Technological Trends and Key Communication Enablers for eVTOLs
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Technological Trends and Key Communication
Enablers for eVTOLs
Abdullah Abu Zaid, Baha Eddine Youcef Belmekki, and Mohamed-Slim Alouini,
Abstract—The world is looking for a new exciting form of
transportation that will cut our travel times considerably. In
2021, the time has come for flying cars to become the new
transportation system of this century. Electric vertical take-off
and landing (eVTOL) vehicles, which are a type of flying cars, are
arXiv:2110.08830v1 [eess.SP] 17 Oct 2021
predicted to be used for passenger and package transportation in
dense cities. In order to fly safely and reliably, wireless commu-
nications for eVTOLs must be developed with stringent eVTOL
communication requirements. Indeed, their communication needs
to be ultra-reliable, secure with ultra-high data rate and low
latency to fulfill various tasks such as autonomous driving,
sharing a massive amount of data in a short amount of time,
and high-level communication security. In this paper, we propose
major key communication enablers for eVTOLs ranging from the
architecture, air-interface, networking, frequencies, security, and
computing. To show the relevance and the impact of one of the
key enablers, we carried out comparative simulations to show the
superiority compared to the current technology. We compared Fig. 1: CityAirbus eVTOLs [1].
the usage of an air-based communication infrastructure with a
tower mast in a realistic scenario involving eVTOLs, delivery
drones, pedestrians, and vehicles. The eVTOL vehicle is a type of flying car that is powered
Index Terms—Flying cars, eVTOL, flying taxi, NTFP, UAV, by electricity and can take off and land vertically. It is
HAP, LEO, RIS, NOMA, RSMA, SDN, NFV, RAN slicing, RF, predicted they will be heavily used to transport packages
mmWave, FSO, VLC, blockchain, quantum computing, edge and passengers in the upcoming decade. This will ease the
computing, cloud computing, fog computing, digital twin.
increasing pressure on ground transportation and utilize the un-
derused near-ground air space. They are more environmentally
I. I NTRODUCTION friendly, and require less travel time than traditional ground
transportation. Companies are racing to design and implement
Since the invention of automobiles, humans have always eVTOLs such as the CityAirbus four-seat eVTOL made by
dreamed of building flying cars, which at that time, were more Airbus [1]. However, this comes with many hurdles, such as
of a science fiction fantasy and The Jetsons trademark than finding a sustainable energy source, passing certifications and
a reality. Today, flying cars are no longer fiction, but rather regulations, and connecting to wireless networks.
an obvious next step for a better future. They are expected Even though communication is of utmost importance for
to alleviate urban congestion, reduce travel time, and offer a eVTOLs, at the time of writing this paper, there are only a
sustainable solution for travel. limited number of works that deal with eVTOLs from the
Although the name “Flying car” is used often to describe a communication aspect. In [2], the authors presented a general
flying vehicle, it is, in reality, an umbrella term. Indeed, flying survey about flying cars systems, where they gave a detailed
cars are categorized depending on their take off/landing mode: categorization of flying cars and their challenges. The authors
horizontal, vertical, or hybrid. Horizontal take off/landing in [3] are the first to tackle the wireless communication aspect
flying cars fly like airplanes where they need long runways of eVTOLs. They proposed several alternatives for cellular
and are difficult to deploy in dense urban cities. On the networks such as tethered balloons, high-altitude platforms
other hand, vertical take-off/landing cars can take off from (HAPs), and satellites. In this paper, we propose and speculate
almost anywhere and can be deployed in cities easily. Hybrid on what can be the major key communication enablers for eV-
take off/landing cars can either take off horizontally and land TOLs ranging from the architecture, air-interface, networking,
vertically, or the opposite. Flying cars can also be categorized frequencies, security, and computing.
depending on their energy source, the most popular are hy- The rest of this paper is organized as follows. In section
drocarbon and electricity. The type of flying cars that have II, We present the main requirements for communications in
attracted the most attention are electric vertical take-off and eVTOLs. In section III, the key enablers for communications
landing (eVTOL) vehicles. in eVTOLs are described. In section IV, we carry out simula-
tions to compare one key enabler with the current technology
for eVTOL communications. Finally, we conclude this paper
in In section V.
2
Fig. 2: Key communication enablers for eVTOLs.
II. E VTOL C OMMUNICATION R EQUIREMENTS B. High Data Rate with Low Latency Communications
Connecting eVTOLs to communication networks and Since eVTOLs have to exchange massive data in an ex-
among themselves is crucial. Indeed, the communication needs tremely short amount of time, they must transmit an ultra-
to be ultra-reliable, secure with high data rate and low latency. high data rate with low latency. This is because eVTOLs fly
Additionally, passengers should enjoy a stable connection at very high speeds (300 km/h) and they can be partially
to voice and data networks with a satisfactory Quality of or fully autonomous. Hence, they deal and react in real-
Service (QoS). Another aspect that makes the reliability and time to any critical occurrence. In fact, eVTOLs need to
robustness of eVTOL communications extremely important constantly share their position, receive other eVTOL positions,
is the autonomy. In fact, researchers and industrialists have and other flying platforms such as UAVs. They also need to
made progress towards making autonomous vehicles a reality. be constantly informed about the air traffic status, weather
Hence, eVTOLs are aimed to have partial then full autonomy conditions, and any unexpected events. To that end, new air
which require a stable and reliable connection to the network interfaces should be used to allow the required data rate and
with minimum delay and high data rate. Hence, eVTOL latency for eVTOLs. Additionally, different frequency bands
communications have to be reliable, to achieve high data rates must be explored and used for eVTOLs.
with low latency, and to be secure.
C. Secure Communications
A. Reliable Communications
One of the main focuses when designing eVTOLs is safety
Ensuring reliable communications between eVTOLs and and security. Since eVTOLs will most likely be flying in
communication networks will be challenging, either significant urban cities and over pedestrians, they must be safe for
changes must be made to existing systems, or new systems both people on the ground and for passengers. Hence, secure
must be designed. For example, using existing cellular net- communications must be implemented. Furthermore, secure
works without adjustment is infeasible, since the antennas are communications are vital to prevent eVTOLs from being
designed in such a way to propagate towards the ground. Also, hacked or jammed. Indeed, it will be disastrous if malicious
line-of-sight (LOS) of cellular towers in urban areas will be hackers could take the control of one or several eVTOLs and
blocked due to high buildings. Therefore, new architectures the damage that results can be fatal for pedestrians, eVTOLs,
have to be adopted such as aerial-based platforms and new passengers, and also buildings. To this end, different tech-
network technology must be used. nologies can be used to guarantee the communication security
3
such as blockchain, machine learning security algorithms and air for long periods of time which allows reliable connections
quantum computing. to eVTOLs [6].
2) Networked Tethered Flying Platforms (NTFPs): NTFPs
III. K EY C OMMUNICATION E NABLERS are flying platforms that are connected to the ground via a
tether. The tether provides power and data to the platforms,
allowing them to stay in the air for long periods of time, to
have an increase in backhaul capacity, and increased security
in comparison to UAVs/HAPs. There are many types of
NTFPs, such as tethered UAVs, tethered balloons, tethered
LEO satellite LEO satellite blimps, and tethered Helikites [6]. These types fly at various
altitudes, ranging from 150 m to 5 km. Low altitude NTFPs
can offer low latency communications while high altitude
NTFPs provide high coverage. NTFPs can provide eVTOLs
with a reliable connection throughout take-off, cruising, and
landing. Using NTFPs still comes with a few challenges.
NTFP For example, tethered HAPs require multiple kilometers of
eVTOL tethers making it heavy, hence the materials used need to be
eVTOL lightweight. In section IV, we compare the use of NTFPs and
cellular towers for communication in eVTOLs.
UAV 3) Low Earth Orbit (LEO) Satellites: LEO satellites are
,,,,
UAV satellites that orbit relatively close to earth, from around 160
UAV
km to 1000 km [7]. Due to their high altitude, LEO satellites
have global coverage, which allows connecting eVTOLs dur-
ing inter-city trips. LEO satellites can be deployed in great
numbers to form a dense network that can offer continuous
connectivity to eVTOLs with high reliability. Furthermore,
LEO satellites can provide accurate position, speed, and alti-
tude of the eVTOLs. Using LEO satellites will help in meeting
the increasing connectivity demand in the skies.
Fig. 3: Proposed architectures for eVTOLs. B. Air Interface
Traditional multiple access schemes, such as frequency di-
vision multiple access (FDMA), time division multiple access
A. Architecture (TDMA), and code division multiple access (CDMA), offered
For a long time, almost all wireless communication base fairly good performance in terms of throughput. However, with
stations were ground based. This worked relatively well when future generations of wireless communications and the new
most users were on the ground. However, with flying cars technologies that will utilize them, superior performance is
and platforms getting close to deployment, the need for more expected. For example, eVTOLs require very low delay for
favorably placed base stations is critical. In this section, we enhanced safety while flying, as well as high throughput for
study different architectures that make use of aerial platforms data rate heavy applications like tactile communications and
to supply eVTOLs with communications. brain-vehicle interfacing [8]. This section studies new and
1) Unmanned Aerial Vehicles (UAVs) & High Altitude Plat- promising multiple access schemes.
forms (HAPs): UAVs are small unmanned vehicles that are 1) Non-orthogonal multiple access (NOMA): NOMA is a
usually deployed in groups and distributed around an area multiple access scheme that allows users to send simultane-
to maximize coverage, they fly at altitudes around 150 m ously on the same frequency, at the same time, with the same
[4]. UAVs are cheaper to deploy than cellular towers and code, but using different power levels [9]. Decoding is done at
they provide larger coverage due to their altitude. UAVs can each user by successive interference cancellation (SIC), where
be deployed easily, and they can be reprogrammed to other the unwanted part is decoded then removed from the signal.
locations quickly and precisely. They can be used to connect Users with low channel gain in NOMA are assigned more
eVTOLs during take-off, landing, and low altitude flights. power, while users with high channel gain are assigned less
UAVs can maneuver to provide a constant LOS connection power. Since users send using the same resources, NOMA will
to eVTOLs. On the other hand, HAPs are platforms that fly in achieve higher spectral efficiency than traditional orthogonal
the stratosphere around 20 km [5], at this height, HAPs have multiple access schemes. Furthermore, since users do not
a large coverage area, and they can connect with eVTOLs need to wait for their time or frequency slots to transmit
using LOS signals. Examples for HAPs are blimps, which are information, NOMA can serve more users with lower latency.
lighter-than-air vehicles that use a buoyant gas to fly, and solar- Hence, NOMA can improve communications for eVTOLs in
powered planes that fly against the wind. HAPs can stay in the dense urban areas, and it can help satisfy latency demands.4
Moreover, NOMA can be used for eVTOLs according to
two types of priority rankings, channel priority ranking and
QoS priority ranking. When considering the channel priority
ranking, eVTOLs with weaker channel gains will have more
power allocated to them and inversely. This will guarantee a
fairness among eVTOLs. However, when considering the QoS
priority ranking, eVTOLs will be ranked according to their
needs in terms of QoS. For instance, an eVTOL requiring
urgent transmission will have more power allocated compared ,,,,
to an eVTOL with less important transmission. The authors
in [10] proposed a cooperative NOMA scheme that lets users
with better channel conditions to the base station (BS) decode
and relay messages for other users that have a poor connection
to the BS. This scheme might be a promising solution to enable
reliable communications between eVTOLs. RIS
2) Rate-splitting multiple access (RSMA): RSMA is a new
multiple access framework that, similar to NOMA, users can
send simultaneously on the same resources. The difference is
that in RSMA, part of the interference is decoded and the
other part is treated as noise. RSMA is a scheme midway
between traditional multiple access schemes and NOMA,
where traditional schemes fully treat the interference as noise, Fig. 4: The usage of RIS for eVTOLs.
and NOMA fully decodes the interference. RSMA allows for
another degree of freedom to improve spectral efficiency and
data rate. equipment to be installed in eVTOLs, lowering the cost, and it
3) Reflective Intelligent Surfaces (RISs): For a long time, will enable the eVTOLs network to be upgraded easily without
enhancements in wireless communications were mostly ap- hardware replacements. It is a promising solution that is used
plied in the transmitter and the receiver, while the channel extensively today in data centers [12].
was given. Recently, RISs gained popularity as a way to 2) Network function virtualization (NFV): NFV allows
alter the channel to enhance the performance of wireless software functions to be applied in virtual machines contained
communications [11]. RISs are a low-cost passive reflective in servers, thus separating network services from hardware and
surface that works by changing the phase and/or amplitude lowering the cost of operation [13]. It is a recent network archi-
of the signal, in a way that improves the signal power in the tecture that was introduced by the industry to reduce the cost of
receiver’s direction. RISs are promising as a key enabler for operating and implementing new technologies, since deploying
communications in eVTOLs, they can be installed on the walls new functionalities required installing new hardware. NFV is a
of buildings in urban cities to improve signals during take-off promising network architecture for eVTOL communications,
and landing, and provide a strong link around the corner where where new functionalities can be integrated using software,
LOS is not available. Additionally, RISs can be installed on thus allowing scalable and cost-effective eVTOL networks.
the rooftops of buildings to strengthen signals coming from As mentioned earlier, eVTOL networks will require strong
aerial platforms or satellites. security and will need to execute complex functions, NFV
allows for these functions to be executed virtually.
3) RAN Slicing: Slicing a RAN network means dividing
C. Networking the network infrastructure into multiple logical and virtual
To achieve a reliable, flexible, and automated network networks each for a different use-case. Hence, the eVTOL
management, a suitable networking algorithm must be chosen. network will be separated into virtual networks depending
1) Software defined networking (SDN): Achieving desired on the application. For instance, a slice is used for inter-
qualities of eVTOL networks like flexibility and programma- net connection for eVTOL passengers, another slice is used
bility using traditional networks is hard, since traditional net- to transport safety information between eVTOLs and their
works functionalities’ are implemented using dedicated hard- respective control towers, and another slice can be used to
ware that requires manual configuration. SDN uses software to share location information among eVTOLs [14]. That way,
control and intelligently reconfigure the network, it does this the eVTOL network is sliced according to the requirements of
by separating the data plane from the control plane. The data each application. Consequently, the right amount of resources
plane consists of simple forwarding devices, while the control is allocated to each slice. Additionally, separating the network
plane does all the computations in a centralized node which into slices can mitigate the attacks on the network by limiting
then distributes commands to the forwarding devices. With the cyber attack on a single slice and leaving the other slices
this architecture, SDN enables a flexible and programmable unharmed. In that case, the level of security of each slice will
communication network for eVTOLs, and allows for easy be different, for example, communication between an eVTOL
scalability of the network. It will allow for simpler and cheaper and their assigned control tower will have ultra-high security5
and robustness compared to a non-critical slice that is used be installed on buildings and on rooftops to have a LOS path.
to provide infotainment for eVTOL passengers. This will be Furthermore, VLC communications is highly secure since an
cost-effective since high security mechanisms come with a attacker would need to intercept the light directly to receive
high cost. the signal.
D. Spectrum E. Security
The operating frequencies must be chosen wisely. The To prevent a hijacking or jamming attack in the eVTOL
frequency spectrum is diverse, some parts are highly congested traffic control networks, and to protect the privacy of passen-
because of their favorable characteristics, and other parts are gers’ data, strong state-of-the-art security applications must be
underutilized because of the challenges they present. implemented.
1) Radio frequency (RF): RF is the frequency range from 1) Blockchain: Blockchain is a decentralized and dis-
around 20 kHz to around 30 GHz. It is the most used part tributed technology that is made up of blocks, the blocks
of the spectrum for wireless communications because of its are connected to each other in a cryptographic link [16]. It
favorable characteristics. It is congested and vulnerable to enables very secure device-to-device communication, where
attacks since the signals propagate radially, however these each device validates the information sent by the sender using
challenges can be overcome by advanced applications of a key. Blockchain can be implemented in a distributive manner
waveforms, encryption, and coding. RF signals travel relatively in eVTOL networks, where nodes can be eVTOLs, control
far and can penetrate walls and go around obstacles, which towers, pedestrian mobile devices, and ground vehicles. De-
makes them helpful for eVTOLs in congested urban areas, centralization in blockchain security prevents a single point of
where LOS to the base station is not always available. They are failure, which means a single compromised node will not have
also suitable for long distance trips. Furthermore, RF signals an impact on the rest of the network.
are not affected greatly by adverse weather conditions. 2) Machine Learning (ML): ML is a really active field of
research, where it’s been implemented in almost every technol-
20 kHz -30 GHz 30-300 GHz 187-370 THz 430-790 THz ogy including security. ML relies on experience and acquired
data to build an analytical model and identify patterns. ML
FSO
RF mmWave VCL can be implemented to secure communications, where the ML
algorithm can detect anomalies, misuses, or intrusions from
Fig. 5: Operating frequencies for eVTOLs.
potential attackers, using data from recorded attacks in the
past. Authors in [17] listed ML algorithms and their uses to
2) Millimeter wave (mmWave): mmWave communications
achieve secure communications in 6G vehicular networks. For
uses the frequency range from around 30 GHz to around
example, neural networks can be used for misuse detection,
300 GHz. It has been researched heavily the past decade. It
support vector machines for malicious attacks detection, and
offers a much bigger bandwidth than RF and has potential
Bayes learning for mobility and anomaly detection. Hence,
for very high data rates, enabling the use of data rate heavy
ML can be used to secure eVTOL communications by de-
applications for eVTOLs. Furthermore, mmWave signals have
tecting when an attack happens, or by detecting suspicious
very short wavelengths, which makes small antennas possible,
behavior, and quickly adjusting the network to protect against
reducing the size of circuits on eVTOLs and allowing for a
them.
large number of antennas to improve capacity and range. Using
3) Quantum Computing: Quantum computing is the study
mmWave comes with challenges, frequencies this high tend
of exploiting the quantum mechanics characteristics to do
to attenuate quickly. Furthermore, mmWave communications
extremely fast computations. Quantum computers can solve
are affected by atmospheric absorption like rain and snow.
certain problems 3 million times faster than a classical com-
However, solutions are proposed to counteract these limita-
puter [18]. Quantum computers can offer extremely strong
tions, like massive multiple-input multiple-output (MIMO) and
security by using the innate quantum entanglement property
beamforming [15].
of particles, which virtually cannot be hacked [19]. It works
3) Free space optics (FSO)/Visible light communications
when the quantum state of two particles become entangled
(VLC): FSO communications uses the frequency range 187
together, then their quantum states will be dependent on each
THz to 370 THz. This part of the spectrum is unlicensed
other, moreover, a device wishing to communicate securely
and underutilized, it also has practically unlimited bandwidth,
to another device will send the entangled particle to it using
allowing for very high data rates. FSO communications need
quantum key distribution (QKD). An attacker that wishes to
a LOS path between transmitters and receivers, which makes
obtain the key must be physically present at the device. This
them a suitable solution for connecting eVTOLs during flight.
method, and the fact that quantum computers are based on
eVTOLs can benefit from FSO communications by having
true randomness, will allow eVTOLs to have secure commu-
an unobstructed LOS to a UAV/HAP or a LEO satellite.
nications.
VLC uses visible light to communicate, it is the frequency
range from 430 THz to 790 THz. Similar to FSO, it has
potential for very high data rates, and needs a LOS path F. Computing Paradigm
between communicating devices. VLC can be implemented As technology progresses and more complex algorithms are
by using the eVTOL headlights and taillights, lights can also invented, and with the exponential increase of the number of6
world network of eVTOLs, all synchronized in real time, then
Slower
simulations can be run to return answers back to the eVTOLs
Cloud
Cloud Server about best routes and to warn about possible congestions.
Processing Speed
Computing
Response Time
A cloud-based digital twin for vehicle communications was
Fog
proposed in [21].
Fog Server Fog Server Fog Server 3) Mobile edge computing (MEC): Instead of off-loading
Computing
computations to a center that might be far away which
Faster
Edge Edge Edge Edge Edge Edge Edge
introduces delay, MEC technology have distributed computing
Computing servers at the edge of the network, in close proximity to com-
munication devices [22]. MEC servers are generally designed
to be placed at cellular BSs [23]. However, authors in [24]
proposed using UAVs as MEC servers, which is a suitable
design for eVTOLs. MEC offers high reliability to eVTOL
networks, since many mobile units can be deployed in the
vicinity of eVTOLs, and there is less possibility of a network
issue that prevents access to them.
,,,,
Pout vs Rate
0.9
0.8
0.7
0.6
Fig. 6: Computing Paradigm for eVTOLs.
0.5
Pout
communication devices and network sizes, the need for high 0.4
computational power became crucial. This prompted research
into innovative computing paradigms that help resource- Tower Mast - Downlink
0.3
limited devices in computations. In this section we study Tower Mast - Uplink
NTFP - Downlink
technologies that enable eVTOLs to have access to strong NTFP - Uplink
computing power, without the need for expensive on-board
0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2
hardware. R (bit/sec/Hz)
1) Cloud computing/Fog computing: Cloud computing of-
fers computational resources to devices, often deployed on the Fig. 7: Outage probability as a function of spectral efficiency.
internet. The devices send their data to the cloud where the
required computations are done. Then, if needed, results are
disseminated back. Cloud computing could help eVTOL net- IV. S IMULATION
works run complex algorithms that require a lot of computing In this section, we show the improvement of the perfor-
resources, such as route planning and weather analysis. Fur- mance when using an NTFP-based communication infrastruc-
thermore, cloud computing could contain quantum computers ture and a traditional tower to connect eVTOLs. We simulate a
that might be used for security in eVTOL networks. Similar real-life scenario composed of delivery drones since they will
to cloud computing, fog computing provides computational be ubiquitous during the next decades along with eVTOLs.
resources, however closer to the end-devices which means less The simulation also involves vehicular communications and
latency and less bandwidth usage on the network. In eVTOL mobile communications from pedestrians. The area of the
networks, cloud computing can be used for computation heavy simulation is set to 1 km2 which corresponds to the tenth
and delay-tolerant applications, like weather reporting, and of downtown Los Angeles area.
fog computing can be used for applications that are delay The eVTOLs fly at an altitude of 300 m and are distributed
intolerant, like route planning and traffic simulation. according to a two-dimensional (2D) binomial point process
2) Digital Twin: A digital twin is a virtual real-time copy with intensity λeV = 10 eVTOLs; the UAVs fly at 100 m and
of a physical entity, which can be used to synchronize with are distributed according to a 2D homogeneous Poisson point
and keep track of the real-world entity [20]. In a multi-device process (HPPP) with intensity λU AV = 5 × 10−5 UAV/m2 ,
network, devices send their data to the digital twin, then it pedestrians and ground vehicles are distributed according to
can build a virtual world where every device is synchronized a one-dimensional (1D) HPPP with intensity λp = 1 × 10−3
with others. The digital twin can then simulate the world and pedestrian/m2 [25]. The NTFP is a tethered blimp flying at
send back its decisions. Digital twins are a good computing an altitude of 500 m, and the cellular tower mast is 50 m
technology for eVTOL communications, where a complete high. We refer to downlink as the connection from NTFP/tower
overview of the eVTOLs is needed for safety and route mast to eVTOL, and uplink to the connection from eVTOL to
planning. A digital twin can be built that mirrors the real NTFP/tower mast.7
Figures 7 to 10 show the outage probability of eVTOL P out vs UAV Intensity - Uplink
1
transmissions. We see from Fig. 7 that by using NTFPs,
a better performance is achieved in comparison with tower 0.9
Tower Mast
masts for eVTOL communications, whether it is for uplink NTFP
0.8
or downlink transmission. The gap in performance is more
significant for the uplink transmission. This is due to the fact 0.7
that pedestrians, vehicles, and the delivery drones are closer
Pout
to the tower mast than to the NTFP. Hence, the interference 0.6
is stronger at the tower mast. This shows a clear advantages 0.5
of an NTFP-based solution that mitigate the effect of ground
interference. 0.4
Figure 8 and 9 show the outage probability as a function 0.3
of the pedestrian/vehicle intensity and the UAV intensity,
respectively. The outage probability increases with increasing 0.2
0.5 1 1.5 2 2.5 3 3.5 4 4.5 5
intensity in the tower mast architecture, but the increase is (UAV/m 2 ) 10 -6
UAV
minimal in the NTFP case. This is because the interfering
devices are closer to the tower mast than the NTFP. We can Fig. 9: Outage probability as a function of UAV intensity.
clearly notice that NTFPs are more suitable than tower masts
for eVTOLs while coexisting with pedestrians and delivery P out vs Number of eVTOLs
1
UAVs. In fact, there is a significant gap in the performance,
which makes NTFPs a better candidate for eVTOL communi- 0.9
cations. 0.8
0.7
P out vs Pedestrian Intensity - Uplink
Tower Mast - Downlink
1 0.6 Tower Mast - Uplink
NTFP - Downlink
Pout
0.9 0.5 NTFP - Uplink
Tower Mast
NTFP 0.4
0.8
0.3
0.7
0.2
Pout
0.6
0.1
0.5 0
0 5 10 15 20 25
Number of eVTOLs
0.4
0.3
Fig. 10: Outage probability as a function of number of
eVTOLs.
0.2
0 0.002 0.004 0.006 0.008 0.01
2
P
(pedestrian/m )
vehicles, unreliable current cellular networks, and the impor-
Fig. 8: Outage probability as a function of pedestrian/vehicle tance of safety and security for eVTOLs. Finally, a simula-
intensity. tion comparing between two communication architectures was
done, where it was shown that NTFPs offer better performance
In Fig. 10, the outage probability for downlink and uplink than cellular towers.
is drawn against the number of eVTOLs flying, the outage
probability increases with increasing number of eVTOLs. R EFERENCES
However, the outage probability is greater when using tower
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and applications,” arXiv preprint arXiv:2010.13509, 2020. supervision of Professor Mohamed-Slim Alouini at
[7] [Online]. Available: https://www.esa.int/Enabling{ }Support/Space{ } King Abdullah University of Science and Technol-
Trans\portation/Types{ }of{ }orbits ogy (KAUST). Abdullah obtained his bachelor’s de-
[8] Z. Liu, H. Lee, M. O. Khyam, J. He, D. Pesch, K. Moessner, W. Saad, gree in Electrical Engineering from The University
H. V. Poor et al., “6G for vehicle-to-everything (v2x) communications: of Jordan in 2020. During his engineering studies,
Enabling technologies, challenges, and opportunities,” arXiv preprint he did research in wireless sensor networks and
arXiv:2012.07753, 2020. cognitive radio. His current research interests include
[9] Y. Saito, A. Benjebbour, Y. Kishiyama, and T. Nakamura, “System-level stochastic geometry modeling, and networked flying
performance evaluation of downlink non-orthogonal multiple access platforms.
(NOMA),” in 2013 IEEE 24th Annual International Symposium on
Personal, Indoor, and Mobile Radio Communications (PIMRC). IEEE,
2013, pp. 611–615.
[10] Z. Ding, M. Peng, and H. V. Poor, “Cooperative non-orthogonal multiple
access in 5g systems,” IEEE Communications Letters, vol. 19, no. 8, pp.
1462–1465, 2015.
[11] Q. Wu and R. Zhang, “Towards smart and reconfigurable environment:
Intelligent reflecting surface aided wireless network,” IEEE Communi-
cations Magazine, vol. 58, no. 1, pp. 106–112, 2019.
Baha Eddine Youcef Belmekki received the B.S.
[12] C.-Y. Hong, S. Mandal, M. Al-Fares, M. Zhu, R. Alimi, K. N. B.,
degree in Electronics, and the M.Sc. degree in Wire-
C. Bhagat, S. Jain, J. Kaimal, S. Liang, K. Mendelev, S. Padgett,
less Communications and Networking from Univer-
F. Rabe, S. Ray, M. Tewari, M. Tierney, M. Zahn, J. Zolla, J. Ong,
sity of Science and Technology Houari Boumediene,
and A. Vahdat, “B4 and after: Managing hierarchy, partitioning, and
Algiers, Algeria, in 2011 and 2013, respectively.
asymmetry for availability and scale in google’s software-defined wan,”
From 2013 to 2014, he was with the Department
in Proceedings of the 2018 Conference of the ACM Special Interest
of Radio Access Network, Huawei Technologies,
Group on Data Communication, ser. SIGCOMM ’18. New York, NY,
Algiers, Algeria. From 2014 to 2016, he was an
USA: Association for Computing Machinery, 2018, p. 74–87. [Online].
Assistant Professor with University of Science and
Available: https://doi.org/10.1145/3230543.3230545
Technology Houari Boumediene, Algiers, Algeria.
[13] W. Zhuang, Q. Ye, F. Lyu, N. Cheng, and J. Ren, “SDN/NFV-
He obtained his Ph.D. degrees in Wireless Commu-
empowered future iov with enhanced communication, computing, and
nications and Signal Processing from the National Polytechnic Institute of
caching,” Proceedings of the IEEE, vol. 108, no. 2, pp. 274–291, 2019.
Toulouse in 2020. From 2019 to 2021, he worked as teaching and research
[14] A. Weissberger. (2021, Jan.) 5G network slicing tutorial +
assistant at the National Polytechnic Institute of Toulouse, France. He is
Ericsson releases 5G RAN slicing software. Accessed Sept.
currently a postdoctoral research fellow at Communication Theory Labo-
28 2021. [Online]. Available: https://techblog.comsoc.org/2021/01/26/
ratory of King Abdullah University of Science and Technology (KAUST).
5g-network-slicing-tutorial-ericsson-releases-5g-ran-slicing-software/
His research interests include cooperative communications, millimeter wave
[15] A. L. Swindlehurst, E. Ayanoglu, P. Heydari, and F. Capolino,
communications, non-orthogonal multiple access systems, stochastic geometry
“Millimeter-wave massive mimo: The next wireless revolution?” IEEE
analysis of vehicular networks, and networked flying platforms.
Communications Magazine, vol. 52, no. 9, pp. 56–62, 2014.
[16] D. C. Nguyen, P. N. Pathirana, M. Ding, and A. Seneviratne,
“Blockchain for 5G and beyond networks: A state of the art survey,”
Journal of Network and Computer Applications, vol. 166, p. 102693,
2020.
[17] F. Tang, Y. Kawamoto, N. Kato, and J. Liu, “Future intelligent and
secure vehicular network toward 6G: Machine-learning approaches,”
Proceedings of the IEEE, vol. 108, no. 2, pp. 292–307, 2019.
[18] A. D. King, J. Raymond, T. Lanting, S. V. Isakov, M. Mohseni,
G. Poulin-Lamarre, S. Ejtemaee, W. Bernoudy, I. Ozfidan, A. Y. Smirnov Mohamed-Slim Alouini (S’94-M’98-SM’03- F’09)
et al., “Scaling advantage over path-integral monte carlo in quantum sim- was born in Tunis, Tunisia. He received the Ph.D.
ulation of geometrically frustrated magnets,” Nature communications, degree in Electrical Engineering from the California
vol. 12, no. 1, pp. 1–6, 2021. Institute of Technology (Caltech), Pasadena, CA,
[19] F. Tariq, M. R. Khandaker, K.-K. Wong, M. A. Imran, M. Bennis, and USA, in 1998. He served as a faculty member in the
M. Debbah, “A speculative study on 6G,” IEEE Wireless Communica- University of Minnesota, Minneapolis, MN, USA,
tions, vol. 27, no. 4, pp. 118–125, 2020. then in the Texas A&M University at Qatar, Educa-
[20] Z. Wang, X. Liao, X. Zhao, K. Han, P. Tiwari, M. J. Barth, and tion City, Doha, Qatar before joining King Abdullah
G. Wu, “A digital twin paradigm: Vehicle-to-cloud based advanced driver University of Science and Technology (KAUST),
assistance systems,” in 2020 IEEE 91st Vehicular Technology Conference Thuwal, Makkah Province, Saudi Arabia as a Profes-
(VTC2020-Spring). IEEE, 2020, pp. 1–6. sor of Electrical and Computer Engineering in 2009.
[21] X. Liao, Z. Wang, X. Zhao, K. Han, P. Tiwari, M. Barth, and G. Wu, His current research interests include the modeling, design, and performance
“Cooperative ramp merging design and field implementation: A digital analysis of wireless communication systems.
twin approach based on vehicle-to-cloud communication,” IEEE Trans-
actions on Intelligent Transportation Systems, 2020.
[22] K. Zhang, Y. Mao, S. Leng, Y. He, and Y. Zhang, “Mobile-edge
computing for vehicular networks: A promising network paradigm with
predictive off-loading,” IEEE Vehicular Technology Magazine, vol. 12,
no. 2, pp. 36–44, 2017.
[23] A. Ahmed and E. Ahmed, “A survey on mobile edge computing,” in
2016 10th International Conference on Intelligent Systems and Control
(ISCO), 2016, pp. 1–8.
[24] F. Zhou, R. Q. Hu, Z. Li, and Y. Wang, “Mobile edge computing in
unmanned aerial vehicle networks,” IEEE Wireless Communications,
vol. 27, no. 1, pp. 140–146, 2020.
[25] B. E. Y. Belmekki, A. Hamza, and B. Escrig, “Cooperative vehicular
communications at intersections over nakagami-m fading channels,”
Vehicular Communications, vol. 19, p. 100165, 2019.You can also read
