filter bank multi-carrier modulation technique for vehicle ... · filter bank multi-carrier...

Filter Bank Multi-Carrier Modulation Technique for

Vehicle-to-Vehicle Communication

Saif H. Alrubaee, Mahamod Ismail, Mohammed A. Altahrawi, and Bara B. Burhan Centre of Advanced Electronic and Communication Engineering (PAKET), Universiti Kebangsaan Malaysia, 43600

UKM Bangi Selangor, Malaysia

Email: [email protected]; [email protected]; [email protected]; [email protected]

Abstract—Modern wireless communication like 5G systems are

expected to serve a wider range of scenarios than current mobile

communications systems. One of the major network

applications related to 5G is Vehicle-to-Vehicle (V2V)

communication that improves vehicle road safety, enhances

traffic and travel efficiency, and provides convenience and

comfort for passengers and drivers. However, supporting high

mobility is a challenge on the air interface. Accordingly,

multicarrer modulation as a multiple access is used to enhance

the connection between vehicles and to overcome this challenge.

In this paper, two multicarrier modulations are simulated. The

first one is the Orthogonal Frequency Division Multiplexing

(OFDM) while the second one is the Filter Bank Multi-Carrier

with Offset Quadrature Amplitude Modulation (FBMC/OQAM)

which is called FBMC. Simulation results show that all

waveforms have comparable BER performance. The throughput

of the FBMC is greater than the OFDM and the spectral

efficiency is increased according to the use of the OQAM

modulation. The FBMC throughput reaches 5 Mbps while the

OFDM reaches 4 Mbps; these results are due to the higher

usable bandwidth and because of using filters in FBMC which

reduces the effect of Cyclic Prefix (CP) on the signal especially

when CP is large in OFDM. Index Terms— Multicarrier, V2V, 5G, OFDM, FBMC

I. INTRODUCTION

Governments are the only entities that are responsible

to give the license of using a spectrum band for such an

application or network [1]. It means that enhancing the

spectrum usage leads to pay more to use extra bandwidth

which in general not a preferable choice to do because of

less available bands to use [2]. Because of this, many

research aims to study a lot of techniques used to enhance

the spectrum usage in order to enhance the data rate of

the transmission for various high speed networks like

Wireless Local Area Network (WLAN) and Long-Term

Evolution (LTE) [3]. One of these techniques used is the

cooperative communication systems between two or

more networks to share their available spectrum between

them [4]. For example, a cognitive radio network permits

such access by enabling nodes to identify and use

spectrum that may be dynamically shared among multiple

nodes [5].

One of the cooperative communication systems is

Vehicle-to-Vehicle (V2V) as part of Intelligent

Manuscript received December 4, 2019; revised June 2, 2020.

Corresponding author email: [email protected]

doi:10.12720/jcm.15.7.566-571

Transportation Systems (ITS) [6]. The scope of ITS is

broader than V2V, as it encompasses railway, maritime,

and aeronautical transportation systems [7]. Motivations

for ITS include increased system efficiency, reduced

transportation delays, economic growth, passenger

entertainment, and the most important issue is the safety.

This is most acute in V2V, as automobile traffic accidents

still claim thousands of lives each year in large developed

nations [8].

V2V is used to study the communication between

vehicles in travelling road. The importance of V2V

comes from the benefits that it enhances the road safety,

knows the road conditions like accidents or traffic jams,

relays signals from one vehicle to another and/or lets

users communicate and exchange multimedia information

[9]. Fig. 1 shows the V2V communication network, it

consists of more than one vehicle on the road in the dame

travelling destination or opposite. The communication

between vehicles affected by the road conditions or area

surrounding the vehicles. The vehicle's speed also is an

important parameter that affects the communication link

and changes the channel characteristics during

communication.

Fig. 1. V2V communication [10]

The V2V channel is distinct from that of many typical

communication system channels. The closest comparison

may be to the cellular channel with some differences

between them such as the antenna heights of both

transmitter (Tx) and receiver (Rx) in V2V are low and

mobile [11]. In addition to this, V2V channels can have

the Line of Sight (LOS) between Tx and Rx obstructed

more frequently and scattering is often non-isotropic. Due

to the vehicle moving, the channel variation rates can also

be larger than in cellular, or in other words, the V2V

channel is statistically stationary for a shorter time period

than in cellular.

In addition, due to multiple scattering or rapid time

variation, in some cases amplitude fading may be more

severe than in the most common cellular fading model

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Journal of Communications Vol. 15, No. 7, July 2020

©2020 Journal of Communications

[12]. Because of this, there are three main parameters

affect the V2V channel and considered important to

optimized in order to have high performance of the

communication. These parameters are the path loss which

represents the power loss due to the distance between

transmitter and receiver and it used to calculate the link

budget of the network [13]. The second one is the Delay

Spread which is coming from the frequency selectivity

that is quantified as the coherence bandwidth. The last

parameter is the Doppler Spread which is reciprocally

related to the channel’s coherence [14].

This paper aims to study the BER and throughput

performances of the use of different Multicarrier

modulation like FBMC and OFDM in V2V channel

communication in order to overcome the aforementioned

challenges that V2V faces. The remainder of this paper is

organized as follows: Section II discusses the use of

Multicarrier modulation in V2V with some concentrate

on using FBMC due to the less Out-of-Band (OOB)

emission and Peak-to-Average Power Ratio (PAPR).

Section III shows the proposed system and methodology

used in this simulation. Section IV addresses BER

performance and throughput results for the use of FBMC

compared with to the results obtained from the use of

OFDM. Section V concludes the paper.

II. MULTICARRIER MODULATION USED IN V2V

Multicarrier modulation (MCM) techniques enable

transmission of a set of data over multiple narrow band

subcarriers simultaneously and they considers suitable

techniques to overcome the pathloss that affects the V2V

communication and the delay spread in the short range

communication [15]. Fig. 2 shows the general block

diagram of MCM transmitter and receiver. It consists of

generating Inverse Fast Fourier Transform (IFFT)

symbols with addition to adding CP to the MCM symbols

at the transmitter. The receiver section consists of the

opposite operations of the transmitter. The main example

of the MCM is the Orthogonal Frequency Division

Multiplexing (OFDM) which becomes the most widely

used baseband MCM waveform in modern wireless

communication systems. However, OFDM encounter

large side lobes that contributes to undesired Out-of-Band

(OOB) emission and large Peak-to-Average Power Ratio

(PAPR) [16]. Excessive OOB emissions partially results

from large side lobes at the baseband that leads to strong

Adjacent Channel Interference (ACI), especially in small

cell systems such as those proposed for use in TV white

space and heterogeneous networks [17].

Fig. 2. General multicarrier transmitter and receiver block diagram for

point to point [18]

Since OFDM is widely considered as a promising

candidate for such applications, Cheng, et al. [19]

simulates OFDM to improve spectral efficiency as well

as enhanced reliability in V2X channels with correlated

frequency-selective fading and inevitable Doppler effects.

Bazzi, et al. [20] investigates the radio resource

management problem for D2D-based V2V

communications by using multi-carrier concept. Matolak

[21] simulates the propagation channel of V2V network

by knowing the channel impulse response and its Fourier

transform in order to mitigate the interference during

transmission. The same is reported by Feteiha and

Hassanein [22] by discussing the performance of V2V

system when using LTE-Advanced (LTE-A) networks

where vehicles act as relaying cooperating terminals. The

simulation results of this study show significant diversity

gains are achievable, and that error rates can be greatly

reduced.

Filter Bank Multi-Carrier (FBMC) modulation is a

family of MCM techniques proposed as an alternative to

OFDM to overcome the aforementioned drawbacks [23].

Different from OFDM, the real and imaginary parts of the

QAM symbols are processed separately with 2×symbol

rate and the zero ISI and ICI with small amount of side

lobes are resulted from the use of filters at the transmitter

and receivers. This construction of FBMC leads to

enlarge the latency of the process, but with enhanced

spectral efficiency [24]. As shown in Fig. 2 at the

receiver, the sub bands do not overlap with each other,

and between sub bands, a small number of guards to are

left to accommodate asynchronous transmission [25]. The

required number of guard tones depends on the transition

region of the filters.

Fig. 3 shows how FBMC makes the waveforms more

flexible to use and coexistence because of the flexibility

of the time-frequency arrangement/allocation besides the

uniform distribution of the FBMC. Compared to OFDM,

the FBMC waveform in V2V communication has short

Transmission Time Interval (TTI) duration and enlarged

subcarrier spacing [26] which are suitable to overcome

the delay and Doppler spread of the channel.

Fig. 3. Flexibility and coexistence of waveforms enabled by FBMC [24]

N-

IFFT

CP

insertionf(n) h(n) + f *(-n)

CP

removal

N-

FFTEq.

TX RX

S*(n)

z(n)

Data r(n)Data

Received

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III. SYSTEM MODEL AND METHODOLOGY

Fig. 3 shows the proposed system model in this paper

in order to evaluate the throughput and BER of the

FBMC with comparison with OFDM. The figure shows

that there are two lines of the road, the first line consists

of vehicles that are travelling in the same direction with

the same speed of travelling. The communication

between them is affected by the channel response H. The

use of multicarrier in V2V communication is used here in

this simulation. The same road has another line of

travelling where vehicles travel in the opposite direction.

The simulation is performed for the two vehicles

travelling in the same direction.

This simulation considers the geometry of the highway

scenario with two vehicle travels along the road in one

direction towards the base station with 140 km/h speed

for both of them. All communication between vehicles

are assumed to be LoS communication to simplify the

process simulation.

Fig. 4. The system model

As mentioned before, the communication is assumed

to be LoS, the channel characteristics that are used in this

simulation scenario is obtained as:

( ) ( )

∑ ( )

∑ ( )

∑ ( )

where P, Q, and R are respectively the numbers of mobile

discrete scatters, static scatters, and diffuse scatters and τ,

τp, τq, and τr are excess delays, which can be

immediately computed by the geometry taking into

account only single bounce. After calculating the channel

response H, the comparison between using several types

of multicarrier waveforms are simulated in order to make

a full simulated overview about the advantages of using

FBMC as a modulation and multiple access techniques in

modern wireless communication. The modulation order is

changed and the BER and throughput as a performance

metric of this paper are obtained.

The simulation parameters which are used in this paper

are shown in Table I. The carrier frequency used is 2.8

GHz where the subcarrier spacing equals 15 kHz to

mitigate interference between sub-carriers. The FFT size

used for OFDM is 1024 with available bandwidth 10

MHz. The speed of vehicles is assumed to be the same

along the road which is equal to 140 km/h. The

modulation orders used are QPSK, 16-QAM, and 64-

QAM. The numbers of vehicles on the road are two in the

same direction of travelling.

TABLE I: THE SIMULATION PARAMETERS AND VALUES

Parameters Values

Carrier frequency 2.8 GHz

Subcarrier spacing 15 kHz

FFT size 1024

System bandwidth 10 MHz

Modulation QPSK, 16-QAM, 64-QAM

Cyclic prefix LTE normal cyclic prefix

Vehicles speed in the same direction 140 km/h

Number of vehicles to be simulated 2

Number of transmitters 1

Static scattering 10

Mobile scattering 10

Diffused scattering 200

The simulation starts from initializing the simulator

used with all simulation parameters required as

mentioned in Table I. The process starts to check what

the multicarrier waveform to be used is. The process

continues and the cyclic prefix is performed. The

throughput and BER performance are calculated and

graphed with respect to different values of SNR. The

modulation order of QAM changed to another value and

simulated with FBMC. The BER results of different

QAM modulation are compared together.

IV. SIMULATION RESULTS AND DISCUSSION

Fig. 5. Power spectral densities for OFDM and FBMC/OQAM

In Fig. 5, the spectral densities of both OFDM and

FBMC over the frequency have been shown. It is clear

568



(1)

that the FBMC spectral density shows a considerable

reduction in the Out-Of-Band (OOB) leakage compared

to OFDM, which can gives enhancement of an

asynchronous transmission without the need of perfect

synchronization with efficient use of spectrum due to the

absence of the CP.

Modulation order in wireless communication is very

important to be simulated and estimated. The QAM

modulation is also one of the modulation schemes that is

used in modern wireless communication. QAM is also the

preferable modulation scheme for OFDM because of its

immunity to the interference. The study of changing the

order of QAM modulation is performed to study its effect

on the FBMC signal. Fig. 6 shows an acceptable result

with the original QAM BER curves. It shows that the

BER of the QPSK modulation is better than that obtained

from the 16-QAM and 64-QAM. This is because lower

adjacent symbol interference. The spectral efficiency of

the high order modulation is preferable. Because of this,

the combination between FBMC and high order

modulation is preferable in modern wireless

communication because of the high spectral efficiency

comes from the high order modulation and the immunity

to nose and OOB comes from the use of FBMC.

Fig. 6. BER response of FBMC with different QAM order

Fig. 7. BER performance for the FBMC compared to OFDM

In order to study the FBMC effects on the V2V

channel, the OFDM is applied to the proposed system and

the BER of both MCM is graphed in Fig. 7 which shows

the BER performance of FBMC compared to the OFDM.

The response of both multiple accesses is the same until

11 dB. This is logical because FBMC and OFDM are the

same in building except that the FBMC uses filter at the

end of transmitter and at the beginning of the receiver.

This enhancement appeared in Fig. 7 because FBMC in

general aims to overcome some of the shortcomings that

were encountered with OFDM which arises from the fact

that OFDM requires the use of CP that reduces the

throughput of the transmission and also wastes power. In

FBMC, the use of filter at transmitter and receiver

removes the side lobes from the signal which leads to a

much cleaner carrier signal.

The throughput of the FBMC is greater than the

throughput of the OFDM as shown in Fig. 8. FBMC has

higher throughput than OFDM due to higher usable

bandwidth and because of using filtering in FBMC which

reduces the effect of CP on the signal especially when CP

is large. Compared to the previous figures, OFDM and

FBMC have the same transmit power which leads to a

smaller SNR for FBMC compared to OFDM because the

power is spread over a larger bandwidth. Fig. 8 shows

that the throughput FBMC reaches 6.5 Mbit/s at 30 dB

SNR while it reaches only 5 Mbit/s at the same value of

SNR in case of OFDM.

Fig. 8. Throughput of the system compared between FBMC and CP-

OFDM

V. CONCLUSION

The goal of this paper is to make a full overview and a

practical simulation of the use of different types of

multicarrier waveforms used in modern wireless

communication especially for V2V communication. The

simulation results show that the response of all

multicarrier waveforms for low mobility V2V networks is

the same because FBMC and OFDM are the same in

building except that the FBMC uses filter at the end of

the transmitter and at the beginning of the receiver. The

throughput of the FBMC is greater than OFDM at high

SNR values and the BER performance of FBMC is more

-5 0 5 10 15 20 25 30 3510

-3

10-2

10-1

100

SBR (dB)

BE

R

QPSK

16-QAM

64-QAM

-5 0 5 10 15 20 25 30 35 4010

-3

10-2

10-1

100

SNR [dB]

BE

R

FBMC

OFDM

-10 -5 0 5 10 15 20 25 300

1

2

3

4

5

6

7

SNR (dB)

Thro

ughput

[Mbit/s

]

FBMC

OFDM

569



enhanced than OFDM especially above 20 dB SNR.

There are several possible future works like an increasing

number of vehicles on the road, considering the effect of

interference between them and considering the effect of

packet size during transmission.

CONFLICT OF INTEREST

The authors declare no conflict of interest

AUTHOR CONTRIBUTIONS

Saif H. Alrubaee and Mohammed A. Altahrawi have

prepared and analyzed the data; Mahamod Ismail has

reviewed the research; Bara B. Burhan has modified the

paper organization and outline. All authors have

approved the final version.

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Saif Alrubaee was born in Baghdad,

Iraq, in 1993. He received the B.Eng.

degree in Communication and

Electronics Engineering from the

University of Almamoon University

College, Iraq, in 2015 and the M.Sc.

degree in Communication and Computer

Engineering from Universiti Kebangsaan

Malaysia (UKM), Bangi, Malaysia, in

2019. His research interests include mobile communications and

wireless networking with particular interest on the Internet of

Vehicles (IoV).

Mahamod Ismail was born in Selangor,

Malaysia, in 1959. He received the

B.Eng. degree in Electronics and

Electrical Engineering from the

University of Strathclyde, United

Kingdom, in 1985 and the M.Sc. degree

in Communications Engineering and

Digital Electronics from the University

of UMIST, Manchester, United Kingdom,

in 1987. He is currently a professor at the

Department of Electrical, Electronic and System Engineering.

His research interests include mobile communications and

wireless networking with particular interest on radio resource

management for 4G and beyond.

Mohammed Altahrawi was born in

Gaza Strip, Palestine, in 1981. He

received the B.Eng. degree in

Communication and Computer

Engineering from the Islamic University

of Gaza, Palestine, in 2003 and the M.Sc.

degree in Communication and Computer



2016. His research interests include Internet of Vehicles (IoV),

Multi-Radio access technology, and Software Defined

Vehicular Network (SDVN).

Bara Burhan was born in Baghdad, Iraq,

in 1993. He received the B.Eng. degree

in Communication and Electronics

Engineering from the University of

Almamoon University College, Iraq, in

2016 and the M.Sc. degree in

Communication and Computer



2019. His research interests include

mobile communications and wireless networking with particular

interest on the Internet of Vehicles (IoV).

571



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