modeling inter-vehicle communication in multi-lane highways: a stochastic geometry approach

Modeling Inter-vehicle Communication in Multi-lane Highways: A Stochastic

Geometry Approach

Muhammad Junaid Farooq, Hesham ElSawy, Mohamed-Slim Alouini

Electrical Engineering ProgramDivision of Computer, Electrical and Mathematical Sciences and Engineering (CEMSE)

King Abdullah University of Science & Technology (KAUST)

Sept 06, 2015

Outline1. Introduction

2. Related Work

3. Motivation

5. Local IVC with un-saturated transmission buffers

6. Conclusion

2Sept 06, 2015

Outline

Introduction

Related Work

Motivation

Local IVC with saturated transmission buffers

Local IVC with un-saturated transmission buffers

Conclusion

MODELING INTER-VEHICLE COMMUNICATION IN MULTI-LANE HIGHWAYS

MODELING INTER-VEHICLE COMMUNICATION IN MULTI-LANE HIGHWAYS 3

IntroductionWhy Vehicular Communication?• Safety• Coordinated braking• Emergency reporting• Risk alerts

• Efficiency• Adaptive traffic control• Congestion avoidance• Automatic toll collection

• Convenience• Driverless cars• Internet access• Peer-to-peer networking

Image taken from US Department of Transportation website

Sept 06, 2015

Introduction

Outline

Introduction

Related Work

Motivation



Conclusion


Related Work

Sept 06, 2015

Related Work

Vehicular Networks (VANETs)

Stochastic geometry

Queueing theory, Markov models,

graph theory

CSMA

ALOHA

Single lane abstraction

(SLA)

Our ModelAppr

ox.

Outline

Introduction

Related Work

Motivation



Conclusion


Modeling of Vehicular Communication• Channel impairments (e.g. fading , shadowing etc..)

• Multiple access of shared wireless spectrum • ALOHA• Carrier sense multiple access (CSMA)

• Interference and network geometry

Sept 06, 2015

Introduction

tRsR

or

Rx Tx

Outline

Introduction

Related Work

Motivation



Conclusion


Motivation • Highways can be extremely wide (e.g. Interstate 5

in US, Ontario Highway in Canada etc…) and existing models are unable to capture them.

• To develop an analytical framework for Inter-vehicle communication in a multi-lane highway setup.

• To gain insights from the developed model for the design of system parameters in vehicular networks.

Sept 06, 2015

IntroductionIntroductionMotivation

Figure: Aerial view of a multi-lane highway

Outline

Introduction

Motivation

Related Work



Conclusion


Modeling Approaches• Single lane model

• Multi-lane model

• 2D-PPP model

Sept 06, 2015


Outline

Introduction

Related Work

Motivation



Conclusion


Signal-to-interference-plus-noise ratio (SINR)• SINR is one of the main performance metrics, defined as:

• It is affected by the radio environment and the network geometry.

• It can be used to evaluate network performance metrics such as:• Probability of transmission success, • Average transmission rate, • Transmission capacity,

Sept 06, 2015

Introduction

1 \

SINR =

iij o

oN

ij iji

v v

Phr

Ph v

[ ]SINR T P P

[ln(1 )]SINRE

P

Outline

Introduction

Related Work

Motivation



Conclusion


Signal-to-interference-plus-noise ratio (SINR)• SINR is a function of several random variables:

• Stochastic geometry can be used as an effective tool to deal with random network topologies.• The network topology can be abstracted as a point process.• Using stochastic geometry tools, SINR statistics can be obtained as a function of

the point process.• Stochastic geometry provides spatial average of the performance metrics.

Sept 06, 2015

Introduction

1 \

SINR =

iij o

i

vi

ij

oN

j

v

P r

v

h

hP

Outline

Introduction

Related Work

Motivation



Conclusion


Local IVC• Each traffic lane is modeled by an independent Poisson point process (PPP), of intensity (cars/km).

• The speed of transmission is much faster than the speed of vehicles. Thus mobility effects are ignored.

• The sensing threshold controls the sensing range of the CSMA

Sept 06, 2015

Local IVC with unsaturated transmission buffers

l

1

11s

th

PR

i

Outline

Introduction

Related Work

Motivation



Conclusion


Tradeoffs imposed by sensing threshold• Higher sensing threshold leads to smaller sensing range and hence higher intensity of interferers.

• High interference intensity results in lower SINR and hence lower success probability.

• Higher number of concurrent transmitters implies higher spatial frequency reuse efficiency.

Sept 06, 2015


Figure: Multi-lane highway (N=7) with large sensing threshold

Outline

Introduction

Related Work

Motivation



Conclusion


Tradeoffs imposed by sensing threshold• Lower sensing threshold leads to larger sensing range and hence lower intensity of interferers.

• Lower interference results in higher SINR and thus higher success probability.

• Lower number of simultaneous transmitters implies lower spatial frequency reuse efficiency.

Sept 06, 2015


Outline

Introduction

Motivation

Related Work


Conclusion


Problem Statement• Characterize the operation of carrier sense multiple access (CSMA) coordinated multi-lane highway vehicular networks.

• Quantify the tradeoff imposed by the spectrum sensing threshold.

• Reveal the interference underestimation problem in existing models.

• Not all vehicles will have packets to send at the same time.

• To capture the effect of unsaturated buffers in the modeling and analysis of vehicular networks.

Sept 06, 2015


Outline

Introduction

Related Work

Motivation



Conclusion


Contribution• Develop a tractable framework based on stochastic geometry to model CSMA coordinated inter-vehicle communication in a multi-lane highway network.

• Highlight the underestimation problem of the MHCPP-II when used for the developed model and propose a simple approximation for the dependent thinning probability to mitigate the underestimation problem in the 1-D case.

• Optimize the sensing threshold to balance the tradeoff between the probability of transmission success and the spatial frequency reuse efficiency.

• Develop a queueing model to characterize the transmission probability of a vehicle.

• Obtain the transmission probability using an iterative procedure.

• Incorporate the transmission probability in the developed analytical framework.

Sept 06, 2015


Outline

Introduction

Related Work

Motivation



Conclusion


Methodology (Spatial Domain)• Single-lane model

• To characterize the probability of success, we require• Probability density function (pdf) of the distance between transmitter and receiver.• Probability density function of the aggregated interference.

• Evaluating the pdf of aggregated interference is not possible. However stochastic geometry allows us to calculate the Laplace transform of aggregate interference.

Sept 06, 2015


Outline

Introduction

Related Work

Motivation



Conclusion


Methodology (Spatial Domain)• Multi-lane model

•Procedure• Identify concurrent transmitters via dependent thinning of the PPPs to form MHCPPs.• Approximate each MHCPP with an equidense PPP.• Project the resulting homogeneous PPPs to non-homogeneous PPPs on the central lane via

transformation of intensity.

Sept 06, 2015


Outline

Introduction

Related Work

Motivation



Conclusion


Methodology (Spatial Domain)• Approximations• Aggressive Interference Approximation• Using the intensity after maximum compression

• Conservative Interference Approximation• By ignoring the effects of compression

Sept 06, 2015


Outline

Introduction

Related Work

Motivation



Conclusion


Methodology (Time Domain)• Each vehicle has a transmission buffer of size .

• Packets arrive at the buffer with a constant arrival rate .

• Packets are successfully processed and leave the buffer at a rate of .

• A vehicle transmits only when the channel is idle and attempts to retransmit until successful.

•The causality problem

Sept 06, 2015


nAp

Bp

Outline

Introduction

Motivation

Related Work


Conclusion


The MHCPP-II• Due to the CSMA contention, only a subset of vehicles can transmit at the same time, .

• The concurrent transmitters can be modeled by the Matérn hard core point process of type II (MHCPP-II).

Sept 06, 2015


0.40.10.70.5 0.40.6 0.8 0.2 0.9

Outline

Introduction

Motivation

Related Work


Conclusion


The MHCPP-II• The MHCPP-II thinning procedure underestimates the intensity of interferes.

Sept 06, 2015


Outline

Introduction

Motivation

Related Work



Conclusion

0.8 0.7 0.6 0.5


Results• Model validation for single-lane highway.

Sept 06, 2015


Outline

Introduction

Motivation

Related Work


Conclusion


Results• Multi-lane highway under sparse and dense traffic.

Sept 06, 2015


Outline

Introduction

Motivation

Related Work


Conclusion


Results• Optimizing sensing threshold to maximize the transmission capacity (i.e. Number of successful transmissions per unit length).

April 15, 2015


Outline

Introduction

Motivation

Related Work



Conclusion


Conclusions• Analytical framework for modeling CSMA coordinated Inter-vehicle communication in Multi-Lane Highways (saturated and unsaturated transmission buffers).

• Approximations for the probability of transmission success.

• The SLA model is not an accurate model for wide highways.

• With proper manipulation of the sensing threshold, the transmission capacity and the spatial frequency reuse can be maximized.

Sept 06, 2015

Outline

Introduction

Motivation

Related Work



Conclusion


Thank You!• Questions?

Sept 06, 2015

modeling inter-vehicle communication in multi-lane highways: a stochastic geometry approach

Documents

multilane highways7

multilane highways3sept

multilane highways6sept

multilane highway setup

motivation highways

network geometry

developed model

related worksept