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Performance Analysis of Vehicular Optical Camera Communications: Roadmap to uRLLC

01 Dec 2019-pp 1-6
TL;DR: This paper analyzes the performance of vehicular optical camera communication towards ultra-reliable and low latency communications (uRLLC) and finds that by satisfying a given target BER, higher spectral efficiency and lower latency can be achieved through adjusting the AoI towards the smaller degrees and switching into the suitable modulation order.
Abstract: In this paper, we analyze the performance of vehicular optical camera communication (OCC) towards ultra-reliable and low latency communications (uRLLC). The employed vehicular OCC model uses light-emitting diodes (LED) as transmitter and camera as receiver. In particular, we investigate the performance of the proposed system in terms of bit error rate (BER), spectral efficiency, and transmission latency at different inter-vehicular distances and angle of incidences (AoI). Further, we investigate the use of adaptive modulation to improve the spectral efficiency. From our analysis, we note that by satisfying a given target BER, higher spectral efficiency and lower latency can be achieved through adjusting the AoI towards the smaller degrees and switching into the suitable modulation order. Finally, we verify the results through simulations, which show that OCC can ensure ultra-low latency as well as satisfy the reliability requirements in automotive vehicles.

Summary (2 min read)

Introduction

  • Such as lane changing alert [2] or automotive braking system [3], have already been deployed, mission-critical services, e.g., collision avoidance, automotive driving, are still posing significant challenges in vehicular networks.
  • Different from RF systems, VLC uses LEDs as transmitters and photodiodes (PD) or image sensors as receivers.
  • Table I summarizes the main characteristics of OCC, PD and RF communication systems, which shows that OCC suffers from almost negligible interference and consumes less power than RF.
  • If the reliability requirement for a certain modulation scheme is not met, the system can reduce the AoI at the receiver to ensure uRLLC.

A. System Overview

  • Let us consider two vehicles that communicate with each other as shown in Fig. 1; one being the transmitter vehicle (TV) denoted by Ti and the other being the receiver vehicle (RV) denoted by Rj .
  • In their system, the LED lights located at the back side of TV is the transmitter, and the high-speed camera (also known as image sensor and has a frame rate of 1000 fps) located at the front side of RV, is the receiver.
  • The communicated information between the vehicles is vehicle’s internal information, e.g., speed, next action, position, and/or other safety and action-related information from the transmitter.
  • In their system, the camera performs two simultaneous functions.
  • Intensity modulation with direct detection (IM/DD) is adopted by the transmitter in which the desired waveform is modulated onto the instantaneous optical power of the LED lights.

B. Optical Channel Model

  • In their analysis, the authors assume an un-interrupted LOS link between the transmitter LED lights and the camera of the receiver.
  • Generally, VLC channel has two types of light propagation, namely, LOS component resulting from direct light propagation to the receiver and diffuse components resulting from the light reflections from other reflection surfaces or vehicles.
  • The inter-relation between the distance calculation parameters is illustrated in Fig. 2(a).
  • Lower AoI of the camera lens means the strength of light beam will be stronger on the image sensor, which in turns, will increase the channel power gain.
  • Finally, the received optical power Pr(θ, t) can be derived from the optical transmitted power Pt from the LEDs as Pr(θ, t) = Pt Hij(θ, t). (8).

III. ANALYSIS OF ADAPTIVE MODULATION AND AOI ADJUSTMENT

  • Motivated by the trade-off between the order of modulation and the achieved BER, the authors consider adaptive modulation that permits us to adopt modulation order by satisfying minimum BER for the system.
  • In the meantime, RV decreases the AoI of the camera lens to focus on the LED transmitter and decodes the transmitted signal within the shortest possible time.
  • For the performance analysis, the authors estimate the achievable BER by each modulation scheme using the formulas found in [17].
  • Since, the goal of the system is to avoid critical conditions, i.e., avoid collision between vehicles, a minimum distance has to be maintained between the vehicles.

IV. SIMULATION RESULTS AND DISCUSSION

  • BER vs AoI for different modulation schemes.
  • The interplay among the various parameters of their system.
  • The results are presented in Fig. 3 and Fig. 4, respectively, which show the achieved BER for the different modulation schemes.
  • So, at shorter distance and narrower AoI, the modulation order will be higher, i.e., higher spectral efficiency, due to higher SNR at the receiver.
  • The latency increases for wider AoI as the light beam strength reduces with the increase of AoI of lights on the image sensor.

V. CONCLUSION

  • The performance of adaptive modulation has been analyzed for automotive vehicular uRLLC considering OCC.
  • The latency is modelled based on the capacity of the vehicular OCC while considering the transmission latency only.
  • Further, the BER performance is studied for various sets of the AoI and inter-vehicular distance.
  • In their system, the spectral efficiency of vehicular OCC is adjusted adaptively using adaptive modulation which ensures reliability by maintaining the BER to a pre-determined target value.
  • Interestingly, the proposed model provides about 7 ms latency while satisfying the reliability requirement of 10−4 or 10−5 when the AoI is varied between 0o to 90o.

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Performance Analysis of Vehicular Optical Camera
Communications: Roadmap to uRLLC
Amirul Islam, Leila Musavian, and Nikolaos Thomos
CSEE, University of Essex, UK.
Email: {amirul.islam, leila.musavian, nthomos}@essex.ac.uk
Abstract—In this paper, we analyze the performance of ve-
hicular optical camera communication (OCC) towards ultra-
reliable and low latency communications (uRLLC). The em-
ployed vehicular OCC model uses light-emitting diodes (LED) as
transmitter and camera as receiver. In particular, we investigate
the performance of the proposed system in terms of bit error rate
(BER), spectral efficiency, and transmission latency at different
inter-vehicular distances and angle of incidences (AoI). Further,
we investigate the use of adaptive modulation to improve the
spectral efficiency. From our analysis, we note that by satisfying
a given target BER, higher spectral efficiency and lower latency
can be achieved through adjusting the AoI towards the smaller
degrees and switching into the suitable modulation order. Finally,
we verify the results through simulations, which show that OCC
can ensure ultra-low latency as well as satisfy the reliability
requirements in automotive vehicles.
I. INTRODUCTION
Automotive vehicles (AVs) are emerging as the revolution
in future smart cities and are considered as one of the
main transformative technologies in intelligent transportation
systems (ITS). We are witnessing an unparallel increase of
the number of vehicles and vehicle-assisting infrastructures
resulting in more traffic congestions, road causalities, and
overall less traffic safety. Communication between AVs can
help improving the traffic safety and enhancing the overall
driving experience by facilitating new service features, such
as collision avoidance and autonomous driving [1]. Although
several AVs services, such as lane changing alert [2] or
automotive braking system [3], have already been deployed,
mission-critical services, e.g., collision avoidance, automotive
driving, are still posing significant challenges in vehicular
networks. The efficiency of ITSs depends on the availability
of reliable communication links within the shortest possible
time that is characterized by uRLLC. Therefore, future AVs
will require uRLLC to exchange their internal or surrounding
information, e.g., speed, next action, and position, with each
other effectively and operate the AVs reliably. However,
achieving uRLLC is one of the major challenges in future
vehicular networks [4], [5].
For enabling uRLLC in ITSs, existing methods, such as
[6], [7] reflect on delay minimization, vehicle clustering, and
excess queue length evaluation. Specifically, in [6], the vehic-
ular network transmission power is minimized by grouping
vehicles into clusters modelling reliability as queuing delay
violation probability. A joint resource allocation and power
control algorithm is proposed to maximize the vehicle-to-
vehicle (V2V) sum rate with latency and reliability constraints
in [7]. Edge computing is also considered as an attractive
solution to minimize latency that processes the requested tasks
locally, without relying on remote servers [8], [9]. The above
systems enable uRLLC, either using radio frequency (RF)
communication or cellular systems with central base stations
(BS), servers or edge servers. However, BSs can become
overloaded with the frequent requested AV tasks because they
have limited computational resources and follow centralized
resource management. Moreover, RF channels are prone to
channel fading, noise, and interference, which render them
inappropriate for uRLLC.
On the other hand, recently, visible light communications
(VLC) have attracted tremendous attention as a potential
alternative to RF communication [10], [11]. Different from
RF systems, VLC uses LEDs as transmitters and photodiodes
(PD) or image sensors as receivers. VLC systems using PD as
the receiver are called light fidelity (LiFi) and those employ
image sensors are called OCC. VLC offers several signif-
icant advantages over RF-based systems including license-
free access spectrum, longer lifespans, less implementation
cost, and enhanced security having the line of sight (LOS)
properties [12]. More importantly, VLC systems do not pose
any potential harm to human bodies or eyes and they do not
create electromagnetic interference (EMI).
In traditional VLC, the receiver often consists of a non-
imaging device, i.e., PD, and its performance is limited by
the trade-off between transmission range and signal reception.
Different from PD-based systems, OCC can spatially separate
and process different sources independently on its image
plane, which enables the receiver to discard noise sources,
e.g., Sun, streetlights, other light sources, and focuses mainly
on the pixels to which the LEDs strikes [13]. This ability
ensures interference-free, reliable, and secure communication
even at the outdoor environment. Table I summarizes the
main characteristics of OCC, PD and RF communication
systems, which shows that OCC suffers from almost negligible
interference and consumes less power than RF. Besides, OCC
supports almost 20 times longer distance than the PD-based
systems. Although having low data rate, OCC can be a better
alternative to the congested and saturated RF system due
to its negligible noise and interference characteristics. The
revolutionary advancements in OCC have made the technol-
ogy as a promising mechanism for AVs communication [13],
[14]. However, OCC can face challenges due to its LOS
requirements for communication, i.e., communication links
can be obstructed by objects or bad weather conditions.

TABLE I
COMPARISON BETWEEN OCC, PD, AND RF
Parameter
OWC
RF
OCC PD
Bandwidth of the carrier Unlimited (400 - 700) nm Unlimited (400 - 700) nm 300 GHz (saturated and regulated)
EMI and hazard
No No Yes
Transmitter LED LED or Laser diode (LD) Antenna
Receiver Camera PD Antenna
Power consumption Relatively low
Relatively higher than OCC
Medium
Interference level Negligible Low Very high
Communication distance 200 m 10 m More than 100 km using Microwave
Environmental effect No Indoor: No, Outdoor: Yes Yes
Noise No Sun and ambient light sources All electrical and electronic appliances
Security High High Low
Data rate 54 Mbps
10 Gbps using LED and 100 Gbps
using LD
6 Gbps (IEEE 802.11ad at 60GHz)
Main purpose
Illumination, communication, and
localization
Illumination and communication
Communication and positioning
Limitation Low data rate
Short distance, no mobility guaranty,
not suitable for outdoor
Interference
To the best of our knowledge, this is the first OCC-based
vehicular system that focuses on uRLLC aspects. In this paper,
we introduce a novel low latency V2V communications frame-
work that ensures ultra-reliability using OCC. The proposed
system is fully decentralized and each vehicle processes the
communicated information individually. In terms of latency,
we only consider transmission latency, as a small amount
of data is processed in our system that is related to the
action or safety information, and hence, the computational
latency is negligible. To improve the efficiency of OCC-based
communication, we use an adaptive modulation scheme. By
increasing the modulation order, higher spectral efficiency and
lower latency can be achieved. In our evaluation, we consider
satisfying the target BER as an indication of reliability in our
system. If the reliability requirement for a certain modulation
scheme is not met, the system can reduce the AoI at the re-
ceiver to ensure uRLLC. Finally, we analyze the performance
of the proposed system in terms of BER, spectral efficiency,
transmission latency for various inter-vehicular distances and
AoIs of LED lights at the receiver. The major contributions
of this paper can be summarized as follows:
This is the first study that formulates the communication
link performance with adaptive modulation scheme to
examine whether OCC is suitable for employing uRLLC
in automotive vehicles.
We provide a mathematical framework to model the OCC
channel in order to find out the probability of errors,
achievable spectral efficiency, and transmission latency
as a function of inter-vehicular distances and AoIs while
considering the adaptive modulation.
We investigate how to achieve uRLLC by introducing a
mechanism of varying the AoI at the receiver vehicle
when the transmitter changes the modulation scheme
depending on the size of the transmitting data.
II. SYSTEM MODEL
A. System Overview
Let us consider two vehicles that communicate with each
other as shown in Fig. 1; one being the transmitter vehicle
(TV) denoted by T
i
and the other being the receiver vehicle
(RV) denoted by R
j
. In our system, the LED lights located
at the back side of TV is the transmitter, and the high-speed
camera (also known as image sensor and has a frame rate
of 1000 fps) located at the front side of RV, is the receiver.
We denote the distance between T
i
and R
j
by d
ij
. The
communicated information between the vehicles is vehicle’s
internal information, e.g., speed, next action, position, and/or
other safety and action-related information from the transmit-
ter. In our system, the camera performs two simultaneous
functions. Firstly, it measures d
ij
. Secondly, the camera in
the RV decodes the signal information received from the
LED transmitters. Intensity modulation with direct detection
(IM/DD) is adopted by the transmitter in which the desired
waveform is modulated onto the instantaneous optical power
of the LED lights.
B. Optical Channel Model
In our analysis, we assume an un-interrupted LOS link
between the transmitter LED lights and the camera of the
receiver. This ensures obstruction free and continuous com-
munication. The light signal transmitted from the LED arrays
is received by an image sensor in the RV which lies within its
field of view (FoV). Then, the radiated signal passes through
an optical filter and a lens to ensure that maximum light
falls within the FoV of the receiver. Depending on the link
conditions, the VLC channel is either a flat fading channel
or a diffuse channel. Generally, VLC channel has two types
of light propagation, namely, LOS component resulting from
direct light propagation to the receiver and diffuse components
resulting from the light reflections from other reflection sur-
faces or vehicles. Usually, the energy of diffuse components
are much lower than the energy of the LOS component and,
therefore, the latter is neglected in this paper. As a result, the
optical wireless LOS channel DC gain is modelled as [15]:
H
ij
(θ, t) =
(
A
eff
(θ)
d
2
ij
(t)
R(φ), 0 θ θ
l
0, θ > θ
l
(1)

Fig. 1. Proposed system model of vehicular optical camera communication.
where A
eff
(θ) is the effective signal collection area of the
image sensor, θ is the AoI, φ is the angle of irradiance with
respect to the emitter, R(φ) is the transmitter radiant intensity,
θ
l
denotes the FoV of the image sensor lens, and finally, t is
time frame index. The distance d
ij
(t) can be expressed as [14]
d
ij
(t) =
f
a
.
D
p(t)
, (2)
where D is the distance between the left and right LED array
units, f is the lens focal length, p(t) is the distance in terms of
number of pixels between the left and right LED array units
on the captured image, and a is the image pixel size. The
inter-relation between the distance calculation parameters is
illustrated in Fig. 2(a).
Regarding the above parameters: D is sent from the TV
to RV, and f and a are known values, such as 15 mm and
7.5 µm, in this system. The value of p(t) can be obtained via
simple image processing techniques or calculating the pixel
values using data pointer.
However, the DC gain can accurately be computed by
considering the LOS propagation. For this, we follow the link
geometry as shown in Fig. 2(b). As LED light usually has the
Lambertian radiation pattern, the light emission from the LED
transmitters can be modeled using a generalized Lambertian
radiant intensity [14], [16]
R(φ) =
(m + 1)
2π
cos
m
(φ), (3)
where m is the order of Lambertian emission which is related
to the LED semiangle at half luminance
1/2
), given by [15]:
m =
ln(2)
ln(cos
1/2
))
. (4)
A
eff
(θ) in (1) of the projected image on the image sensor
can be expressed as [15]
A
eff
(θ) =
A T
s
(θ) g cos(θ), 0 θ θ
l
0, θ > θ
l
(5)
where A is the area of the entrance pupil of the camera lens,
T
s
(θ) is the signal transmittance of the optical filter, and g is
the gain of the lens, which is given by
g =
n
2
sin
2
(θ
l
)
, (6)
where n corresponds to the internal refractive index of an ideal
lens. Taking into account (3) and (5), (1) can be written as
follows:
H
ij
(θ, t) =
(
(m+1)A
2πd
2
ij
(t)
cos
m
(φ)gT
s
(θ)cos(θ), 0 θ θ
l
0. θ > θ
l
(7)
From (7), we observe that if A and g are fixed for an image
sensor, the channel power gain H
ij
(θ, t) can be increased by
either (a) decreasing the distance, d
ij
(t) and/or (b) increasing
the collection area, i.e., by decreasing the AoI of the camera
lens. Lower AoI of the camera lens means the strength of light
beam will be stronger on the image sensor, which in turns,
will increase the channel power gain. Alternatively, higher AoI
reduces the H
ij
(θ, t) as the LED light beam will spread out
at the wide angle of the camera lens. So, maintaining nar-
rower AoI at the receiver will provide improved performance
because of having higher gain.
Finally, the received optical power P
r
(θ, t) can be derived
from the optical transmitted power P
t
from the LEDs as
P
r
(θ, t) = P
t
H
ij
(θ, t). (8)
III. ANALYSIS OF ADAPTIVE MODULATION AND AOI
ADJUSTMENT
Motivated by the trade-off between the order of modulation
and the achieved BER, we consider adaptive modulation that
permits us to adopt modulation order by satisfying minimum
BER for the system. Moreover, adaptive modulation offers
improved spectral efficiency. It is worth to note that the
adjustment of modulation depends on the road scenarios. At
normal conditions, when there is nothing to communicate, RV
maintains the wider AoI to understand the whole scenario of
the road. If the TV wants to transmit any critical information,
it chooses a higher modulation based on the size of the
transmitted data. On the receiver side, if the RV notices any
sudden change in the TV transmitted signal and fails to decode
it using the current modulation scheme, the RV switches to
another modulation from the chosen limited modulation set.
In the meantime, RV decreases the AoI of the camera lens
to focus on the LED transmitter and decodes the transmitted
signal within the shortest possible time.
In order to analyze the system performance in terms of
BER, spectral efficiency, and latency, we first need to formu-
late the signal-to-noise ratio (SNR) of the optical link. We
consider SNR as a measure of communication link quality
of the signal transmission. Therefore, according to [16], the
received SNR γ(θ, t) of visible light link can be expressed by
γ(θ, t) =
P
2
r
(θ, t)
σ
2
total
(θ, t)
=
P
2
t
H
2
ij
(θ, t)
σ
2
total
(θ, t)
, (9)
where σ
2
total
(θ, t) denotes total noise power and can be ex-
pressed as
σ
2
total
(θ, t) = σ
2
shot
(θ, t) + σ
2
thermal
. (10)

Fig. 2. (a) Inter-vehicular distance measurement [14] and (b) LOS channel model of OCC.
While shot-noise variance σ
2
shot
(θ, t) is given by
σ
2
shot
(θ, t) = 2 q B(sP
r
(θ, t) + I
bg
I
2
P
n
), (11)
where q is the electronic charge, B is the equivalent noise
bandwidth, I
bg
is the background current, P
n
is the noise
power (I
amp
/R
b
), I
amp
is the amplifier current, R
b
is the data
rate, and I
2
is the noise bandwidth factor for a rectangular
transmitter pulse shape.
The thermal noise variance in (9) is given by
σ
2
thermal
=
8πkT
G
I
2
B
2
C
f
A +
16π
2
kTΓ
g
m
I
3
B
3
C
2
f
A
2
, (12)
where k is Boltzmanns constant, T is absolute temperature,
G is the open-loop voltage gain, C
f
is the fixed capacitance
of the image sensor per unit area, g
m
is the FET trans-
conductance, Γ is the FET channel noise factor, and I
3
is
the noise bandwidth factor.
From (7) and (9), we can see that the received SNR depends
on both AoI and the distance between the transmitter and
receiver. Therefore, we can control the SNR by modifying
the AoI and d
ij
(θ, t).
In the proposed system, we use adaptive modulation scheme
with the combination of binary phase shift keying (BPSK), M-
ary quadrature amplitude modulation (M-QAM), and M-ary
phase shift keying (M-PSK) as example, but other modulation
schemes can be used as well. For the performance analysis,
we estimate the achievable BER by each modulation scheme
using the formulas found in [17].
We should note that channel capacity (measured in bits/sec)
of a camera based communication system depends on the
employed modulation scheme as has been shown in [12]
where capacity is expressed as
C(θ, t) = W
fps
· W
s
(t) · log
2
(M(θ, t)), (13)
where W
fps
is the camera-frame rate in fps, W
s
(t) is the
spatial-bandwidth (14), which can also be denoted by the
number of information carrying pixels per camera image
frame, and M(θ, t) is constellation size. log
2
(M(θ, t)) is the
spectral efficiency which depends on the modulation scheme,
e,g, 1 for BPSK, 2 for 4-QAM. The spatial bandwidth W
s
(t)
can be defined by
W
s
(t) = N
LEDs
· N
row
(t), (14)
where N
LEDs
is the number of LEDs at each row of the
transmitter and N
row
represents the captured number of row
pixel lines in each frame. Considering the operation of the
Fig. 3. BER vs distance for different modulation schemes.
rolling shutter camera, the actual number of samples (pixel
rows) can be expressed as follows:
N
row
(t) = w ·
L
size
2 tan
θ
l
2
· d
ij
(t)
, (15)
where w is the image width (in case the rolling axis is along
the width of the image sensor), and L
size
is the size of LED
lights in cm
2
.
Hence, the overall end-to-end latency can be found as
τ(θ, t) =
L
C(θ, t)
, (16)
where τ(θ, t) represents transmission latency which includes
the downlink latency only and L is the packet size in bits.
Please recall that, we neglect the computational latency as in
our system small amount of data should be processed.
Since, the goal of the system is to avoid critical conditions,
i.e., avoid collision between vehicles, a minimum distance
has to be maintained between the vehicles. However, with
the increase of the distance between the vehicles deteriorates
the quality of the communication. Specifically, increasing the
distance beyond a threshold would lead uRLLC conditions to
be violated. So, in order to maintain uRLLC, we can vary the
modulation order at the transmitter depending on the size of
the transmitting data and the AoIs at the RV to satisfy the
target BER.
IV. SIMULATION RESULTS AND DISCUSSION
In this section, we evaluate the proposed system for dif-
ferent performance metrics to get a better understanding of

TABLE II
SIMULATION PARAMETERS
Parameter, Notation Value Parameter, Notation Value
Angle of irradiance w.r.t. the emitter, φ 70
o
Boltzmanns constant, k 1.3807 × 10
23
Semi-angle at half luminance of the LED, Φ
1/2
60
o
Absolute temperature, T 298 K
Inter-vehicular distance, d
ij
(0 150) m Open loop voltage gain, G 10
AoI w.r.t. the receiver axis, θ 0
o
to 90
o
Fixed capacitance, C
f
112 × 10
8
FOV of the camera lens, θ
l
90
o
FET channel noise factor, Γ 1.5
Image sensor physical area, A 10 cm
2
FET trans-conductance, g
m
30 ms
Transmission efficiency of the optical filter, T
s
1 Noise bandwidth factor, I
3
0.0868
Refractive index of concentrator/lens, n 1.5 Constellation size, M
8, 16, 32 for M-PSK and
4, 16, 64 for M-QAM
Concentrator/lens gain, g 3 Camera-frame rate, W
fps
1000 fps
Optical transmitting power, P
t
1.2 Watts Number of LEDs in the transmitter, N
LEDs
300 (30 × 10)
Electron charge, q 1.6 × 10
19
C Focal length of the camera lens, f 15 mm
Equivalent noise/electronic bandwidth, B 2MHz Image pixel size, a 7.5 µm
Distance between the left and right LED array, D 50 cm Background current, I
bg
5100 µA
Noise bandwidth factor for a rectangular pulse, I
2
0.562 Size of the LED, L
size
15.5 × 5.5 cm
2
Amplifier current, I
amp
5 pA Resolution of image, w 512 × 512 pixels
Data rate of system, R
b
500 bps
Fig. 4. BER vs AoI for different modulation schemes.
the interplay among the various parameters of our system.
We consider adaptive modulation scheme with options BPSK,
M-PSK, and M-QAM modulation with the constellation size,
M = {4, 8, 16, 32, 64}, i.e., BPSK, 8-PSK, 16-PSK, 32-
PSK, 4-QAM, 16-QAM, 64-QAM. Target BER is set to 10
4
and 10
5
for performance comparison to be compliant with
uRLLC requirements. All the results are generated considering
the parameters in Table II.
We start by comparing the BER performance at different
inter-vehicular distances and for different AoIs. The results
are presented in Fig. 3 and Fig. 4, respectively, which show
the achieved BER for the different modulation schemes. In this
evaluation, we do not vary the distance and AoI at the same
time. In Fig. 3, we change the distance from 0 m to 150 m by
keeping the AoI at 60
o
, whereas in Fig. 4, we vary the AoI
between 0
o
to 90
o
while keeping distance fixed at 50 m. We
note that the same BER can be achieved at different distances
and AoIs by using different modulation schemes. Fig. 3 shows
that 32-PSK satisfies target BER (10
4
) up to 40 m and for
64-QAM, it is satisfied at 52 m. At smaller distance, higher
order modulation can be employed because of the higher SNR
Fig. 5. Comparison of spectral efficiency vs distance at target BER of 10
4
and 10
5
.
level. Similarly in Fig. 4, target BER (10
4
) is satisfied at 38
o
and 62
o
for 32-PSK and 64-QAM. Because at the narrower
AoI, the strength of light beam on the image sensor is strong
which increases channel power gain. So, at shorter distance
and narrower AoI, the modulation order will be higher, i.e.,
higher spectral efficiency, due to higher SNR at the receiver.
The spectral efficiency improvements offered by the pro-
posed system are illustrated in Fig. 5 and Fig. 6 for two
different target BERs (10
4
and 10
5
). In this comparison,
we explore the BER performance at different modulations
by varying inter-vehicular distance (Fig. 5) and AoI (Fig. 6)
and then choosing the highest spectral efficiency from all the
available schemes that satisfy the target BER requirement.
From Fig. 5, we see that we can support spectral efficiency of
6 bits/s/Hz when the inter-vehicle distance is up to 48 m (for
BER = 10
5
) and 52 m (for BER = 10
4
). From Fig. 6, we can
notice that for 64 QAM, the spectral efficiency is 6 bits/s/Hz
when the AoI is 57
o
(for BER = 10
5
) and 62
o
(for BER =
10
4
). Similarly, our scheme achieves a spectral efficiency of
4 bits/s/Hz from 48 m to 62 m and 57
o
to 71
o
, 52 m to 66
m and 62
o
to 73
o
for BER of 10
5
, 10
4
, respectively. We

Citations
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Posted Content
TL;DR: This paper introduces a rate optimization approach in vehicular OCC through optimal power allocation while respecting uRLLC requirements and presents an algorithm based on Lagrangian relaxation and Bisection method to solve the optimization problem.
Abstract: Optical camera communication (OCC) has emerged as a key enabling technology for the seamless operation of future autonomous vehicles. By leveraging the supreme performance of OCC, we can meet the stringent requirements of ultra-reliable and low-latency communication (uRLLC) in vehicular OCC. In this paper, we introduce a rate optimization approach in vehicular OCC through optimal power allocation while respecting uRLLC requirements. We first formulate a discrete-rate optimization problem as a mixed-integer programming (MIP) subject to average transmit power and uRLLC constraints for a given set of modulation schemes. To reduce the complexity in solving the MIP problem, we convert the discrete-rate problem into a continuous-rate optimization scheme. Then, we present an algorithm based on Lagrangian relaxation and Bisection method to solve the optimization problem. Considering the proposed algorithm, we drive the rate optimization and power allocation scheme for both discrete-rate and continuous-rate optimization schemes while satisfying uRLLC constraints. We first analyze the performance of the proposed system model through simulations. We then investigate the impact of proposed power allocation and rate optimization schemes on average rate and latency for different target bit error rates. The results show that increasing the transmit power allocation improves the average rate and latency performance.

1 citations


Cites background from "Performance Analysis of Vehicular O..."

  • ...In our earlier results published in [16], we analyzed the performance of vehicular OCC with fixed power allocation to justify whether OCC will be suitable for satisfying uRLLC requirements in AV communications....

    [...]

  • ...Readers interested in a detailed comparison among OCC, PD, and RF-based systems are referred to [16]....

    [...]

Proceedings ArticleDOI
05 May 2022
TL;DR: This paper introduces a spectral efficiency optimization approach in vehicular OCC by optimally adapting the modulation order and the relative speed while respecting bit error rate and latency constraints.
Abstract: Optical camera communications (OCC) has emerged as a key enabling technology for the seamless operation of future autonomous vehicles. In this paper, we introduce a spectral efficiency optimization approach in vehicular OCC. Specifically, we aim at optimally adapting the modulation order and the relative speed while respecting bit error rate and latency constraints. As the optimization problem is NP-hard problem, we model the optimization problem as a Markov decision process (MDP) to enable the use of solutions that can be applied online. We then relaxed the constrained problem by employing Lagrange relaxation approach before solving it by multi-agent deep reinforcement learning (DRL). We verify the performance of our proposed scheme through extensive simulations and compare it with various variants of our approach and a random method. The evaluation shows that our system achieves significantly higher sum spectral efficiency compared to schemes under comparison.

1 citations

Posted Content
20 Nov 2019
TL;DR: This paper introduces a novel approach of capacity maximization in vehicular OCC through the optimization of capacity, power allocation, and adaptive modulation schemes while guaranteeing reliability and latency requirements.
Abstract: Optical camera communication (OCC) has emerged as a key enabling technology for the seamless operation of future autonomous vehicles. By leveraging the supreme performance, OCC has become a promising solution to meet the stringent requirements of vehicular communication to support ultra-reliable and low-latency communication (uRLLC). In this paper, we introduce a novel approach of capacity maximization in vehicular OCC through the optimization of capacity, power allocation, and adaptive modulation schemes while guaranteeing reliability and latency requirements. First, we formulate a vehicular OCC model to analyze the performance in terms of bit-error-rate (BER), achievable spectral efficiency, and observed latency. We thus characterize reliability over satisfying a target BER, while latency is determined by considering transmission latency. Then, a capacity maximization problem is formulated subject to transmit power and uRLLC constraints. Finally, utilizing the Lagrange formulation and water-filling algorithm, an optimization scheme is proposed to find the adaptive solution. To demonstrate the robustness of the proposed optimization scheme, we translate the continuous problem into a discrete problem. We justify our proposed model and optimization formulation through numerous simulations by comparing capacity, latency, and transmit power. Simulation results show virtually no loss of performance through discretization of the problem while ensuring uRLLC requirements.

1 citations


Cites methods from "Performance Analysis of Vehicular O..."

  • ...In our previous paper [22], we analyzed the performance of vehicular OCC to justify whether OCC will be suitable for satisfying uRLLC in AVs....

    [...]

Proceedings ArticleDOI
01 Jun 2022
TL;DR: In this paper , a spectral efficiency optimization approach for vehicular optical camera communications (OCC) is proposed to optimally adapt the modulation order and the relative speed while respecting bit error rate and latency constraints.
Abstract: Optical camera communications (OCC) has emerged as a key enabling technology for the seamless operation of future autonomous vehicles. In this paper, we introduce a spectral efficiency optimization approach in vehicular OCC. Specifically, we aim at optimally adapting the modulation order and the relative speed while respecting bit error rate and latency constraints. As the optimization problem is NP-hard problem, we model the optimization problem as a Markov decision process (MDP) to enable the use of solutions that can be applied online. We then relaxed the constrained problem by employing Lagrange relaxation approach before solving it by multi-agent deep reinforcement learning (DRL). We verify the performance of our proposed scheme through extensive simulations and compare it with various variants of our approach and a random method. The evaluation shows that our system achieves significantly higher sum spectral efficiency compared to schemes under comparison.

1 citations

Journal ArticleDOI
TL;DR: This paper proposes a Hybrid Access Scheme (HAS) aiming to convert a future robot’s backend communication system by a finite number of sensors instead of using a lot of wires and utilizes the packet diversity principle to forward multiple copies of the same packet over the massive number of subcarrier channels.
Abstract: This paper proposes a Hybrid Access Scheme (HAS) aiming to convert a future robot’s backend communication system by a finite number of sensors instead of using a lot of wires. To replace this communication, the HAS needs to assure higher reliability within stringent low latency packet transmission. In this paper, the HAS utilizes the packet diversity principle and forward multiple copies of the same packet over the massive number of subcarrier channels. The HAS assigns the random accessing to select a subcarrier channel for general packet transmitting sensors. The audio and video sensors transmit packets over the dedicated channels to avoid collisions. The HAS system allows transmitting audio, video and general sensors simultaneously. The minimum number of subcarriers to satisfy theURLLC reliability requirement of 99.999% is evaluated for different packet duplications over different arrival condition.The HAS system’s reliability and collision probability are evaluated in MATLAB simulator for different packet duplication over different arrival condition. Moreover, the signal propagation expressions are captured using ANSYS HFSS software for rectangular and circular transmission medium over the 900 MHz, 2.4 GHz, 24 GHz, and 55 GHz frequency bands for differentstructural configurations.

1 citations

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TL;DR: The use of infrared radiation as a medium for high-speed short-range wireless digital communication, and several modification formats, including on-off keying (OOK), pulse-position modulation (PPM), and subcarrier modulation, are discussed.
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  • ...As a result, the optical wireless LOS channel DC gain is modelled as [15]:...

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Frequently Asked Questions (1)
Q1. What are the contributions in "Performance analysis of vehicular optical camera communications: roadmap to urllc" ?

In this paper, the authors analyze the performance of vehicular optical camera communication ( OCC ) towards ultrareliable and low latency communications ( uRLLC ). In particular, the authors investigate the performance of the proposed system in terms of bit error rate ( BER ), spectral efficiency, and transmission latency at different inter-vehicular distances and angle of incidences ( AoI ). Further, the authors investigate the use of adaptive modulation to improve the spectral efficiency. Finally, the authors verify the results through simulations, which show that OCC can ensure ultra-low latency as well as satisfy the reliability requirements in automotive vehicles. From their analysis, the authors note that by satisfying a given target BER, higher spectral efficiency and lower latency can be achieved through adjusting the AoI towards the smaller degrees and switching into the suitable modulation order.