andeasy [5]. Additionally, DA2GC can have very low delay, between 5 and 10 msround trip time (RTT) [6]. However, aircraft outside the range of base stationsare unable to connect. Therefore, other ways to provide connectivity need tobe exploited for aircraft flying over areas which are out of reach of ground basestations, such as the sea.

A way to overcome this barrier is by using satellites. SATCOM is widely usedin various situations in everyday life, for instance satellite TV and navigation.

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Table 1.1: Advantages and disadvantages of Internet access methods.

DA2GC SATCOM A2A

Advantages• Cheap, easy

deployment• Low latency

• Global coverage• High data rate

• Cheap, easydeployment

• Low latency

• Extension ofDA2GC coverage

Disadvantages • Limited range

• High latency

•HTS requiredfor high datarate

• Bandwidthsharing

Depending on their altitude, these are the satellite categories: [7]

• Low Earth Orbit (LEO) satellites, with altitudes from 160 to 2000 km,

• Medium Earth Orbit (MEO) satellites, with altitudes from 2000 to 35786km,

• Geosynchronous Orbit (GSO) or Geostationary Orbit (GEO) satellites,with altitude 35786 km,

• High Earth Orbit, or Highly Elliptical Orbit (HEO) satellites, with alti-tude higher than 35786 km.

The most important advantage of SATCOM is global coverage [5]. The maindisadvantages of satellite systems are high communication delay (up to approx-imately 500 ms RTT propagation delay for GEO satellites) [5], which makes itimproper for delay-sensitive applications. Even though LEO satellites have amuch lower RTT, large constellations are required to provide connectivity glob-ally, because of their relative speed to earth. According to [8], only 16% of theaircraft can achieve high speed connections, allowing advanced media stream-ing. The data rate problem can be solved by using high throughput satellitesoperating in the Ku-band (11.7 GHz − 12.2 GHz), providing up to 100 Mbpsby exploiting frequency re-use and spot-beam technologies [6].

An alternative to using SATCOM when providing connectivity in areaswhere base stations cannot be deployed is implementing A2A. In this concept,aircraft serve as relay nodes to transmit a signal from the ground base stationto other aircraft. With this technique, the range of DA2GC can be extendedto areas which are out of base station’s reach, for example over oceans. A2Ahas the advantages of DA2GC, but is not limited by areas without ground basestations. Its main disadvantage is that the bandwidth is shared between theconnected airplanes, consequently providing worse data rate per aircraft, whencompared to DA2GC.

The above mentioned ways to provide high speed connectivity can be sum-marized by Figure 1.1 and Table 1.1.

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Figure 1.1: Air-to-Ground Communication methods.

1.2 Ad-Hoc Networks

A Mobile Ad-hoc Network (MANET) is a network created in a dynamicmanner by an autonomous system of mobile nodes connected via wireless links,without the need for an external infrastructure or a centralized administration[9]. In a MANET, the nodes collaborate to achieve proper routing of packages.They are free to move arbitrarily, thus the topology of the network can changerapidly.

Ad-hoc networks can be modeled as a graph G(V, E), where V is the setof nodes and E the connections between them. Since the nodes are movingrandomly, the topology may change in the next moment, as shown in Figure1.2.

Some important characteristics of ad-hoc networks are the following [9]:

• Absence of infrastructure: There is no central administration. The estab-lishment and maintenance of connectivity is maintained by the nodes ofthe network.

• Dynamic topology: The nodes of an ad-hoc network move freely. Thetopology of the network changes rapidly and the nodes may join or leaveat any time.

• Limited bandwidth, variable capacity of links: Ad-hoc networks are wire-less. In contrast with wired networks, the wireless transmission channelis shared. This results in wireless networks having a limited bandwidth,which leads to congestion.

• Energy constraints: The nodes of ad-hoc networks are powered by au-tonomous energy sources. In order to have long expected life, energyconsumption should be minimized.

3

1

2

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5

1

2

3

4

5

t = t0

t = t0 + t

Figure 1.2: Ad Hoc network in different moments.

The above mentioned characteristics hold for networks created by aircraft.A node can be connected to the network, if data can be routed via other nodeswhich are already in the network. Aircraft are traveling at high speeds, thusconstantly changing the topology of the network. Depending on their distancefrom other aircraft in the network, they can join or leave it. Connections betweenaircraft can only be wireless. Therefore, they suffer from interference and canonly use a limited bandwidth. Finally, energy cannot be produced on aircraft.Therefore, the power consumption should be reduced to low levels.

There are different kinds of MANETs. In this work, we are interestedin Aeronautical Ad-hoc Networks (AANETs). They are a special case of aMANET, whose nodes are aircraft. When analyzing such networks, the follow-ing parameters should be evaluated:

• Investigated area: Networks can be local and limited to a few squaremeters, but can also include nodes which are far away from each other.Similarly, AANETs can include aircraft over specific regions, such as theNorth Atlantic ocean, or can be expanded to include all aircraft globally.

• Routing/topology formation: There are different ways to form the topol-ogy of a network. In order for a topology control algorithm to be optimal,the following should be taken into consideration:

– Connectivity: This is a very important feature for a network. Highconnectivity is desirable in networks, and the topology formationalgorithm should try to maximize it.

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– Interference: Wireless networks are generally affected by interference.There are some ways of reducing interference, such as using multiplechannels. Interference can also be completely eliminated dependingon the technology. When free-space optical links are used, as pro-posed in [10], interference is no longer a constraint.

– Node degree: Each aircraft has a limited number of neighbors it canconnect to. This number is restricted by the number of antennas.Even if scheduling techniques are used, an antenna will not be ableto transmit to an unlimited number of aircraft.

– Antenna directivity: MANETs can use either omnidirectional, or di-rectional antennas. This is an option for AANETs as well, with eachof them having its advantages and disadvantages. Omnidirectionalantennas can transmit to any direction. However, there is high in-terference caused by the neighboring nodes. The interference issuecan be reduced by using directional antennas. Directional antennascan also transmit to a higher distance, since they have a higher gainthan omnidirectional antennas [11].

1.3 Related Work

Aeronautical Ad-Hoc Networks (AANETs) are currently being researched asan upcoming technology for providing connectivity to aircraft. The goal of A2Ais to exploit the air to ground networks to provide high data rate communicationto aircraft out of reach of base stations. The biggest challenge in AANETs is tofind the optimal topology to minimize interference and maximize connectivity.So far, there are papers analyzing such networks, however the models useddo not take into consideration all the necessary parameters, mentioned in theprevious section.

The feasibility AANETs over the North Atlantic corridor is evaluated in[12]. The connectivity is measured for a maximum communication distancebetween aircraft of 100, 200 and 300 nautical miles. 89% of the total number ofthe packets sent were delivered successfully, and the median link duration wasfound 24.69 minutes. These results were found by greedy forwarding, based onlocal positions.

In [11], the authors investigate the importance of topology control. In theirscenario, the antennas are directional, however there is no limit on node degree.Two topology control algorithms, one centralized and one with local decisionsfor each node, are compared to the original, unmodified graph. Both give higherthroughput than the original graph. This paper also simulates a case with fivedifferent channels. The sum throughput of the network increases up to 4 timeswhen compared to using a single channel.

A routing algorithm for AANETs, where Internet access is provided both byground base stations and satellites, is proposed in [13]. The topology algorithmis based on the estimation of packet delay, the traffic along the path and thetraffic load of the gateway, instead of choosing the gateway that is closest interms of hops. The proposed algorithm works better in terms of delay, whencompared to algorithms that take into consideration hop count or utilization.

In order to find the optimal topology for a network, interference caused byeach link needs to be modeled and calculated. [14] suggests a model to measure

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the interference cost of nodes in cellular ad-hoc networks. The interference costof node i is calculated as:

Ci =

N∑j=1j 6=i

Iji · d−αij

where N the number of nodes, dij the distance between nodes i and j, α thepath loss exponent and

Iji =

{1, if i causes interference to j

0, otherwise

[15] studies how interference affects ad-hoc networks and how it can be re-duced by implementing topology control schemes. This paper assumes omnidi-rectional antennas and an unlimited number of possible links per node. Underthese assumptions the authors propose interference-minimal topology formationalgorithms.

The impact of changing the antennas from omnidirectional to directional isstudied in [16]. Simulations on airborne ad-hoc networks using omnidirectionalantennas, as well as sector antennas of with up to 4 beams per sector weredone. Simple sector antennas increase the sum rate by more than 7 times whencompared to the omnidirectional case. Using even more advanced antennas,with sidelobe canceling can increase the sum rate up to 72 times. This hugeincrease is due to the reduced interference when advanced antenna arrays areused.

A2A connectivity is evaluated and compared to SATCOM in [17]. In thispaper, the authors simulate one-hop A2A connections, which can be used toprovide connectivity above lakes or small seas. The combination of DA2GCand A2A can provide data rates up to 93 Mbps and even exceed the capacityof SATCOM.

An evaluation of A2A connectivity over the North Atlantic is also done in[18]. In this case, aircraft are equipped with omnidirectional antennas and areable to send signals to any number of aircraft simultaneously. It is assumedthat links do not interfere with each other and therefore no topology control isrequired. The bandwidth is shared between aircraft in a max-min fair way. Itis shown that such a system offers robust connectivity.

1.4 Problem Definition and Contribution

The main goal of this thesis is to evaluate the performance of A2A commu-nication in terms of connectivity and achieved data rate for the aircraft overthe North Atlantic corridor. The results can then be extended to other areaswith the same characteristics, such as oceans or areas where there is no groundinfrastructure. Toward this end, first, aircraft data is analyzed. Given theirpositions, speed and direction, an aircraft movement model is created, with air-craft traveling from Europe to America, and vice versa. After this model iscreated, the bandwidth allocation problem can be formulated as follows:

Given: (a) a graph G = (N,E), where N is the set of nodes, including ahypothetical source node, s, aircraft nodes NA = {1, 2, ..., |NA|} base stationnodes NB = {|NA|+ 1, |NA|+ 2, ..., |NA|+ |NB |} and E the set of all availablelinks; (b) the DA2GC and A2A frequencies; (c) a mapping from the SINR of a

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link to its capacity; (d) the transmit power Pt and antenna gains Gr and Gt;(e) the maximum number of connections, TN ; (f) the beamwidth θ.

The objective is: maximize min(Λi), subject to:

•∑

j:(i,j)∈Efs,ti,j −

∑j:(j,i)∈E

fs,ti,j =

Λi if i = s

−Λi if i = t

0 otherwise

• fs,ti,j ≤Mz(i, j) ∀s, t ∈ N, ∀(i, j) ∈ E

• z(i, j) = z(j, i) ∀(i, j) ∈ E

•∑

j:(i,j)∈Ezi,j ≤ TN ∀i ∈ N

•∑

s,t∈Nfs,ti,j ≤ ci,j

• fs,ti,j ≥ 0 ∀s, t ∈ N, ∀(i, j) ∈ E

• zi,j ∈ {0, 1}

where Λi is the demand for node i, z(i, j) ∈ {0, 1} is the topology variablerepresenting if there is a link between nodes i and j after the topology formation,fs,ti,j is the variable representing the flow from source s to destination t on link(i, j), and ci,j is the capacity of link (i, j).

The output of this problem is a graph G′ = (N,E′), where N are the nodes,as defined above, and E′ ⊂ E, the set of links that guarantee the maximizationof the minimum data rate, under those constraints.

Since this problem has high computational time, a heuristic algorithm forthe topology formation will be used instead. This greedy topology formationalgorithm will be used to reduce the number of connections for each aircraft,which is necessary for the following two reasons: Firstly, a small number ofconnections reduces interference, thus increasing the throughput of the linksused. Additionally, aircraft have a limit on how many connections can be used,because the of the limit on installed antennas.

After the links between all aircraft are defined, a fair bandwidth sharingalgorithm is applied. In our case, we define fair bandwidth sharing, as theallocation of bandwidth in a way that the minimum bandwidth is maximized.In other words, after the fair sharing algorithm is applied, the throughput ofan aircraft can only be increased, if the throughput of another aircraft, with alower data rate is reduced.

A2A is a new concept that is currently being researched. The specificationsaffect the quality of the results. It is important to define the parameters thatinfluence the outcome, and study their impact on the network quality, in or-der to determine a realistic and feasible setup that gives the desirable results.Therefore, the effect of changing the following parameters is measured:

These calculations were done for 15 days, every 15 minutes. Since A2A com-munication is a new concept that is currently being researched, it is important todefine the parameters that influence the outcome, and study their impact on thenetwork quality, in order to determine a realistic and feasible setup that givesthe desirable results. Therefore, the simulations are repeated using the sameaircraft positions, but changing one of the following parameters each time:

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• Data rate threshold to consider connectivity,

• Maximum number of connections per aircraft,

• Maximum antenna steering angle,

• Beamwidth,

• Bandwidth.

The results are then compared and a value that is both realistic and gives decentresults is suggested for each of the parameters evaluated.

The main difference with previous work done on this topic is the parameterstaken into consideration for the topology formation. Other similar implemen-tations, such as [18], consider omnidirectional antennas which do not interferewith neighboring nodes, and no constraint on node degree. In our work, inter-ference is considered and is an important factor when the network topology iscreated. Directional antennas are assumed for two reasons. Firstly, they greatlyreduce interference, thus increasing the capacity of the created links. Secondly,directional antennas have higher gain than omnidirectional ones, resulting inlonger ranges when assuming the same transmission power [11]. The band-width allocation problem is already solved in [18] by defining a linear program.A heuristic algorithm is instead proposed that gives the same results but per-forms better, especially in networks with a high number of nodes. Finally, theimpact of changing the relevant parameters is evaluated, to give an idea of thenecessary requirements.

The research questions that will be answered in this thesis are the following:

• What is the maximum range from mainland that A2A can work, whilemeeting specific data rate thresholds?

• What percentage of aircraft can be connected, having a data rate higherthan certain threshold, when they are over the ocean?

• What is the capacity that can be achieved, under different antenna capa-bilities and aircraft densities?

• What percentage of aircraft can exceed specific data rate thresholds?

• Which aircraft density is sufficient for the feasibility of A2A?

• Under which conditions can SATCOM be replaced by A2A consideringachieved data rates only?

In addition to them, the contribution to the research of A2A communication is:

• Creation of a mobility model for aircraft traveling over the North Atlanticcorridor

• Problem formulation for the optimal topology formation for maximizingthe minimum data rate

• Implementation of a heuristic algorithm for the topology formation, takinginto consideration interference, node degree, and antenna steering capa-bility

• Implementation of a heuristic algorithm to calculate the maximum con-current rate of connected aircraft

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1.5 Organization

The remainder of this thesis is organized as follows. In Chapter 2, thecommunication parameters are defined. These include the frequencies selected,modulations and antenna characteristics. The assumptions for the simulationsare also stated. Chapter 3 describes the model used for the aircraft generationand mobility as well as the positions of base stations. Chapter 4 explains thealgorithms used for the topology formation and bandwidth allocation. Theresults and comparison between different parameters are presented in Chapter5. This chapter also includes a brief comparison with the existing SATCOMtechnology. Finally, Chapter 6 summarizes the work done and presents ideasfor future work, as well as improvements to the suggested methods.

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Chapter 2

Communication Parameters

In this chapter, the communication parameters will be defined. More specif-ically the frequency band selection, maximum communication distance, mod-ulations and antenna parameters will be explained. In addition to that, theassumptions made will also be presented.

2.1 Frequency Bands

Two different kinds of links are present in the scenario. First is the DA2GClink, from the ground base stations to the aircraft, and second is the A2A link,between two airplanes. In DA2GC, the frequency bands defined by the Eu-ropean Telecommunications Standards Institute are the 1.9 GHz and 5.8 GHzbands [19]. An important advantage compared to high frequencies is the rela-tively small attenuation due to rain [20]. DA2GC systems using beamformingantennas could have relaxed limits to their bandwidth [21], but so far 20 MHzare used [19]. For the required calculations, the frequency band of 5.8 GHz wasconsidered, and more precisely 5855 MHz-5875 MHz.

For the future, new frequency sets at the millimeter wave (mmWave) spec-trum are being researched because of the amount of available bandwidth (500MHz or more). In the mmWave spectrum, there are high path losses. How-ever, the low wavelengths allow the use of high gain antennas to compensate forthe losses. With large antenna arrays, advanced techniques such as multi-userbeamforming can be implemented, allowing for even higher spectral efficienciesand modulations [6].

A2A links differ from DA2GC links. A2A communication does not haveany dedicated frequencies yet, which creates an open question that needs to beresearched. mmWaves have characteristics which make them appealing to usefor aircraft communication. The most important advantage of using mmWavesis the wide bandwidth, which can be exploited for very high data rates. Using alarger bandwidth though comes with the cost of increased noise (N = kBT∆f ,where kB is the Boltzmann constant, T the temperature in Kelvin and ∆f thebandwidth). In addition to that, high frequencies suffer from high attenuationdue to water and atmospheric gases, as seen in Figure 2.1 [22]. However, raindoes not exist and atmospheric gases are sparse in typical cruising altitudes.Another disadvantage of mmWaves is the poor diffraction when encountering

10

0 100 200 300 400 500 600 700 800 900 1000

Frequency (GHz)

10-4

10-2

100

102

104

106S

pecific

Attenuation (

dB

/km

)

Standard

Dry

(a) 1 GHz − 1000 GHz

0 5 10 15 20 25 30 35 40 45 50

Frequency (GHz)

10-3

10-2

10-1

100

Specific

Attenuation (

dB

/km

)

(b) 1 GHz − 50 GHz

Figure 2.1: Specific attenuation due to atmospheric gases.

obstacles, but this is not a limit when flying in the air since there are no block-ages. Furthermore, high frequencies result in high path losses even in the simplefree space loss model.

The high path losses make it necessary to use high gain antennas. Buildinghigh gain antennas is much easier in the mmWave frequency band than in lowerfrequencies because of the low wavelength, which directly affects the antennadimensions. A short wavelength enables large antenna arrays to fit in smallphysical dimensions. This also makes it possible to include more antenna el-ements in a small area allowing for higher gain antennas with narrow beams.Having narrow beams can reduce, or even completely eliminate interference,which is vital when considering ad-hoc networks.

Frequencies in the mmWave frequency band were selected for the commu-nication between aircraft because of the above mentioned reasons. As seen inFigure 2.1, there is a local minimum for the specific attenuation at 31 GHz.This frequency band is selected for the A2A communications in the simulationsof this thesis. A bandwidth of 20 MHz will be used to match the bandwidthof the DA2GC links.

2.2 Maximum Line of Sight Distance

In aeronautical networks, there are no obstacles between two airplanes atcruising altitude. It is safe to assume therefore, that the line of sight pathalways exists. The maximum distance at which a node can maintain line ofsight to another can be determined by the altitude of the aircraft as seen inFigure 2.2a. The maximum distance that guarantees line of sight occurs whenthe line AB is tangent to Earth. It can be calculated as:

dmax =√h2B + 2Rhb +

√h2A + 2RhA , (2.1)

where R is the Earth’s radius and hA, hB the altitudes of the airplanes. Assum-ing a cruising altitude of 10 km, the maximum line of sight distance is 714.2 km.This distance is halved when considering air to ground links.

11

AB

hAh

B

R

(a) Simple geometry model.

AB

hAh

B

(b) Model with effect of atmospheric refraction.

Figure 2.2: Maximum line of sight distance.

This approach is based on simple geometry and does not consider the effectsof the atmosphere on the propagation of electromagnetic waves. The density ofthe atmosphere decreases as the altitude increases, which results in a decrease ofthe refractive index. Consequently, electromagnetic waves are bent towards theground, increasing the maximum distance they can be transmitted, as shown inFigure 2.2b. To model this effect, Earth’s radius is scaled by 4

3 [23] and equation2.1 is used. In this more complicated model, the maximum line of sight distanceincreases to 824.6 km.

In the simulations, the more conservative approach is used. The communi-cation distance is limited to 350 km and 700 km in terms of line of sight path forDA2GC and A2A, respectively. Beyond this distance, no signal can be received.

2.3 Modulations

A dynamic modulation scheme is used to ensure optimal transmission. Thelowest possible modulation is QPSK and the highest 256 QAM. Higher modu-lations provide higher data rate, but require a higher SINR to avoid high biterror rate. The LTE standard will be used, since it is the latest technology stan-dardized. To calculate the bit rate, we consider a setting with 12 subcarriers, 7OFDMA symbols and 2 slots per ms [24]. For a bandwidth of 20 MHz we canget up to 12.6 symbols/s when 256 QAM modulation is used. By considering a2x2 MIMO gain of 2, because of the antenna polarization, links with capacityup to 186.64 Mbps can be created. The achievable link data rates are presentedin Figure 2.3. More details about the used modulations can be seen in Table2.1 [25]. The data rates are calculated for the setting described above.

2.4 Receiver Noise

Before we determine the antenna parameters, the thermal noise needs tobe calculated. It can be found by the formula N = kTB, where k is theBoltzmann constant, T is the temperature in Kelvin and B is the bandwidth.In our setup, we have assumed a bandwidth of 20 MHz. The temperature at

12

-20 -10 0 10 20 30 40 50

SINR (dB)

0

20

40

60

80

100

120

140

160

180

200

Da

ta R

ate

(M

bp

s)

QPSK

16 QAM

64 QAM

256 QAM

Figure 2.3: Achievable data rates for different SINR values.

SINR Modulation Spectral Efficiency Data Rate (Mbps)(-∞, -9.478) Weak Signal 0 0[-9.478, -3) QPSK 0.1523 3.8380[-3, -0.2) QPSK 0.377 9.5004[-0.2, 4.9) QPSK 0.877 22.1004[4.9, 7) 16-QAM 1.4766 37.2103[7, 8.8) 16-QAM 1.9141 48.2353[8.8, 10.5) 16-QAM 2.4063 60.6388[10.5, 13) 64-QAM 2.7305 68.8086[13, 14.5) 64-QAM 3.3223 83.7220[14.5, 16.2) 64-QAM 3.9023 98.3380[16.2, 18.8) 64-QAM 4.5234 113.9897[18.8, 20.5) 64-QAM 5.1152 128.9030[20.5, 22) 256-QAM 5.5547 139.9784[22, 23.7) 256-QAM 6.2266 156.9103[23.7, 27.3) 256-QAM 6.9141 174.2353[27.3,+∞) 256-QAM 7.4063 186.6388

Table 2.1: Spectral efficiency and data rate for different SINR values.

13

aircraft cruising altitude is C = −49.9◦ ⇒ T = 223.25K [26]. This gives a noisefigure NdB = −132.1009 dBW

2.5 Path Loss

There are many effects that cause attenuation on the communications links.The most important ones are distance, water and oxygen absorption and multi-path fading. For both DA2GC and A2A links the links are considered narrowenough so that there is no multi-path fading. Additionally, A2A links do notsuffer from water attenuation, since there is no water in cruising altitude. Asseen in Figure 2.4[22] [26], attenuation due to gases is much lower than pathloss for both the DA2GC and the A2A case. Since the attenuation caused bydistance is dominant, a simple free space loss model is enough to describe thelosses in our system:

PL =

(4πdf

c

)2

,

where d is the distance between the two aircraft, or the base station and theaircraft, f the frequency and c the speed of light.

0 100 200 300 400 500 600 700

Distance (km)

0

20

40

60

80

100

120

140

160

180

Att

en

ua

tio

n (

dB

)

Path Loss (5.8 GHz)

Gas Attenuation (5.8 GHz)

Path Loss (31 GHz)

Gas Attenuation (31 GHz)

Figure 2.4: Attenuation due to path loss and gases comparison.

2.6 Antennas

Antennas are an important part of the whole system. Choosing antennaswith proper characteristics can increase the performance significantly. In this

14

section, the antenna parameters will be determined.

2.6.1 Antenna Gain and Size

The antenna parameters are chosen so that the maximum modulation canbe achieved when no interference is present. Traveling Wave Tube Amplifiers(TWTA) are widely used in SATCOM and have been adapted for aerospaceapplications. Assuming a TWTA with maximum transmit power of 20 dBW[18], the antenna gains are calculated as follows:The total received power in dB is

Pr(dB) = Pt +Gt +Gr − PL−N, (2.2)

where Pt is the transmit power, Gt and Gr the transmitter and receiver gains,PL = 10 · log10( 4πdf

c )2 the path losses for distance d between two airplanesand N is the noise figure calculated in Section 2.4. In order to be able to usethe highest possible order of modulation (256 QAM), the SNR requirementis SNR ≥ 27.3 dB [25]. For these values and for d equal to the maximumcommunication distance (700 km) we get:

Pr ≥ SNRthr ⇒ Gt +Gr ≥ SNRthr + PL+N − Pt = 54.4 dB (2.3)

By adding a noise margin of 10 dB to compensate for unpredicted losses in thisideal setting, a total gain of Gtot = 64.4 dB is required for the A2A antennas.The best way to achieve this total gain is to have the same gain on the trans-mitter and receiver antenna (Gt = Gr = 32.2 dB). Such antennas are highly

directional, have diameter D = 10G20 ·λ

πf√k

= 0.14 m, where λ is the wavelength and

3 dB beamwidth ψ = 70·λD = 4.69◦ [27] for the A2A frequency.

For the DA2GC antennas, a higher gain antenna can be used on the basestation. In this way, we can have a smaller antenna on the aircraft, thus makingits impact on flights smaller. Since the frequency of the DA2GC links is lower,the path loss will be smaller, and the gain required will be less than the A2Acase. In addition to that, the maximum communication distance is smaller,which also reduces the required gain. For f = 5.8 GHz and after adding thenoise margin, the total gain required is Gtot = 43.7 dB. In order to have asmaller antenna on the aircraft than on the base station, this total gain willbe achieved with GBS = 29.2 dB and Gair = 14.5 dB in our scenario. Thediameter of such an antenna would be Dair = 0.1m. Table 2.2 summarizes theantenna gains and sizes.

DA2GC A2AFrequency (GHz) 5.8 31Gr (dB) 29.2 32.2Gt (dB) 14.5 32.2Diameter of antennaon aircraft (cm)

10 14

Table 2.2: Antenna parameters.

15

2.6.2 Placement

Aircraft are already equipped with various antennas. The position of eachantenna depends on its use. For example, DA2GC antennas are placed on thebelly, while SATCOM antennas on the top. For A2A communication, there area few options on where to place the antennas each having its own advantages anddisadvantages. For example, placing an A2A antenna on the top of the tailplanegives good line of sight in all directions. However, the width of the tailplanelimits the placement options on it. If an antenna is placed on the nose or tailof the aircraft, its body would obstruct line of sight to other aircraft behind, orin front of it. A deep analysis of antenna placement is not the purpose of thisthesis, however the case of placing it on the top of the tailplane will be brieflystudied and evaluated.

In order to avoid complicated models of the aircraft, a pessimistic approachwill be taken. The worst case of shadowing would be in the direction of theaircraft’s body, because this is the direction that causes the most obstructionto the antenna’s line of sight. The worst case is calculated for the Airbus A380aircraft, which is commonly used for transatlantic flights. The dimensions ofthe A380 are found in the Airbus A380 specification document [28]. By usingFigure 2.5 we can calculate the maximum altitude difference ∆hmax betweentwo aircraft with distance d, to guarantee line of sight between the two antennas.

h

∆hmax=L

d⇒ ∆hmax =

h

L· d (2.4)

hmax

d

L

h

Figure 2.5: Maximum altitude difference.

Typical cruising altitudes for commercial airplanes are 30, 000 to 40, 000 feet,or 9.144 to 12.192 km. For a height difference of 3 km, the distance betweentwo aircraft according to Equation 2.4 should be d ≥ ∆hmax·L

h = 15.1 km. Forcomparison, Figure 2.6 illustrates the cumulative distribution function of thedistance from each aircraft to its closest neighbor. Considering the worst casescenario, 3% of the total number of aircraft cannot connect to their closestneighbor because of altitude difference. In reality though, this would be better,because the height difference is not always the maximum possible, when aircraftare cruising.

When considering antenna placement, it should be noted that multiple an-tennas need to be installed on each aircraft. According to our assumptions, a

16

0 200 400 600 800 1000 1200

Distance to closest neighbor (km)

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1C

um

ula

tive

Pro

ba

bili

ty

(a) 0 to 1200 km

0 5 10 15 20 25 30

Distance to closest neighbor (km)

0

0.02

0.04

0.06

0.08

0.1

0.12

Cu

mu

lative

Pro

ba

bili

ty

(b) 0 to 30 km

Figure 2.6: Cumulative distribution function of minimum distances betweenaircraft.

pair of transmit and receive antennas need to be installed separately for the up-and down-link. Other options include transmitting with one antenna to multipleaircraft, by using scheduling techniques, or having different paths for the up-and down-link. However, in our case, all aircraft are constantly transmittingand receiving the maximum they can and therefore need a separate antenna setfor each connection. The number of antenna pairs depends on the maximumnumber of connections each aircraft can have. Antennas can also be placed indifferent parts of the aircraft to minimize its body’s distortion and maximizethe area that can be served by it.

2.6.3 Connections Per Aircraft

The maximum number of connections per aircraft is an important variable.A high number of connections means that more aircraft can be connected andthat the bandwidth can be shared more evenly. However, the maximum numberof connections is limited by the antennas installed on the aircraft. Moreover,more connections result in more interference. Therefore, to minimize interfer-ence, some links should not be utilized, even if that means that an aircraft willnot be connected to the network. The effect of different maximum connectionsper aircraft has been simulated and will be analyzed in the Section 5.3.

2.6.4 Maximum Steering Angle

The antennas suggested in Section 2.6.1 are highly directional. Since an air-craft might need to connect to other aircraft not directly in front of it, a way tosteer the beam needs to be implemented. This can be done either mechanically,or electronically for more advanced antenna implementations. Different maxi-mum antenna steering angles affect the results. With a higher steering angle,more connection options become available, allowing more connectivity as wellas more chances of finding good quality links.

17

2.7 Interference

Interference plays an important role in wireless networks. When a network isdense, some links can cause lots of interference to neighbouring nodes. Minimiz-ing interference is important in order to increase the throughput of the network.The way to guarantee low interference will be analyzed in Section 4.1.3. In thissection, interference will be defined.

Assuming link (1, 2) between aircraft 1 and 2 and link (3, 4) between aircraft3 and 4, as shown in Figure 2.7. Aircraft 4 receives interference from aircraft 1,if ∠214 < beamwidth

2 and ∠341 < beamwidth2 . In other words, aircraft 4 should

be inside the beam of aircraft 1 and vice versa. In this specific example, aircraft1 and 4 interfere with each other. Aircraft 2 and 3 however, do not, becauseaircraft 2 is not inside the beam of aircraft 3.

1

2

3

4

Figure 2.7: Interference caused by link (u, v).

In our scenario, air-to-ground links use a different frequency than air-to-airlinks, and therefore do not interfere. Additionally, the DA2GC are using ad-vanced beamforming and beamsteering techniques. This results in very narrowbeams which utilize the full bandwidth and do not interfere with each other.Interference can also be avoided, both in the DA2GC and A2A case by usingmultiple channels, if there is enough available bandwidth.

2.8 Assumptions

The results of this project are based on simulations. The assumptions madefor the evaluation of the scenario are the following:

18

• Two aircraft with distance less than the maximum line of sight distancecan communicate with each other if their antennas can be steered towardseach other. Each aircraft knows the exact location of other aircraft andcan steer its antennas towards the target. There are no obstacles betweentwo aircraft.

• Aircraft can instantly connect to other aircraft. There is no time require-ment to steer the antennas or to establish a connection.

• All links are duplex. When two airplanes are connected, they both trans-mit data to each other. Interference is caused both by the down and uplink.

• All aircraft are in cruising altitude. Its precise value is only consideredwhen calculating the distance from base stations. In the case of calculatingdistances between aircraft and interference, all aircraft are assumed to beflying at the same altitude (10 km).

• Aircraft are treated as points. They can connect to each other even iftheir altitude difference is higher than the maximum altitude differencedefined in Section 2.6.2.

• The free space path loss model is assumed. Other factors of signal atten-uation are ignored.

• Base stations have enough capacity to serve all the connected aircraft,such that the number of beams on a base station is always greater thanthe number of aircraft in the coverage area. No interference is caused bythem. In combination with the assumed antenna gains, this means thatall DA2GC links can achieve the maximum possible data rate, which is186.64 Mbps.

• The data used to model aircraft mobility is sampled every 15 minutes.Therefore, the interval between two consecutive time instances in the simu-lations is also 15 minutes. The connections between aircraft do not changeduring this time. This is safe to assume for flights going in the same di-rection, since all airplanes have similar velocities. It is not true howeverwhen two airplanes which are going to opposite directions are connected.Considering the worst case, two airplanes are flying with velocities towardsopposite directions. With an average velocity of 900 km/h, their relativespeed is 1800 km/h. In 15 minutes, the distance between them will changeby 450 km, which is lower than the maximum communication distance,but still significant.

• All aircraft have equal priority. When allocating the bandwidth, an at-tempt to have equal data rate on each aircraft is made.

• All aircraft are equipped with the same number of A2A and DA2GC anten-nas and cooperate with each other irrespective of airline or manufacturer.

19

Chapter 3

System Model

In this chapter, the system model which was used to simulate aircraft mo-bility will be described. It includes every parameter needed to define aircraftposition and movement, such as coordinates, velocity and direction. Each air-craft has a unique identification number, so it can be tracked throughout thewhole simulation.

Even though flight data is available, it is better to create a model to simu-late the mobility of airplanes. Models are created using statistical techniques,which makes them reflect general tendencies rather than daily-specific cases. Bycreating a model, more data can be generated. The new datasets can be eitherbased completely on the data used, or have differences which allow specific casesto be simulated.

3.1 North Atlantic Tracks

The North Atlantic is the oceanic route used to travel between Europe andNorth America. It is the busiest oceanic airspace in the world. For this reason,the simulation area is the North Atlantic ocean, more specifically the area with−60◦ < longitude < −10◦ and 40◦ < latitude < 65◦. However, A2A is notlimited to the North Atlantic. It can be extended for other oceanic routes, oreven areas above mainland.

In order to deal with passenger demand, time zone differences and airportnoise restrictions, air traffic in the North Atlantic is divided in two flows: awestbound, departing from Europe in the morning and an eastbound, departingfrom North America in the evening. More specifically, the westbound trafficreaches its peak between 11:30 UTC and 19:00 UTC, while the eastbound trafficbetween 01:00 UTC and 08:00 UTC [29].

Due to the congestion during peak times, a system of organized tracks isconstructed. These tracks change from day to day with the purpose of avoidingbad weather conditions and taking advantage of the jet streams. They arereferred to as the North Atlantic Organised Track System (NAT-OTS). Theuse of NAT-OTS tracks is not mandatory. Currently, about half of the NorthAtlantic flights follow the NAT-OTS. The rest follow their own routes. Nomatter what route they are following, some regulations about speed, horizontaland vertical separation, need to be followed to ensure safety for oceanic flights.

20

3.2 Aircraft Data

As mentioned in the previous section, not all flights follow the NAT-OTS.Additionally, even if they do, the predetermined routes may vary dependingon weather conditions or other reasons. Therefore, real flight data was studiedto create a model for flights over the North Atlantic. Creating a model fromreal data instead of using the NAT-OTS and departure times makes it morerealistic because it includes variations caused by random factors, such as theroutes changing, or aircraft which do not follow the OTS tracks.

The data used to create the mobility model of airplanes is data of all flightsduring the period 3/11/2017, 09:30 − 10/11/2017, 09:30 from Flightradar24[30]. They contain the following information sampled every 15 minutes:

• Plane ID,

• Latitude,

• Longitude,

• Direction,

• Altitude,

• Speed,

• Time of observation,

• Vertical speed,

• Departure continent,

• Arrival continent,

• Airline,

• Aircraft type.

To locate an aircraft, ADS-B receivers which have a coverage of about 250-450 km are used. The distance limit makes it difficult to cover the oceanicflights [30]. To overcome the problem of incomplete dataset, the location ofaircraft above the North Atlantic is found by interpolating and by consideringthe predetermined routes. The information about position, direction and speedcan be studied to develop a model of aircraft movement. More specific cases,such as being able to connect only airplanes of the same airline alliance ormanufacturer, can be simulated using the airline and aircraft type information.

3.3 Aircraft Generation and Mobility Model

The first step in creating a mobility model for the aircraft flying over theNorth Atlantic is to define the routes used. Since the NAT-OTS tracks changefrom day to day, or even during the day, they cannot be used for a generalmodel. As seen in Figure 3.1, the routes can be identified by analyzing thetraces of aircraft data. The routes are different when considering eastboundand westbound flights. Eastbound flights want to take advantage of the jet

21

streams, while aircraft flying with a direction to North America, want to avoidthem. Every aircraft that follows the NAT-OTS tracks, passes through specificwaypoints. In a similar way, the routes in our case were defined manuallyby setting waypoints every 5 degrees of longitude for lon ∈ [−60◦,−50◦] ∪[−20◦,−10◦] and every 10 degrees of longitude for lon ∈ (−50◦,−20◦).

Figure 3.1: Aircraft traces of all available data

After the routes have been defined, the probability of being on each of themwas calculated. This was done by checking the position of each aircraft andcomparing their distance to the routes. Each aircraft is considered to belongto the route which is closest to it. After assigning all aircraft to a route, theprobability of having an aircraft on each of them is

P (route = route i) =N(aircraft assigned to route i)

N(total aircraft).

Then, the number of new aircraft in each time instance was estimated. Sincethe data is sampled every 15 minutes, the generated data will also have the sametime between two consecutive time instances. Assuming a mean aircraft velocityof 900 km/h, the average distance covered, in the x-axis, is 900 · cosθ, where θis the mean of the absolute value of the aircraft’s direction with a reference tothe longitude axis. In 15 minutes, the distance covered is d = 900 · 15

60 · cosθ =225 · cosθ. Therefore, the number of new aircraft in the simulation area is thenumber of aircraft with a maximum distance d from the edge of the simulationarea. Figure 3.2 represents the average number of new aircraft over a day with15 minute intervals. As mentioned previously, the peak times are different foreastbound and westbound flights.

22

09:30 12:30 15:30 18:30 21:30 00:30 03:30 06:30

Time of day (UTC)

0

50

100

150

200

250

300T

ota

l n

um

be

r o

f a

ircra

ft

0

5

10

15

20

25

Nu

mb

er

of

ne

w a

ircra

ft

N aircraft

New eastbound

New westbound

Figure 3.2: Average number of total aircraft and new aircraft in the simulatedmodel.

Newly generated aircraft will follow a predetermined route, which is definedwhen they are generated. It is assumed that the speed and direction of eachaircraft remain the same until they reach the longitude of one of the waypoints.At the next time instance, the direction will change in order for the aircraft tostay on track. Since there are no landings on the ocean, all aircraft are in thesimulated area until they reach the edge opposite to their starting edge.

The above characteristics define the aircraft generation and mobility model.They are used to generate new data sets of aircraft flying over the North AtlanticOcean. Initially, the area is empty. To simulate the eastbound flow, new aircraftare generated in the simulation area in positions with distance up to 225·cosθ kmfrom the lon = −60◦ edge. When an aircraft is generated, its route is definedand depends on the route probabilities. However, it does not follow the routeprecisely. Instead, each time its direction changes, it takes a random value fromthe interval [route direction − 2◦, route direction] if the aircraft is above theroute and from the interval [route direction, route direction+ 2◦] if it is belowthe route. This ensures some deviation to the routes and randomness in themodel, while the aircraft are still trying to pass through the waypoints.

Aircraft are generated every 15 minutes. The positions of aircraft depend ontheir current speed and direction as well as their previous position. However,when determining the connections between aircraft, each time instance will beindependent. Links created in previous snapshots do not affect the next ones. Itis assumed that the topology remains almost the same for those 15 minutes andthe connections between aircraft are not changed, as mentioned in section 2.8.

23

The model creation and aircraft generation procedures are done for eastboundand westbound flights. The two datasets are then merged into one, so they canbe studied together. The total number of aircraft in the simulation area can beseen in Figure 3.2. The two peaks of high density can be easily distinguished.

3.4 Base Station Positions

The network of ground base stations is the core of a mobile network andof great importance. We need to ensure maximum coverage by deploying basestations in strategic locations, to maximize the coverage and also provide highSINR values. On the Europe side, the positions of base stations used are theones of the European Aviation Network [31]. For North America, Greenlandand Iceland the base stations were assumed in positions near the sea, with thegoal of covering as much area as possible. More base stations can be deployedin case of congestion, but in our case it is assumed that each base station hasenough available capacity to serve all the connected aircraft. The location ofthe base stations and the total covered area can be seen in Figure 3.3.

Figure 3.3: Base stations’ locations and coverage.

24

Chapter 4

Algorithm Design

In this chapter, the algorithms implemented to create the topology and allo-cate the bandwidth will be presented. The topology formation and bandwidthallocation are done separately. The input of the program includes:

• Aircraft locations

• DA2GC and A2A frequencies

• SINR to link capacity mapping

• Transmit power and antenna gains

• Maximum number of connections per aircraft

• Beamwidth

• Maximum steering angle of antennas

First, the topology is created, taking into consideration the above mentionedvariables, and then this network serves as an input to the second algorithm,which allocates the bandwidth to the connected aircraft.

4.1 Topology formation

The first step after defining the aircraft positions is to create the networktopology. This includes finding the best possible connections between aircraftwith some constraints. The constraints that need to be fulfilled in our case arethe following:

• Connectivity should be maximized: Connectivity is not only a metric ofhow good the system is. With more connected aircraft, there are moreroutes from base stations to aircraft, making bandwidth sharing moreeven.

• Interference should be minimized: When an aircraft receives interferencefrom another aircraft, all the links are affected by it. This reduces thereceived SINR, which results in less total capacity.

25

• Each aircraft has a maximum number of connections allowed: This is alimit that depends on the antennas installed during the construction ofaircraft.

The aircraft network is represented as a graph, where the nodes are basestations and aircraft, while the edges are the connections between them. Twohypothetical nodes are also added, a source and a sink, with connections asshown in Figure 4.4. They are helping nodes which will be used in the linkselection and bandwidth allocation steps. The procedure to find the optimalnetwork topology is described in Sections 4.1.1 to 4.1.3

4.1.1 Available Links Between Aircraft

In order to find the final network topology, first all the available links needto be identified. Since a duplex link is required to have both up and downlinks, two aircraft can communicate if their antennas can be steered towardseach other.

To determine if an aircraft’s antenna can be steered towards another aircraft,a simple angle comparison needs to be made. As seen in Figure 4.1, aircraft 1and 3 can transmit signals to each other, making communication between themviable. Even though aircraft 1 can receive a signal from aircraft 2, the oppositeis impossible. Therefore, aircraft 1 and 2 cannot communicate. By performingthis check on all pairs of aircraft, all the possible connections of the network arefound.

1

2 3

1

2 3

Figure 4.1: Link feasibility.

26

4.1.2 Interference Model

Interference plays an important role in the link selection. The interferencecaused by some links is high, while others cause little or no interference atall. Links whose wanted signal is higher than the interfering signal should beprioritized. To model the interference, a two dimensional table of size N ×N ,where N is the number of aircraft, is used. The interference table values I arecalculated according to the following equation:

Iij =d−αij∑N

k=1k 6=i,j

Aijk · d−αik, (4.1)

where α = 2 because the free space loss model is considered, dij is the distancebetween nodes i, j, dik is the distance between nodes i, k and

Aijk =

{1, if k is inside the beam of link (i, j)

0, otherwise

By using this interference model, high values mean good quality links. Links

I > 1

I < 1

I = 1

2

3

4

bad link

good link

best link

Figure 4.2: Link quality.

which cause no interference at all have value I = ∞, while for links which cancause more interference than wanted signal I < 1. In the example of Figure 4.2,aircraft 3 is covered by the beam of link (1, 2), and aircraft 2 is covered by thebeam of link (1, 3), while no aircraft other than 4, is inside the beam of link(1, 4). Since d12 < d13, we have I12 > 1, I13 < 1 and I14 = ∞. In the nextstep of link selection we will keep high quality links and delete the lower qualityones while trying to maintain as much connectivity as possible.

27

When the beamwidth increases, almost all links cause higher interferencethan their wanted signal. In this case, we try to prioritize links between air-craft which are close to each other. To do this, when beamwidth ≥ 40◦, theinterference model used instead is:

Iij =d−αij

max(Aijk · d−αik ). (4.2)

By using this interference model, aircraft whose receiver is closer than the air-craft which receive interference are prioritized.

4.1.3 Link Selection

Selecting which links to keep and which to discard is the most importantpart of the simulation. An airplane has many possible links, even when themaximum steering angle is small. However, not all of them can be used, andeven if they could, they should not due to the following two reasons. Firstly,the number of connections is limited by the installed antennas. This resultsin a hard limit on the maximum number of possible connections. Secondly,more links create more interference in the network. Keeping all the possiblelinks would cause lots of interference, making the network conditions worse andresulting in lower data rates.

The link selection should be such, that interference is minimized while keep-ing the connectivity as high as possible. To achieve that, the connections arechosen in two steps. First, a spanning tree with root the hypothetical sourcenode is created. This Minimum Interference Spanning Tree (MIST) has a con-straint of maximum connections and aims in maximizing connectivity whileminimizing interference. There might be cases where some aircraft are not con-nected to the network, even though theoretically they could. These connectionshowever, would be of low quality and would not be able to provide high datarates and are therefore not chosen. To protect the network from bad qual-ity links which would degrade the overall performance, links which cause highinterference are deleted before it is run. More specifically, for beamwidth upto 20, links which cause at least double interference than wanted signal aredeleted. For higher beamwidths no links are deleted because they all cause lotsof interference.

The implemented algorithm for the first link selection is a minimum spanningtree algorithm, where the weights are the values of the interference model sug-gested in Section 4.1.2. In the initialization, the graph includes the hypotheticalsource, base stations and aircraft with DA2GC links as well as the connectionsbetween them. In each loop iteration, a new node is added to the tree. Thisnew node is connected to a node already in the tree, but with less connectionsthan the maximum number of connections (Nconn). The link (u, v) with thehighest value in the interference table I is chosen. This edge and aircraft v areadded to the output graph GMIST . The algorithm stops when either all nodesare connected or when there are no more possible links. A pseudocode for MISTis Algorithm 1.

After creating the MIST, more links are added in order to share the band-width more evenly between aircraft. The idea behind the algorithm for theextra links is the same as Algorithm 1, with the difference that we now check

28

Algorithm 1 Minimum Interference Spanning Tree (MIST) Algorithm

Input: a set of nodes V and the possible links between all nodes EInitialization: GMIST includes the hypothetical source, base stations andaircraft connected with DA2GC links, and the links connecting themE = E\{edges in GMIST }while not(all nodes connected) and E 6= Ø doEMIST = all edges from nodes in GMIST with Nconn < max conn to nodesnot in GMIST

e = (u, v) ∈ EMIST , where Iuv = maxGMIST = GMIST ∪ {v} ∪ {e}E = E\{(x, v)}∀x ∈ V

end whileOutput: Graph GMIST

all links in the graph and choose the best one without the constraint of maxi-mizing connectivity, since it is already guaranteed from the previous step. Thelinks compared are the ones connecting aircraft for which Nconn < max conn.The one that creates the least interference is added to the graph GMIST cal-culated before. For the next iterations, the already used links are excludedfrom the comparison. The algorithm ends when there are no more connectionpossibilities due to maximum node degree or no more available links. By usingadditional links the bandwidth can be shared more evenly among the aircraft.Additionally, the network becomes more robust and less affected by a possiblelink failure.

Figure 4.3 shows the process of topology formation. First, all possible linksare found (Figure 4.3a). From these links, a tree structure is created withthe purpose of maximizing connectivity (Figure 4.3b), creating the MinimumInterference Spanning Tree. The final step can be seen in Figure 4.3c wheremore links are added to make the bandwidth sharing more even.

The importance of topology formation can be seen in Section 5.3. We can seethat, even if we can have a large number of maximum connections per aircraft,it does more harm than good to include all the available links.

4.2 Bandwidth Allocation

In the previously created ad-hoc networks, the links between nodes define themaximum data rate that can be transmitted between them. The base stationsprovide aircraft with a specific data rate, which needs to be shared as evenlyas possible to all aircraft in the network. To find the optimum bandwidthallocation, flow algorithms were utilized. It is therefore important to introducethe basic theory of what was used to achieve the results.

4.2.1 Maximum Flow

A flow network is a directed graph G(V,E) with a source s ∈ V and a sinkt ∈ V . Each edge has an assigned capacity cuv which denotes the maximumflow that can go through this edge. The total flow |f | of such a network is theamount of flow going from the source to the sink. The maximum flow is the

29

(a) All available links. (b) Tree structure.

(c) Final topology of connected aircraft.

Figure 4.3: Topology formation process.

30

7

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(b) Graph G′(V ′, E′) after adding the hypo-thetical source and sink.

Figure 4.4: Graph examples for data rate calculation.

largest possible value that |f | can take. There are several ways to calculatethe maximum flow in a given network. Matlab has a maximum flow functionalready implemented, which is the one used in our case.

4.2.2 Maximum Flow With Node Demands

The networks in the simulations have more than one source (base stations)and sink (aircraft). The typical maximum flow problem includes only one source-sink set and cannot be used in our case. There is however, an extension of itwhich lets us define multiple sources and sinks, as well as specific capacitydemands for each sink. With this max-flow extension we can find a flow thatsatisfies the capacity constraints and additionally some demand constraints.The demand of each node is the excess flow that should come into the node,representing its data rate [32].

Let G(V,E) be the graph in Figure 4.4a. This is a network of 2 base stations(BS) and 5 aircraft (Ai). For the maximum flow extension with node demands,we construct a new graph G′(V ′, E′) from G, by adding hypothetical source(S) and sink (T) nodes. New edges are added from S to the base stations.The capacity of each of those edges is equal to infinite to ensure that this newsource node is not a bottleneck of the system. Edges are also added from eachaircraft to the sink node. The capacities of these edges represent their datarates and need to be defined. Ideally, each aircraft should have the same datarate. However, this is not possible due to the capacity of the available links. Inthe example of Figure 4.4 all aircraft should ideally receive 5 Mbps, but A5 canreceive up to 4 Mbps. The remaining 1 Mbit should be allocated evenly to theremaining aircraft.

This max-flow extension will be used when calculating the optimum band-width allocation. The goal of the sharing algorithm (Algorithm 2) is to allocatethe available bandwidth as evenly as possible to the connected aircraft. Thealgorithm has two steps. First, the maximum concurrent data rate λ for allaircraft is calculated (Algorithm 3). Then the aircraft that is responsible forthe concurrent data rate not being able to increase is found. The data rate ofthis aircraft is set to λ and is constant for the rest of the calculations. The max-

31

imum concurrent data rate of the remaining aircraft is calculated in the nextiteration. The fair sharing algorithm ends when the data rates of all aircraft arecalculated.

Max-Min Fair Allocation Algorithm

First, the Max-Min Fair Allocation Algorithm is described (Algorithm 2).Its goal is to allocate the available bandwidth in a way that is max-min fair. Tostate this mathematically, a data rate allocation vector R is defined, where Riare the allocated data rates of each aircraft. An allocation vector is max-minfair if each of its elements Ri can’t be increased without decreasing any othernode k’s rate Rk for which Rk ≤ Ri [18].

The algorithm works by slowly creating the optimal allocation vector, start-ing from the nodes with the lowest data rates. It is similar to the OptimalMax-Min Fair Allocation Algorithm proposed in [18]. Instead of using a linearprogram it is replaced by heuristic algorithms. In the beginning all the aircraftare considered unsaturated. The goal of each iteration in the main loop is tofind the maximum concurrent data rate λ and which aircraft is the bottleneck ofthe system. The maximum concurrent data rate is calculated using Algorithm3. To find which aircraft cannot further increase its data rate, we first set thedata rate of all aircraft to λ. One by one, the flow from each aircraft to thehypothetical sink is set to infinite and the maximum flow from the source to thesink is calculated. The bottleneck is the aircraft which gives a minimum maxi-mum flow after increasing its flow to infinite equal to the maximum concurrentdata rate, as it cannot be increased any more. This aircraft is now consideredsaturated and its data rate will be λ. The algorithm then repeats the sameprocedure for all aircraft whose flows are unsaturated until the data rate of allaircraft is calculated.

Algorithm 2 Max-Min Fair Allocation Algorithm

Input: Graph G(V,E), where V the set of nodes and E the edgesΓunsat = V , the set of unsaturated aircraftΓsat = Ø, the set of saturated aircraftRsat = Ø, the data rates of the saturated aircraftwhile Γunsat 6= Ø doλ = MCRA (G(V,E), Γunsat, Γsat, Rsat)for all k ∈ Γunsat doRj = λ ∀j ∈ ΓunsatRj = Rsat ∀j ∈ ΓsatRk =∞f tempj = maxflow(s, t)

end fork = j : f tempj = min(f temp)Γunsat = Γunsat\{k}Γsat = Γsat ∪ {k}Rsat = Rsat ∪ {λ}

end whileOutput: Γsat, Rsat

32

Maximum Concurrent Rate Algorithm (MCRA)

Finding the maximum concurrent data rate for all aircraft can be imple-mented with a linear program (LP). Its objective would be to maximize theconcurrent flow of unsaturated aircraft with the following constraints:

• The flow allocated on each link f(u, v) cannot exceed the capacity c(u, v)of the link0 ≤ f(u, v) ≤ c(u, v) ∀ u, v ∈ V

• The flow into a node is equal to the flow out of that node, for all nodesexcept the hypothetical source and sink∑Nu=1 f(u, v) =

∑Nu=1 f(v, u) ∀ v ∈ V \{S, T}

• The data rates (d) of all unsaturated aircraft are equaldi = dj ∀i, j ∈ Vunsat

• The saturated data rates calculated in previous iterations of Algorithm 2remain constantdsat = Rsat

Instead of a LP, a heuristic algorithm is used to solve the maximum concur-rent rate problem. First, it will be described and then its results and run-timewill be compared to the LP. The goal of the Maximum Concurrent Rate Algo-rithm (MCRA, Algorithm 3) is to bound λ between two values. Initially, thelower bound of λ is set to 0, and the upper bound equal to the maximum ca-pacity of all DA2GC links because this is the maximum data rate that can beallocated to an aircraft. In each step, λ is defined as the mean of the lower andthe upper bound. The data rate of each unsaturated aircraft is set to λ, whilesaturated aircraft have their constant saturated data rates. Then the maximumflow fmax from the hypothetical source to the hypothetical sink is calculated.If fmax is equal to the sum of all flows from the aircraft to the hypotheticalsink, this means that all aircraft can satisfy their demand, and therefore λ canincrease. λ is now the new lower bound of the maximum concurrent data rate.If fmax is less than the sum of the flows, then at least one aircraft cannot achievedata rate λ. Therefore, λ is an upper bound of the maximum concurrent datarate. The case that f is higher than

∑Nair

i=1 Ri, where Ri is the data rate ofaircraft i, and Nair is the total number of aircraft, is not possible since a min-cut exists between the hypothetical sink and the aircraft. In the next iteration,the difference between the two bounds will be halved. The algorithm contin-ues until upper bound − lower bound < ε. If ε is set to a number insignificantcompared to the achieved data rates (ε = 10−5 in our case) we can assume thatλ = mean(lower bound, upper bound).

MCRA, LP Comparison

The aim of the MCRA and the LP is the same. They find the maximum datarate that can be achieved by all unsaturated aircraft simultaneously. Figure 4.5ashows the absolute difference of the calculated values when simulating a day offlights. The difference does not exceed 10−4, which is very small compared to thecalculated data rates. The time required to run the LP though is considerablyhigher, getting close to double for a high number of aircraft, as seen in Figure

33

Algorithm 3 Maximum Concurrent Rate Algorithm

Input: G(V,E) with capacity between nodes u and v cuv, indexes of unsatu-rated aircraft Γunsat, indexes of saturated aircraft Γsat, data rates of saturatedaircraft Rsatupper bound = max(cuv), u : nodes with DA2GC links

lower bound =

{0, if first iteration

previous max concurrent rate, otherwise

while upper bound− lower bound > ε doλ = mean(lower bound, upper bound)Rk = λ ∀k ∈ ΓunsatRk = Rsat ∀k ∈ Γsatf = maxflow(s, t)

if f =∑Nair

i=1 Ri thenlower bound = λ

elseupper bound = λ

end ifend whileλ = lower boundOutput: rate

4.5b. Therefore, the MCRA was used to calculate the maximum concurrentrate.

34

0 5000 10000 15000

aircraft index

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Figure 4.5: Comparison between LP and MCRA.

35

Chapter 5

Results

In this chapter the simulation results will be presented. First some generalresults about connectivity and data rate will be shown. Then the variation ofthe following parameters will be analyzed:

• Data rate threshold, Rthr

• Maximum number of connections per aircraft, Nconn

• Antenna steering angle, φ

• Beamwidth, θ

Additionally, two scenarios including less aircraft will be evaluated: onewith aircraft manufactured by Airbus and one with aircraft which belong toStar Alliance members. An ideal case, with no restrictions on connections andwith no interference will also be considered. Finally, the bandwidth will beincreased to 200 MHz to provide a fair comparison with SATCOM.

5.1 General Results

In this section one reference scenario will be presented. This is the case ofno threshold on data rate for connectivity, φ = 45◦ maximum steering angle,θ = 4.69◦ beamwidth and 3 maximum connections per aircraft. Figure 5.1ashows the average number of aircraft during a day. As mentioned previously,there are two peaks in the aircraft density, one for the eastbound and one for thewestbound flow. For a simple case where an aircraft is considered as connectedif it can find a path to any base station, the percentage of connected aircraft isclose to 100% for most of the time and drops to a minimum of 87% a few hoursbefore the westbound peak. Between the two peak times, the connectivity ishigh because aircraft in opposite directions can establish a connection.

In Figure 5.1b, the probability density function of the data rates for allaircraft is shown. There is a high peak at 25 Mbps, which is the result ofshared bandwidth between a large number of aircraft. Some smaller peaks canbe identified at the capacity of the DA2GC links, RDA2GC (186.6 Mbps) as wellas values k

n ·RDA2GC , where k, n ∈ N. This happens because of clusters whichconsist of n aircraft, k of which are connected directly to a base station.

36

09:30 12:30 15:30 18:30 21:30 00:30 03:30 06:30

Time of day (UTC)

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(a) Number of aircraft and connectivity.

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Figure 5.1: Reference case results.

Another interesting result is how the connectivity and data rates change, asaircraft are moving away from mainland. Figure 5.2a shows how the mediandata rate and the probability of an aircraft being connected changes duringtransatlantic flights for different aircraft densities. We defined three differentdensity categories: high, medium and low, which are defined by the averagenumber of aircraft. When Nair ≥ 0.75·max(Nair), it is considered a high densitycase. Medium density is when 0.25 ·max(Nair) ≤ Nair < 0.75 ·max(Nair) andlow when Nair < 0.25 ·max(Nair). The connectivity remains high during thewhole flight irrespectively of aircraft density. In cases of high and mediumdensity, the connectivity is almost perfect. while when there is low aircraftdensity, it is around 94%. When compared with Figure 3.3, we can see that datarates are higher in the areas where there is direct air-to-ground connectivity. Thedata rate difference between areas close to the mainland and areas in the middleof the ocean is higher in the case of high densities of aircraft. This happensbecause a larger number of connected aircraft, results in the bandwidth beingshared in more nodes. Additionally, there is more interference, which causesthe A2A links to have less capacity. The data rates increase as we approachEurope mainland, but does not reach the level of North America. The reasonfor this is that the simulation area includes less of Europe’s mainland, thanNorth America’s and therefore less aircraft with DA2GC.

The same conclusion can be drawn from Figure 5.3. Figure 5.3a shows thedistance from the closest base station for all connected aircraft, divided in thethree density cases. In all of them, the distribution of distances is similar, with25% of aircraft having DA2GC, irrespective of the aircraft density. This explainsthe similar data rate in every case, shown in Figure 5.2a. Since the portion ofaircraft directly connected to a base station does not have a considerable change,the relative bandwidth, compared to the number of aircraft remains the same.However, as expected, the data rates decrease, as aircraft are further away fromthe base stations, as it can be seen in Figure 5.3b.

Something else that should be noted is a small decrease in connectivity in thearea with longitude < 55◦. This happens because it is hard to achieve connec-tivity for aircraft close to the simulation area corners. Aircraft are traveling inroughly the same direction. Being limited by steering angle, an aircraft cannotbe served by another aircraft flying in parallel with it. A similar issue happens

37

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(a) Connectivity (dashed line) and mediandata rate of connected aircraft (continuousline) vs longitude for different aircraft den-sities.

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titu

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(b) Connectivity for different areas of the simu-lation area.

Figure 5.2: Connectivity and data rate during flight.

high density medium density low density

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(a) Distances from nearest base stations forconnected aircraft.

(b) Data rate vs distance from nearest basestation.

Figure 5.3: Connected aircraft distances and data rates.

in the European side, but its effect is visible for a short distance after DA2GCrange. This difference is due to the corners of the simulation area being coveredby the ground base stations on the European side (Figure 3.3). Figure 5.2bvisualizes connectivity for different areas of the simulation area. As mentionedabove, areas near the edge have lower connectivity, because of less possible con-nections per aircraft. By ignoring the areas with low connectivity due to thisissue, and taking into consideration only the area where 45 < latitude < 60,the connectivity is unaffected by longitude. In the case of high and mediumdensity, almost all aircraft are connected, while for lower densities, the averageconnectivity is 98.3%. As it will be seen in section 5.4, this problem decreaseswhen the steering angle of aircraft increases.

Finally, in order to evaluate the feasibility of A2A, its performance shouldbe measured in terms of aircraft density. As Figure 5.4a shows, aircraft densitygreatly affects connectivity. Connectivity was higher than 80% when there weremore than 6.6 · 10−6 aircraft/km2, or 1 aircraft/151515 km2, which correspondsto 88% of a 24 hours period, in case all aircraft cooperate with each other. Theeffect of aircraft density is less in the median data rate (Figure 5.4b). This

38

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(b) Median data rates vs aircraft density.

Figure 5.4: Connectivity and data rates vs. aircraft density.

happens because as the number of aircraft increases, both the aircraft withand without DA2GC increase proportionally. Therefore, more bandwidth isavailable to be allocated.

The case presented in this section is far from the perfect allocation, whichwill be presented in Section 5.7. This cannot be achieved because of interferenceand the limits on maximum number of connections and steering angle. In thenext sections, we will be changing the simulation parameters to find how theyinfluence the result. Only one parameter will be changed in each case, whilethe others will remain the same as in this case. The scenario presented in thissection will be included and highlighted for comparison.

5.2 Data Rate Threshold

Achieving good connectivity is important, but when considering Internetconnection, a high data rate is important. In this section, we set a thresholdto the data rate that needs to be achieved by an aircraft in order to connect tothe network. In case it cannot reach this data rate it gets disconnected and allof its potential links are disabled. The data rate thresholds were set to 20, 40and 50 Mbps which correspond to 10.75%, 21.5% and 26.88% of the maximumdata rate (186.6 Mbps) respectively.

Setting a high threshold decreases connectivity, because less aircraft needto share the bandwidth in order to achieve the data rate goal. As seen inFigure 5.5a, areas close to mainland still have high connectivity. However, inthe middle of the ocean, it drops to a minimum of 37% when the threshold isset to 50 Mbps. The data rates, on the other hand (Figure 5.5b), have a muchsmaller difference when comparing areas close to mainland and in the middleof the ocean. In all cases, the mean data rate is around 110 Mbps when anaircraft is close to land. For areas between North America and Europe the datarates vary a lot. Compared to a minimum of 16.7 Mbps with no threshold, themedian data rate of connected aircraft when applying a threshold of 50 Mbpshas a minimum of 51 Mbps. Applying a data rate threshold can be used in thecase of aircraft belonging to the same alliance, in order to provide high datarates only to specific aircraft.

39

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Figure 5.5: Connectivity and data rates for different data rate thresholds.

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Figure 5.6: Connectivity and data rates for different number of maximum con-nections.

5.3 Maximum Number of Connections Per Air-craft

The maximum number of available connections per aircraft is an importantparameter, because it defines the total number of antennas which needs to beused, and consequently plays an important role in determining installation andmaintenance cost. In general, increasing the number of connections per aircraftincreases connectivity and helps in sharing the bandwidth more evenly.

As Figure 5.6a shows, there is not much difference in connectivity whenthe number of maximum connections is 3, 4, 5, 10 or 20. In the case of 2connections per aircraft, they are not enough to offer connectivity. Increasingthe number of connections per aircraft, helps in sharing the available bandwidthmore evenly, as Figure 5.6b shows. As the number of connections increases, thedeviation of median data rate over the whole area decreases, with the biggestrelative increase being from 3 to 4 connections. In this case, the minimum of themedian data rate of aircraft over the sea, increases from 20 to 28 Mbps. Thishappens, because with more connections data can be routed via many links,

40

thus increasing the total capacity than can reach aircraft in the middle of theocean. As the number of connections increases to more than 10, adding moreconnections has an insignificant impact on the data rate distribution. Eventhough 10 or 20 connections per aircraft gives the ideal result, having so manyantennas on aircraft only for that purpose is not feasible.

Adding more connections has a positive impact, when the beamwidth is4.69◦. For higher beamwidths, the interference caused by having more linkswould cause the performance to increase less, or even become worse.

5.4 Antenna Steering Capability

Steering the beam of antenna can be done either mechanically or electroni-cally. When mechanical steering is used, an antenna can be steered towards anydirection, but some time is required for the rotation of the device. Electronicbeam steering is implemented by using phase shifters in each of the antenna’selement, in order to change the direction of the main lobe. This is much fasterthan mechanical steering, but requires advanced and expensive antennas. In thesimulations, we assumed that the antennas can point to the desired directioninstantly. The maximum angle that an antenna can steer from the aircraft’sdirection varies from 15◦ to 90◦. Since there are antennas that target to thefront and the back of the aircraft, a steering angle of 90◦ covers the whole area.

Increasing the steering angle enables more possible connections between air-craft. This has an impact on both connectivity and link quality. The percentageof connected aircraft increases when the maximum steering angle φ increases.The connectivity of lower density times is affected more by this change, as seenin Figure 5.7a. This happens because in higher aircraft densities, there are lotsof airplanes, and therefore they can find alternative routes to base stations.Figure 5.7b shows the connectivity for different longitude values. Increasingthe maximum steering angle of antennas reduces the connectivity problem oflongitudes near the edges of the area because aircraft can now connect to otheraircraft flying in parallel directions. Even though more possibilities enable moreand better quality links, the data rates follow the pattern of Figure 5.2a. Withonly 3 maximum connections per aircraft and a narrow beamwidth, the linksof lower maximum steering angles are almost as good as the case of being ableto transmit in every direction. A maximum steering angle of 30◦ is enough toguarantee close to perfect connectivity in high densities.

5.5 Beamwidth

The antennas assumed in Section 2.6 have high gain (32.2 dB) and a narrowbeamwidth (4.69◦). The beamwidth of an antenna depends on its size. Smallersized antennas have a wider beamwidth. However, they are cheaper and haveless impact on the aircraft’s aerodynamics. Therefore, it is important that theeffect of different beamwidth is also studied.

Wider beamwidth means that each link creates more interference to otheraircraft, degrading the network’s performance. This has a higher impact inareas with high density, where lots of aircraft interfere with each other. In suchareas the SINR can drop so much that no information can be transmitted. In

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Figure 5.8: Data rates for different beamwidth.

Figure 5.8a we can see that up to a beamwidth of 90◦ there is no big differencein connectivity. For a beamwidth of 180◦ though, the interference caused is somuch, that the connectivity drops significantly.

Even though beamwidths up to 90◦ provide roughly the same percentage ofconnected aircraft, the distribution of data rates has considerable differences,which can be observed in Figure 5.8b. Networks with high interference havemore outliers. This is due to the low SINR of many aircraft which prohibitshigh data rates. Most of the bandwidth is allocated to the aircraft which areconnected directly to the base stations or have a small number of hops. In thecase of no interference, the difference in throughput is due to the number ofaircraft and the maximum number of connections.

Since the beamwidth is directly affected by the antenna size, it is hard todefine a desirable value for it. Installing new antennas is not only about antennainstallation and maintenance cost. It also affects the performance of the aircraftand its fuel consumption. A deeper study should be done to determine theantenna size and positions that offer good network performance and do notobstruct the normal aircraft operations.

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Figure 5.9: Connectivity and data rates for closed network of aircraft.

5.6 Closed Network of Aircraft

The above mentioned scenarios assume that all aircraft can connect to eachother irrespective of company or manufacturer. However, this is an ideal sce-nario and it is highly probable that not all airlines will cooperate with eachother. In this section we have simulated two cases: One where only Airbusaircraft are taken into consideration and one with aircraft from airlines whichare members of Star Alliance. These two scenarios represent reality better. It isassumed that aircraft which are in the same area but belong to another networkuse a different frequency band and therefore cause no interference.

Both cases give similar results and they depend mostly on the number ofaircraft. In terms of connectivity, as shown in Figure 5.9a, more than 90% of theparticipating aircraft are connected in high density times. In lower densities theconnectivity is much worse, and can get as low as 30% at the minimum aircraftdensity. The distribution of the data rates is slightly better for the closednetworks, but still there are no major differences compared to the general case.This happens because there are fewer aircraft in the network. With less aircraft,interference is not strong and the bandwidth needs to be shared by less aircraft.

It is interesting to see how the network behaves in cases of different densities.Figure 5.9b shows the connectivity and data median data rate when consideringhigh, medium and low density independently. With medium and high densityof aircraft, we can have more than 85% connectivity during flights. When theaircraft density is low though, most of the aircraft cannot connect to a groundbase station. According to these results, implementation of A2A is feasible forclosed networks only in times of high or medium aircraft density. In situationsof low aircraft density, other methods of connectivity should be used, such asSATCOM. The relationship between aircraft density and connectivity can beseen in detail in Figure 5.4a.

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5.7 Theoretical Ideal Case

An ideal case is also simulated for educational purposes, even though thisscenario is unrealistic by using today’s, or the near future’s technologies. Thisis the case of having an unlimited number of connections per aircraft, whichcause no interference. The antennas can be steered towards any direction andthere is no threshold when connecting aircraft. In reality, this could be achievedby using pencil-beam antennas, or different channels to avoid interference. Thenumber of connections can be maximized by adding more antennas, or usingscheduling techniques.

This results in a network where the bandwidth is shared as evenly as possi-ble, and each aircraft has a data rate close to the average. Figure 5.10a showsthe standard deviation of the data rates for different times of the day and acomparison with the reference case. As the number of aircraft increases, thestandard deviation of the ideal case decreases, because more links are estab-lished, giving alternative routes for the bandwidth sharing. However, in casesof low density, independent networks of aircraft are created which are not con-nected with each other. Figure 5.10b represents such an example. There are3 clusters of aircraft: one small over the mainland of North America, and twobigger ones, one close to North America and one close to Europe. There arealso 3 aircraft which cannot establish a connection to a ground base station. Allaircraft in those clusters have the same data rate, with very few exceptions incases where there are only a few links. However, two aircraft of different clustershave different data rates, which depend on the total number of aircraft and theaircraft connected with DA2GC. If there are enough links to guarantee perfectsharing of the bandwidth, each aircraft will have data rate

R = RDA2GC ·NDA2GC

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where RDA2GC = 186.6 Mbps, the data rate of DA2GC links assuming SINR >27.3 dB, NDA2GC the number of aircraft with DA2GC and Ntot the total num-ber of aircraft in the cluster. The standard deviation for the reference case isdouble than the maximum of the ideal case at almost any time. The reasons ofthe better bandwidth allocation in the ideal case are the assumption of no inter-ference, and the increase of node degree. When no topology control is applied,each aircraft has an average of 34.01 connections. By applying the topology con-trol algorithm, the average node degree is reduced to 2.70 connections/aircraft,which makes bandwidth sharing worse, as shown in Section 5.3.

Having no restrictions creates a topology that can support high data rateseven in the middle of the ocean. As Figure 5.11 shows, there is only a smalldrop in the median data rate when aircraft are not in DA2GC range. More than50% of the connected aircraft have higher than 42 Mbps data rate in this idealcase, regardless of their position.

5.8 Increased Bandwidth and Comparison WithSATCOM

The goal of this project is to evaluate A2A as an alternative solution toSATCOM. So far, today’s LTE technology with a bandwidth of 20 MHz was

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assumed. In order for DA2GC and A2A to be competitive towards SATCOM,increased bandwidth is required. As mentioned in Section 2.1, the mmWavespectrum is being researched for 5G technologies, increasing the available band-width.

In this section, the bandwidth of DA2GC and A2A links will be increasedand the results will be compared to SATCOM. Increasing the bandwidth alsoincreases the thermal noise. Therefore, we cannot increase the bandwidth in-definitely, even if there is enough available spectrum. A bandwidth of 200 MHzwill be considered in this scenario. Increasing the bandwidth 10 times, resultsin 10 dB higher receiver noise. To make the simulations even more realistic,10 dB of additional losses will be considered, to negate the 10 dB noise marginthat was assumed in Section 2.6. These additional losses have an impact on linkquality, especially when two nodes are far from each other.

In terms of connectivity, there is no difference when compared to the generalcase in Section 5.1. Data rate increases linearly when the bandwidth is increased,if the SINR remains the same. In our case however, noise is also increased. Forthis reason, even though the data rates are increased, they are not 10 timeshigher. Just as in the general case, there is an increase of the median data ratebefore the eastbound and westbound peaks, as seen in Figure 5.12a. This isdue to the combination of lots of aircraft with DA2GC and a small number ofaircraft over the ocean, which leads to the bandwidth being shared between afew aircraft. In Figure 5.12b, the data rates in different longitudes for the 20MHz and 200 MHz cases are being compared. The median data rate of aircraftover the North Atlantic in the high bandwidth scenario has a minimum of 184Mbps, which is higher than the peak data rates of current SATCOM system(70-100 Mbps). Therefore, SATCOM could be replaced by DA2GC and A2A,if the bandwidth is increased, or a cooperation between the two systems couldbe implemented, which would give even better results.

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Chapter 6

Conclusions and FutureWork

In this thesis, the performance of A2A was evaluated. Aircraft mobilityover the North Atlantic was modeled and an aeronautical ad-hoc network wassimulated. The effect of different parameters on the connectivity and data ratewas analyzed.

Setting a threshold on the data rate decreases connectivity. This decrease ismore noticeable in the middle of the ocean, due to the lack of DA2GC connectionin this area. However, the connected aircraft have higher data rates. Increasingthe number of maximum connection per aircraft helps in sharing the bandwidthmore evenly, because traffic can be routed more efficiently to aircraft in themiddle of the ocean. Regarding the steering angle of antennas, a wider maximumangle increases the link choices, which results in higher connectivity and betterbandwidth allocation. A maximum steering angle of 30◦ would be enough toprovide 80% connectivity even in low density times. Finally, having a widerbeamwidth results in more interference, and eventually less connectivity andhigher deviation of data rates.

When considering subgroups of all aircraft, the density is a more importantfactor. For low aircraft densities, the connectivity drops significantly, down toan average of 50.85% compared to 98.14% and 92.86% for high and mediumdensities respectively. The data rates of the low density case are slightly betterdistributed, due to a smaller number of connected aircraft over the ocean andlower interference. As a conclusion, A2A is feasible for subgroups of aircraft,such as members of Star Alliance, however only in times of medium and highdensity.

Using LTE standards is not enough to compete with SATCOM throughput,which gives peak data rates of 70-100 Mbps. By having increased spectrumthough, 50% of the aircraft connected with DA2GC and A2A combination havehigher data rate than any aircraft connected with SATCOM. To compare A2Awith SATCOM, a bandwidth of 200 MHz was assumed. A higher bandwidthresults in higher thermal noise at the receivers and lower SINR values. Thisdecrease in SINR affects the achieved data rate. The median data rate of aircraftin the middle of the ocean is increased by a factor of The SINR may dropsignificantly in high distances, decreasing connectivity in low densities. However,

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when the density is high, the difference in connectivity is insignificant. Themedian data rate of aircraft in the middle of the ocean is increased by a factorof 7.36.

The work of this thesis is an evaluation of A2A networks. After analyzingthe results of Chapter 5, it is clear that it is a viable concept. There are however,some restrictions. Most importantly, there needs to be a cooperation betweenairlines. Aircraft density plays the most important role in the feasibility of A2Aand aircraft belonging to one, or a few airlines is not enough. Additionally, theeffect of the parameters should be considered and evaluated. The requirementsset for A2A, together with their expected costs and profit will be the factorsthat determine if it is worth implementing.

Even though the parameters have realistic values, there is still room forimprovement regarding some of the assumptions. First, a more detailed flightmodel can be implemented. Having access to flight data from the airlines andnot from third-party websites would allow a detailed model that represents re-ality more to be created. Shadowing effects due to the bodies of aircraft is notconsidered. Despite the fact that proper antenna placement can mitigate shad-owing effects, it is still something that needs to be considered in real systems.Another limiting factor of the implementation is that two consecutive time in-stances are completely independent. In reality, a link between two aircraft wouldbe maintained as long as possible, if it does not degrade the network too much.Even when the topology changes, it would be useful to assign weights to thenew potential connections, depending on the angle that the antennas need tobe steered, in order to minimize the time required to establish new connections.Finally, other parameters should also be evaluated, such as the delay of thereceived signals.

The simulations in this work only consider the North Atlantic area. Theresults can be extended to other oceanic areas, or areas without air to groundinfrastructure to assess the performance of A2A. Satellites can cooperate withA2A, providing coverage to aircraft which do not have a path to a ground basestation, or whose link quality is bad. After a few modifications, the simulationscan be run to evaluate other similar networks, such as drone or ship networks.

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