Vehicular Technologies Increasing Connectivity Part 1 doc

the Geometrical T-Junction Model 1Ali Chelli and Matthias Pätzold Simulation of SISO and MIMO Multipath Fading Channels 11 Antonio Petrolino and Gonçalo Tavares User Scheduling and Partn

VEHICULAR TECHNOLOGIES: INCREASING CONNECTIVITY

Edited by Miguel Almeida

Vehicular Technologies: Increasing Connectivity

Edited by Miguel Almeida

Published by InTech

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Copyright © 2011 InTech

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Vehicular Technologies: Increasing Connectivity, Edited by Miguel Almeida

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ISBN 978-953-307-223-4

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the Geometrical T-Junction Model 1

Ali Chelli and Matthias Pätzold

Simulation of SISO and MIMO Multipath Fading Channels 11

Antonio Petrolino and Gonçalo Tavares

User Scheduling and Partner Selection for Multiplexing-based Distributed MIMO Uplink Transmission 35

Ping-Heng Kuo and Pang-An Ting

Resource Allocation for Multi-User OFDMA-Based Wireless Cellular Networks 51

Dimitri Kténas and Emilio Calvanese Strinati

From Linear Equalization to Lattice-Reduction-Aided Sphere-Detector as an Answer to the MIMO Detection Problematic in Spatial Multiplexing Systems 71

Sébastien Aubert and Manar Mohaisen

DFT Based Channel Estimation Methods for MIMO-OFDM Systems 97

Moussa Diallo,Maryline Hélard, Laurent Cariou and Rodrigue Rabineau

Channels and Parameters Acquisition

in Cooperative OFDM Systems 115

D Neves, C Ribeiro, A Silva and A Gameiro

Fast Power and Channel Adaptation for Mobile Users in OFDMA Multi-Cell Scenarios 137

L Reggiani, L Galati Giordano and L Dossi

Contents

Statistical Properties of the Capacity of Double Nakagami-m Channels for Applications in V2V

Dualhop Communication Systems 153

Gulzaib Rafiq, Bjørn Olav Hogstad and Matthias Pätzoldt

Resource Allocation and User Scheduling in Coordinated Multicell MIMO Systems 165

Edgar Souza, Robson Vieira, Mari Kobayashi and Mérouane Debbah

Hybrid Evolutionary Algorithm-based Schemes for Subcarrier, Bit, and Power Allocation in Multiuser OFDM Systems 185

Wei-Cheng Pao, Yung-Fang Chen and Yun-Teng Lu

Reduced-Complexity PAPR Minimization Schemes for MC-CDMA Systems 205

Mariano García Otero and Luis A Paredes Hernández

Cognitive Radio Communications for Vehicular Technology – Wavelet Applications 223

Murroni Maurizio and Popescu Vlad

Multiple Antenna-Aided Spectrum Sensing Using Energy Detectors for Cognitive Radio 239

Seung-Hoon Hwang and Jun-Ho Baek

New Method to Generate Balanced 2n-PSK STTCs 261

P Viland, G Zaharia and J.-F Hélard

Correlation Coefficients of Received Signal I and Q Components in a Domain with Time and Frequency Axes under Multipath Mobile Channel with LOS and NLOS 281

Shigeru Kozono, Kenji Ookubo, Takeshi Kozima and Tomohiro Hamashima

Multimodulus Blind Equalization Algorithm Using Oblong QAM Constellations

for Fast Carrier Phase Recovery 299

Jenq-Tay Yuan and Tzu-Chao Lin

Peak-to-Average Power Ratio Reduction for Wavelet Packet Modulation Schemes via Basis Function Design 315

Ngon Thanh Le, Siva D Muruganathan and Abu B Sesay

Outage Performance and Symbol Error Rate Analysis of L-Branch Maximal-Ratio Combiner for κ-µ and η-µ Fading 333

Mirza Milišić, Mirza Hamza and Mesud Hadžialić

Technological Issues in the Design

of Cost-Efficient Electronic Toll Collection Systems 359

José Santa, Rafael Toledo-Moreo, Benito Úbeda,

Miguel A Zamora-Izquierdo and Antonio F Gómez-Skarmeta

Propagation Aspects in Vehicular Networks 375

Lorenzo Rubio, Juan Reig and Herman Fernández

Propagation Path Loss Modelling

in Container Terminal Environment 415

Slawomir J Ambroziak, Ryszard J Katulski,

Jaroslaw Sadowski and Jacek Stefanski

Link Budgets: How Much Energy is Really Received 433

Aarne Mämmelä, Adrian Kotelba,

Marko Höyhtyä and Desmond P Taylor

Chapter 20

Chapter 21

Chapter 22

Chapter 23

This book covers the most recent advances concerning the ability to overcome tivity limitations and extend the link capacity of vehicular systems Ranging from the advances on radio access technologies to intelligent mechanisms deployed to enhance cooperative communications, cognitive radio and multiple antenna systems have been given particular highlight

connec-While some contributions do not off er an immediate response to the challenges that appear in some vehicular scenarios, they provide insight and research conclusions, from which Vehicle Networking Design can greatly benefi t Finding new ways to over-come the limitations of these systems will increase network reachability, service deliv-ery, from infrastructure to vehicles, and the inter-vehicle connectivity Having this in mind, particular att ention was paid to the propagation issues and channel character-ization models To overcome the current limitations over these systems, this book is mainly comprised of the following topics:

1 Multiple Antenna Systems, Cognitive Radio and Cooperative tions: focusing on multiple smart antenna systems, MIMO, OFDM, MC-CDMA sys-tems, cognitive radio advances

Communica-2 Transmission and Propagation: evaluating the propagation aspects of these systems, link layer coding techniques, mobile/radio oriented technologies, channel characterization, channel coding

It is our understanding that advances on vehicular networking technologies can

great-ly benefi t from the research studies presented herein In this book, we tried to rize the areas concerning physical and link layers with contributions that expose the state of the art for vehicular networks We are thankful to all of those who contributed

summa-to this book and who made it possible

Miguel Almeida

University of Aveiro

Portugal

Ali Chelli and Matthias Pätzold

University of Agder

Norway

1 Introduction

According to the European commission (Road Safety Evolution in EU, 2009), 1.2 million road

accidents took place in the European Union in 2007 These road accidents have resulted

in 1.7 million injuries and more than 40 thousand deaths It turned out that human errorswere involved in 93% of these accidents V2V communication is a key element in reducingroad casualties For the development of future V2V communication systems, the exactknowledge of the statistics of the underlying fading channel is necessary Several channelmodels for V2V communications can be found in the literature For example, the two-ringchannel model for V2V communications has been presented in (Pätzold et al., 2008) There, areference and a simulation model have been derived starting from the geometrical two-ringmodel In (Zaji´c et al., 2009), a three-dimensional reference model for wideband MIMO V2Vchannels has been proposed The model takes into account single-bounce and double-bouncescattering in vehicular environments The geometrical street model (Chelli & Pätzold, 2008)captures the propagation effects if the communicating vehicles are moving along a straightstreet with local roadside obstructions (buildings, trees, etc.) In (Acosta et al., 2004), astatistical frequency-selective channel model for small-scale fading is presented for a V2Vcommunication links

The majority of channel models that can be found in the literature rely on the stationarityassumption However, measurement results for V2V channels in (Paier et al., 2008) haveshown that the stationarity assumption is valid only for very short time intervals This factarises the need for non-stationary channel models Actually, if the communicating cars aremoving with a relatively high speed, the AoD and the AoA become time-variant resulting

in a non-stationary channel model The traditional framework invoked in case of stationarystochastic processes cannot be used to study the statistical properties of non-stationarychannels In the literature, quite a few time-frequency distributions have been proposed tostudy non-stationary deterministic signals (Cohen, 1989) A review of these distributions can

be found in (Cohen, 1989) Many commonly used time-frequency distributions are members ofthe Cohen class (O’Neill & Williams, 1999) It has been stated in (Sayeed & Jones, 1995) that theCohen class, although introduced for deterministic signals, can be applied on non-stationarystochastic processes

A Non-Stationary MIMO Vehicle-to-Vehicle

Channel Model Derived From the Geometrical T-Junction Model

1

In this chapter, we present a non-stationary MIMO V2V channel model The AoD andthe AoA are supposed to be time dependent This assumption makes our channel modelnon-stationary The correlation properties of a non-stationary channel model can be obtainedusing a multi-window spectrogram (Paier et al., 2008) For rapidly changing spectral contenthowever, finding an appropriate time window size is a rather complicated task The problem

is that a decrease in the time window size improves the time resolution, but reducesthe frequency resolution To overcome this problem, we make use of the Choi-Williamsdistribution proposed in (Choi & Williams, 1989) The extremely non-isotropic propagationenvironment is modelled using the T-junction scattering model (Zhiyi et al., 2009) In contrast

to the original multi-cluster T-model, we assume to simplify matters that each cluster consists

of only one scatterer Under this assumption, the reference and the simulation model areidentical The main contribution of this chapter is that it presents a non-stationary channelmodel with time-variant AoD and AoA Moreover, analytical expressions for the correlationproperties of the non-stationary channel model are provided, evaluated numerically, and thenillustrated

The rest of the chapter is organized as follows In Section 2, the geometrical T-model ispresented Based on this geometrical model, we derive a reference (simulation) model inSection 3 In Section 4, the correlation properties of the proposed channel model are studied.Numerical results of the correlation functions are presented in Section 5 Finally, we draw theconclusions in Section 6

2 The Geometrical T-junction Model

A typical propagation scenario for V2V communications at a T-junction is presented inFig 1 Fixed scatterers are located on both sides of the T-junction In order to derive thestatistical properties of the corresponding MIMO V2V channel, we first need to find ageometrical model that describes properly the vehicular T-junction propagation environment.This geometrical model is illustrated in Fig 2 It takes into account double-bounce scatteringunder non-line-of sight conditions Each building is modelled by one scatterer which makesour model extremely non-isotropic The scatterers in the neighborhood of the transmitter MST

are denoted by S T

m (m = 1, 2, , M), whereas the scatterers close to the receiver MSR are

designated by S R n (n = 1, 2, , N) The total number of scatterers near to the transmitter is

denoted by N, while the total number of scatterers near to the receiver is designated by M.

The transmitter and the receiver are moving towards the intersection point with the velocities

vTand vR, respectively The direction of motions of the transmitter and the receiver w.r.t the

x-axis are referred to as φ Tandφ R, respectively The AoD are time-variant and are denoted

byα T

m(t), while the symbolβ R

n(t)stands for the AoA The AoD and the AoA are independentsince double-bounce scattering is assumed The transmitter and the receiver are equipped

with an antenna array encompassing M T and M Rantenna elements, respectively The antennaelement spacing at the transmitter side is denoted byδ T Analogously, the antenna elementspacing at the receiver side is referred to asδ R The tilt angle of the transmit antenna array isdenoted byγ T, whileγ Rstands for the tilt angle for the receive antenna array The transmitter

(receiver) is located at a distance h T1 (h R1)from the left-hand side of the street and at a distance

h T2 (h R2)from the right-hand side seen in moving direction

Fig 1 Typical propagation scenario for V2V communications at a T-junction.

Fig 2 The geometrical T-Junction model for V2V communications

3 The Reference Model

The starting point for the derivation of the reference model for the MIMO V2V channel isthe geometrical T-junction model presented in Fig 2 For the reference model, we assumedouble-bounce scattering from fixed scatterers We distinguish between the scatterers near tothe transmitter and the scatterers close to the receiver It can be seen from Fig 2 that a wave

emitted from the lth transmit antenna element A T l (l=1, 2, , M T)travels over the scatterers

S T

m and S R

n before impinging on the kth receive antenna element A R

k (k=1, 2, , M R) Using

the wave propagation model in (Pätzold et al., 2008), the complex channel gain g kl ( r T, r R)

describing the link A T

A Non-Stationary MIMO Vehicle-to-Vehicle Channel

Model Derived From the Geometrical T-Junction Model

The symbols c mnandθ mn(t)stand for the the joint gain and the joint phase shift caused by the

scatterers S T m and S R n The joint channel gain can be written as c mn=1/√

MN (Pätzold et al.,

2008) The phase shiftθ mn(t)is a stochastic process, as the AoDα T

m(t)and the AoAβ R

n(t)aretime-variant This is in contrast to the models proposed in (Pätzold et al., 2008) and (Zhiyi

et al., 2009), where the phase shift is a random variable The joint phase shift can be expressed

asθ mn(t) = (θ m(t) +θ n (t))mod 2π, where mod stands for the modulo operation The terms

θ m(t)andθ 

n(t)are the phase shifts associated with the scatterers S T m and S n R, respectively.The second phase term in (1), k T

m · r T, is caused by the movement of the transmitter The wave

vector pointing in the propagation direction of the mth transmitted plane wave is denoted

where fmaxT =vT/λ denotes the maximum Doppler frequency associated with the mobility of

the transmitter The symbolλ refers to the wavelength The time-variant AoD α T

m(t)can beexpressed as

m(t1)at time instant t1and the AoDα T

m(t2)at time instant t2are equal if the angledifference| α T

m,i−1 is a constant that can be obtained from (3) by setting the time t to t i−1 Thelength of the intervals[t i−1 , t i)and[t i , t i+1)can be quite different for i =1, 2, The phaseshift introduced by a scatterer is generally dependent on the direction of the outgoing wave.Hence, a change in the AoDα T

m(t)results in a new random phase shift Since the AoDα T

m(t)

is defined piecewise, the phase shiftθ m(t)is also defined piecewise as follows

θ m(t)=θ m,i−1 if t i−1 ≤ t < t i for i=1, 2, (7)

where θ m,0, θ m,1, are independent identically distributed (i.i.d.) random variablesuniformly distributed over[0, 2π).

The third phase term in (1), k R

n · r R, is associated with the movement of the receiver Thesymbol k R

n stands for the wave vector pointing in the propagation direction of the nth received

plane wave, while r R represents the spatial translation vector of the receiver The scalarproduct k R

where fmaxR =vR/λ denotes the maximum Doppler frequency caused by the receiver

movement Using the geometrical T-junction model shown in Fig 2, the time-variant AoA

n(t 1) at time instant t 1 and the AoAβ R

n(t 2) at time instant t 2 are equal if the angledifference| β R

n,j−1 is a constant that can be obtained from (9) by setting the time t to t  j−1 The

length of the intervals[t  j−1 , t  j)and[t  j , t  j+1)can be quite different for j=1, 2, The phaseshift introduced by a scatterer is generally dependent on the direction of the incoming wave.Hence, a change in the AoAβ R

n(t)results in a new random phase shift Since the AoAβ R

n(t)

is defined piecewise, the phase shiftθ  n(t)is also defined piecewise as follows

θ  n(t)=θ  n,j−1 if t  j−1 ≤ t < t  j for j=1, 2, (13)whereθ  n,0,θ  n,1, are i.i.d random variables uniformly distributed over[0, 2π)

After substituting (2) and (8) in (1), the complex channel gain g kl(t)can be expressed as

A Non-Stationary MIMO Vehicle-to-Vehicle Channel

Model Derived From the Geometrical T-Junction Model