Unmanned aerial vehicles (UAVs) are expected to be an important component of thenext generation of mobile networks because of its low cost and flexible connectiv-ity. Telecommunication organizations are currently exploring possibilities for servingUAVs with existing and further cellular network and industries beginning to trialearly prototypes of the cellular connected UAV while scholars are in full swing intro-ducing mathematical and algorithmic solutions to address interesting new problemsarising from the cellular connected UAV communication system. Compared to theconventional mobile communication system with terrestrial users, cellular- connectedUAV communication possesses substantially different characteristics that present newresearch challenges as well as opportunities. Also, most of the research on cellular -connected UAV are still at initial stages. As a result, there are only a few simulationsand experimental results available. Because of that, the objective of this research isa redesign physical layer of unmanned aerial vehicle communication system to alignwith the fifth generation cellular network technology
Physical Layer Redesign for Unmanned Aerial Vehicle (UAV) Communication in 5G
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Physical Layer Redesign for Unmanned
Aerial Vehicle (UAV) Communication in
5G
Chathuranga Madhushan Basnayaka
Supervisors: (Prof.) Dushantha Nalin Kumara and (Dr.) Udesh Sanjika
Oruthota
National Research Tomsk Polytechnic University / Center for
Telecommunications Research,SLTC,Sri Lanka
December 4, 2020
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Robot-Piloted Plane From 1947
”Robot-Piloted Plane Makes Safe Crossing of Atlantic...”
THE NEW YORK TIMES, September 23, 1947
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Source: Buchholz, K. amp; Richter, F., 2019. Infographic: Commercial Drones are Taking Off. Avail-
able at:https://www.statista.com/chart/17201/commecial-drones-projected-growth/
4. 4/42
Unmanned Aerial Vehicle Applications
Source:C. M. W. Basnayaka, K.O. Lakamal and D. N. K.Jayakody, 2019. “The Era of the 5G Drone is
Ahead, Are We Ready?” Vidurava, 36(4), pp.19-21
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Basic Requirement: Wireless Communications for UAVS
Control and Non payload Communications (CNPC)
Payload Communications
Wireless Communications for UAVs: Existing Technologies
Unlicensed spectrum (2.4 GHz)
Main limitations:
Unreliable
Insecure
Vulnerable to Interference
Limited data rate
Visual line of sight (LoS) operation
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Communication and Networking Technologies for UAVs: A
Survey
In collaboration with Abhishek Sharma, Pankhuri Vanjani, Nikhil Paliwal,
Dushantha Nalin K. Jayakody and Hwang-Cheng Wang
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Survey Article
This survey aims at providing insights into the latest UAV communi-
cation technologies through investigation of suitable task modules;
antennas; resource handling platforms and network architectures.
Communication Modules for UAV Communication
Networking Technologies for UAV Communication System
IoT-Enabled UAV Communication System
Integrating UAVs into Cellular Networks
Artificial Intelligence and UAV communication
Navigation Strategies for UAVs
Techniques for Secure UAV Communication
Optimization Theory for UAV Communication System
Challenges, Open Issues, and Future Directions
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Proposed Cellular connected UAV Communication Network
Source: A. Sharma, P. Vanjan, N. Paliwal, C. M. W. Basnayaka, D. N. K.Jayakody, H.C.Wang and
P.Muthuchidambaranathan “Communication and Networking Technologies for UAVs: A Survey ” Jour-
nal of Network and Computer Applications (Accepted)
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Physical Layer Redesign For Unmanned Aerial Vehicle
(UAV) Communication in 5G
Objectives and Aims
To investigate the current state of UAV assisted wireless com-
munication technologies and the feasibility of integrating UAV
into the cellular network using these technologies.
To investigate how to minimized the “Age of Information” in a
cellular-connected UAV communication network.
To investigate how to guarantee the Quality of service (QoS)
and the network availability for Ultra-reliable and low-latency
communications (URLLC) with the UAV.
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Chanel Model for the UAV Communication
Illustration of the transmission signal from the UE to UAV
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Cumulative distribution function of distance between UAV and UE
is given by
Fd (x) =
x3 − R3
min
R3
max − R3
min
(1)
Probability distribution function of distance is equal to
fd (x) =
dFd (x)
dx
=
3x2
R3
max − R3
min
(2)
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Line of sight probability
According to The International Telecommunication Union (ITU)
the probability of line of sight channel exists between UE and UAV
is given by
P(LOS) =
bd
b=1
P(structure height < hLOS ) (3)
According to the mathematical steps given by the International
Telecommunication Union (ITU) Los probability is given by
P(LOS) =
bd
k=0
1 − exp
−
(k+1
2
)dtanθ
bd
2
2γ2
(4)
Here, γ is a scale parameter that describes the buildings’ heights
distribution according to Rayleigh probability density function and
bd is the number of buildings crossed.
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Continue..
Akram et al. closely approximated equation 4 to a simple modified
Sigmoid function (S-curve) of the following form:
P(LOS, θ) =
1
1 + ρ.exp(−ϕ(θ − ρ))
(5)
Where ρ and ϕ are called here the S-curve parameters that are
depends on α, β and γ constant.
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Large–Scale Fading
Let α be the large scale channel gain of the channel which can be
obtained from the following equation.
−10log(α) = 20log(d)+20log(
4πfc
C
)+ηNLOS +
ηLOS − ηNLOS
1 + ρ.exp(−ϕ(θ − ρ)
(6)
Where fc , C are the carrier frequency(Hz) ,the speed of the light
(m/s) respectively and η is normally distributed random variable,which
reflects the location variability of the UAV and shadowing due to
NLOS and LOS connection , i.e.,
η ∼ N(µ, σ2
(θ)) (7)
Relationship between standard deviation of the distribution and
elevation angle given by
σ(θ) = λexp(−νθ) (8)
Where ν,λ and µ are parameters depending on the types of com-
munication environments.
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Small–Scale Fading Statistics
We have used Nakagami-m distribution for model Small–Scale Fad-
ing .Then,the probability density function is given by
fg (z) =
mmzm−1exp(−mz)
(m − 1)!
(9)
Assume that the transmission power from UE to UAV is fixed as P
and the noise power at UAV is denoted as σ2. The instantaneous
signal-to-noise ratio (SNR) at the UAV is given by
˜γ(θ, d) =
αgP
σ2
(10)
where α and g are the large scale channel gain and the small scale
channel gain respectively.
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Average SNR
The average SNR is written as
γ =
Rmax
Rmin
90
θmin
˜γ(x, y)fd,θ(x, y)dydx (11)
where fd,θ(x, y) is the joint probability distribution function of d
and θ.Here,PDF of θ is given by
fθ(y) =
1
90 − θmin
(12)
Since d and θ are independent the joint PDF of d and θ are given
by
fd,θ(x, y) = fd (x)fθ(y) (13)
where fd (x) is given by (2).
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Age of Information
Unmanned aerial vehicles exchange position, velocity, and
other control information to enable collision avoidance
mechanisms
Command and control systems exchange mission critical
information in order to maintain situational awareness
In such applications, it is essential to keep the information
fresh as outdated information has diminished value
The concept of Age of Information ( AoI ) was introduced in
to quantify the freshness of the knowledge we have about the
status of a remote system.
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Age of Information and UAV communication
To measure the performance of data freshness at destination
(UAV or UE), we bring in a new metric, i.e., Age of
Information (AoI).
The transmission system model
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Continue..
Consider a system in which a source (UE) generates packet
that traverse a cellular connected UAV network to reach the
destination (UAV) as shown in figure.
g(t) be the generation time of the freshest received packet
decoded at time t.The Age of Information (AoI) defined as the
random process.
∆(t) = t − g(t) (14)
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Continue..
As plotted in Fig assume that at t = 0 start observing the
system, the queue is empty, and the AoI at the destination is
∆(0) = ∆0.
User equipment generates packets at time instants
g1 , g2 , ..., gn and these packets received at time D1 , D2 , ..., Dn
AoI increases linearly in time until it received, at which point
AoI takes the value of the packet delay.
∆ (t) = t − gi (15)
∆ (Di ) = Di − gi (16)
AoI Just before the packet i + 1 is delivered
∆ D−
i+1 = Yi+1 + Xi (17)
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Continue..
∆ (Di+1) = Yi+1 (18)
Consider time interval [0, Tn]
Tn =
n−1
i=0
(Xi − Yi + Yi+1) (19)
Tn
0
(∆(t).d(t)) =
n−1
i=0
Qi (20)
Area of trapezoid
Qi =
Di +Xi −Yi +Yi+1
Di
(∆(t).d(t)) (21)
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Finalized AoI Equation for a Any Wireless Network
Average(AoI) = limn→∝sup
E(
Tn
0 (∆(t).d(t)))
E(Tn)
(22)
E (AoI) =
E[X2]
2 + E[XY ]
E[X]
(23)
We assume that Y = S + W
1
2 E X2
E [X]
+ E [S] +
E [WX]
E [X]
(24)
In Here,E[X] and E[S] are i.i.d . E[WX] is the most difficult part in
this problem since W and X are dependent variables.
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Pollaczek–Khinchine formula
The Pollaczek–Khinchine formula states a relationship between the
queue length and service time distribution Laplace transforms for
an M/G/1 queue (where jobs arrive according to a Poisson process
at rate λ and have general service time distribution)
E [W ] =
E(S2)
2(E [X] − E [S])
(25)
Using Lindley equation
E [WX] =
E [X] (E [X] − E [S])
E(e−λS )
+
E S2 .E [X]
2(E [X] − E [S])
−E X2
(26)
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AOI of M/G/1 queue
For M/G/1 queue,E[X] = 1
λ and using equation (24) and (26)
E [AOI] = E [S] +
E S2
2(E [X] − E [S])
+
E [X] − E [S]
E [e−λS ]
(27)
In Here the service time of the communication channel depends on
number of re -transmission
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Service Time and Re-transmission
The total number of re-transmission T,needed for the reliable trans-
mission of a packet is geometrically distributed with transmission
success rate and its probability mass function is given by
PT (m) = (1 − ) m−1
; m = 1, 2.... (28)
Here,ε is the packet error probability
Total service time for packet given by
Stime = TMD (29)
where D and M are symbol duration and symbols per packet(block
length) respectively.
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Continue..
Since both of theses values are constant the service time is a geo-
metric random variable and it is given by
E(S) =
MD
1 − ε
(30)
E(S2
) = (MD)2 1 + ε
(1 − ε)2
(31)
E(e−λS
) =
(1 − ε)e−MDλ
1 − εe−MDλ
(32)
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AoI -Uncoded Communication Scheme
Using equations 30-32 and 27
Average AoI of the Uncoded Communication Scheme
=
MD
1 − ε
+
(MD)2λ(1 + ε)
(1 − ε) ∗ (1 − ε − MDλ)
+
(1 − (ε ∗ e−MDλ)) ∗ (1 − ε − MDλ)
(1 − ε)2 ∗ e−MDλ
(33)
Without any coding , the probability of a packet error where pack-
ets are formed with M transmitted symbols at a given instanta-
neous SNR is given by
ε = (1 − (1 − Ps(γ))M
) (34)
where Ps(γ) is symbol error rate
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AoI -Coded Communication Scheme
Ultra-reliable low-latency communications (URLLC) concerns the
transmission of data at a very small error probability without vio-
lating a given latency constraint.
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Future direction
Finite-blocklength information theory and AoI
We have investigated the minimum error probability achievable at
a given block length M and channel coding rate r The minimum
error probability of quasi static fading channel is equal to (ε)
ε = Q(
M
V (γ)
(log2(1 + γ) + O(
log2M
M
) − r)) (35)
Where Q(x) = 1√
2π
∞
x e−
t2
2 dt, V (γ) is the channel dispersion
that is function of SNR γ and is given by V (γ) = log2e ∗ (1 −
1
(1+γ)2 ) ,and r is coding rate. Using Yury Polyanskiy and V.Poor
approximation :non-asymptotic fundamental limits
ε ≈ Q(
M
V (γ)
(log2(1 + γ) +
log2M
2M
− r)) (36)
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Work Plan for Next 6 Months
Simulate proposed short packet transmission model and age of
information model (finite block lent regime)
Optimize the age of information of the network with other
parameters of the channel using an appropriate optimization
technique
Simulate the proposed 3-D channel model using simulation
software and study the effect of other parameters such as
elevation angle and amplitude of UAV on the behavior of the
communication channel
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Publications
A. Sharma, P. Vanjan, N. Paliwal, C. M. W. Basnayaka, D.
N. K.Jayakody, H.C.Wang and P.Muthuchidambaranathan
“Communication and Networking Technologies for UAVs: A
Survey ” Journal of Network and Computer Applications
(Accepted)
C. M. W. Basnayaka, K.O. Lakamal and D. N. K.Jayakody,
2019. “The Era of the 5G Drone is Ahead, Are We Ready?”
Vidurava, 36(4), pp.19-21.
