In the recent year’s vehicular ad hoc networks VANETs has received significant attention in the intelligent transport system research. Vehicle-to-vehicle V2V communication can be considered an important approach to help the drivers to satisfy requirements like less congestion, accident warning, road exploration, etc. The propagation issues such as path loss, multipath fading, shadowing loss, depolarization loss, and polarization mismatch loss significantly affect the reliability of V2V communication. The goal of this paper is to evaluate the performance of the PHY layer in V2V communication using a modified Spatial Channel Model SCM-MIMO. Presented at Nets4Cars: 6-8 Oct 2014, Saint Petersburg, Russia.

1. Feasibility Study of Utilising
SCM – MIMO Channel Model
in V2V Communication
Presented by:
Ahmad Al-Khalil
Team:
Ali Al-Sherbaz, Scott Turner and Ahmad Al-Khalil
The University of Northampton - UK

2. Outline
• Aim.
• Introduction.
• Vehicle to Vehicle V2V Advantages.
• V2V Communication Challenges.
• SCM – MIMO Channel Model in V2V.
• Simulation Results.
• Discussion.
• Conclusion and Future Works.
Nets4Cars: 6-8 Oct 2014, Saint Petersburg, Russia.

3. Aim
Utilising the Spatial Channel Model SCM as a Vehicle to Vehicle V2V
Channel Model using MIMO 2x2 Alamouti coding.
Nets4Cars: 6-8 Oct 2014, Saint Petersburg, Russia.

4. Introduction
In the recent years the advent of Vehicular Ad-hoc Networks considers one of the most
important developments in the wireless communications systems.
In a recent United Nation UN road safety report around the world, it was documented
that road safety deaths made up 2.2% of the leading causes of death in 2004 . It has been
predicted that this will rise to 3.6% by 2030.
There have been recommendations made by the Global Status Report regarding the poor
collaboration between the sectors made responsible for collecting and reporting data on
road traffic incidents.
These recommendations have also included communication between the police, health
and transport services and their ability to man such operations.
There is therefore a need to provide the driver with information regarding traffic and
road conditions to reduce these incidents, thus will keep many people's lives.
Nets4Cars: 6-8 Oct 2014, Saint Petersburg, Russia.

5. Wireless Ad-hoc Networks System
Nets4Cars: 6-8 Oct 2014, Saint Petersburg, Russia.
Wireless ad-hoc
Networks
Wireless Mesh
Networks
Mobile ad-hoc
Networks
Vehicular ad-hoc
Networks
Vehicle to
Vehicle V2V
Vehicle to
Infrastructure
V2IWireless sensor
Networks

6. Vehicle to Vehicle V2V Advantages
Nets4Cars: 6-8 Oct 2014, Saint Petersburg, Russia.
• Provides real time safety.
• It does not need any roadside Infrastructure.
• Battery power is generated during the journey, providing an
extended battery life.

7. Vehicle to Vehicle V2V Communication Challenges
Nets4Cars: 6-8 Oct 2014, Saint Petersburg, Russia.
• High mobility of nodes and rapidly changing network topology:
In V2V, the connectivity between the vehicles may not be there all
the time since the vehicles are moving at different velocities due to
which there might be a high mobility and/or quick network
topology changes.
• V2V communication is not very useful in case of Sparsely
connected or low density vehicular networks.

8. SCM – MIMO Channel Model in V2V
Nets4Cars: 6-8 Oct 2014, Saint Petersburg, Russia.
• Channel Model.
• Environment Setting.
• Simulation Parameters setting.

9. Channel Model
Nets4Cars: 6-8 Oct 2014, Saint Petersburg, Russia.
The following equation is representing the mathematical model of V2V
communication which is used to estimate the channel parameters in microcell
suburban.
ℎ 𝑟,𝑠,𝑛
𝐼
𝑡
=
𝑃𝑛
𝑀
𝑚=1
𝑀 𝑉1 (𝜃 𝑚,𝑛,𝐴𝑜𝐷exp 𝑗 (𝑘𝑑 𝑠 sin ( 𝜃 𝑚,𝑛,𝐴𝑜𝐷) + Φ 𝑛,𝑚)] ×
𝑉2 (𝜃 𝑛,𝑚,𝐴𝑜𝐴exp 𝑗 𝑘𝑑 𝑟 sin 𝜃 𝑛,𝑚,𝐴𝑜𝐴 ×
exp (𝑗𝑘𝑣 cos 𝜃 𝑛,𝑚,𝐴𝑜𝐴 − 𝜃𝑣 𝑡)
The channel model is constructed under the condition of MIMO and Alamouti-coding.

10. Environments Settings
Nets4Cars: 6-8 Oct 2014, Saint Petersburg, Russia.
Three different environments are considered in the simulation tests
according to the level of disruption.
 Low level disruption environment.
The number of clusters N=2 and the number of sub-path clusters M=5.
 Medium level disruption environment.
The number of clusters N=3 and the number of sub-path clusters M=10.
 High level disruption environment.
The number of clusters N=6 and the number of sub-path clusters M=20.

11. Simulation Setting
Nets4Cars: 6-8 Oct 2014, Saint Petersburg, Russia.
Parameters Values
Environment Urban Microcell
Vehicles Antennas Omnidirectional
Antennas Array MIMO 2x2
Antennas Spacing 6 Lambda
AOD and AOA 180 ± 5
Carrier Frequency 5.9 GHz
Vehicles Speeds 10, 30 and 60
Modulations BPSK, QPSK, 8QAM and 16QAM

13. Low Level of Disruption Simulation Results
Nets4Cars: 6-8 Oct 2014, Saint Petersburg, Russia.

14. Medium Level of Disruption Simulation Results
Nets4Cars: 6-8 Oct 2014, Saint Petersburg, Russia.

15. High Level of Disruption Simulation Results
Nets4Cars: 6-8 Oct 2014, Saint Petersburg, Russia.

16. VEHICLE
SPEEDS
BPSK QPSK 8-QAM 16-QAM
Disruption Levels Disruption Levels Disruption Levels Disruption Levels
L M H L M H L M H L M H
10 kmh 22 31 35 25 31 40 30 40 44 30 40 47
30 kmh 29 25 30 31 29 31 36 31 38 37 33 39
60 kmh 22 29 30 26 30 31 30 34 36 31 38 39
Discussion
Different Vehicle Speeds Vs. SNR (dB) at BER=10-4
Nets4Cars: 6-8 Oct 2014, Saint Petersburg, Russia.

17. Conclusion and Future Work
The amount of parameters which are considered in this channel model try to reflect
the highly dynamic environment in V2V.
Due to the high mobility, in different speeds some results are inconsistent and
having larger period of time, that could reduce the inconsistency.
However, it is recommended to choose:
More robust space time coding techniques which are reliable in highly dynamic
environment and mobility.
Using 4x4 MIMO with OFDM technique.
Using V-Blast, T-Blast are more reliable/robust coding which is highly
recommended.
Nets4Cars: 6-8 Oct 2014, Saint Petersburg, Russia.

18. THANKS
QUESTIONS?
AHMAD.AL-KHALIL@NORTHAMPTON.AC.UK
AHMAD.AL-KHALIL@HOTMAIL.COM