Tài liệu Communications and Networking doc

Communications

and Networking

edited by

Jun Peng

SCIYO

Communications and Networking

Edited by Jun Peng

Published by Sciyo

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Copyright © 2010 Sciyo

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First published September 2010

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Additional hard copies can be obtained from [email protected]

Communications and Networking, Edited by Jun Peng

p. cm.

ISBN 978-953-307-114-5

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Chapter 1

Chapter 2

Chapter 3

Chapter 4

Chapter 5

Chapter 6

Chapter 7

Chapter 8

Chapter 9

Chapter 10

Preface IX

Transform Domain based Channel Estimation for 3GPP/LTE Systems 1

Moussa Diallo, Rodrigue Rabineau, Laurent Cariou and Maryline Hélard

Channel Estimation for Wireless OFDM Communications 17

Jia-Chin Lin

OFDM Communications with Cooperative Relays 51

H. Lu, H. Nikookar and T. Xu

High Throughput Transmissions in OFDM

based Random Access Wireless Networks 81

Nuno Souto, Rui Dinis, João Carlos Silva,

Paulo Carvalho and Alexandre Lourenço

Joint Subcarrier Matching and Power Allocation

for OFDM Multihop System 101

Wenyi Wang and Renbiao Wu

MC-CDMA Systems: a General Framework

for Performance Evaluation with Linear Equalization 127

Barbara M. Masini, Flavio Zabini and Andrea Conti

Wireless Multimedia Communications

and Networking Based on JPEG 2000 149

Max AGUEH

Downlink Capacity of Distributed Antenna Systems

in a Multi-Cell Environment 173

Wei Feng, Yunzhou Li, Shidong Zhou and Jing Wang

Innovative Space-Time-Space Block Code

for Next Generation Handheld Systems 187

Youssef Nasser and Jean-François Hélard

Throughput Optimization forUWB-Based Ad-Hoc Networks 205

Chuanyun Zou

Contents

Chapter 11

Chapter 12

Chapter 13

Chapter 14

Chapter 15

Chapter 16

Chapter 17

Chapter 18

Chapter 19

Chapter 20

Chapter 21

Outage Probability Analysis of Cooperative Communications over

Asymmetric Fading Channel 221

Sudhan Majhi, Youssef Nasser and Jean François Hélard

Indoor Radio Network Optimization 237

Lajos Nagy

Introduction to Packet Scheduling Algorithms

for Communication Networks 263

Tsung-Yu Tsai, Yao-Liang Chung and Zsehong Tsai

Reliable Data Forwarding in Wireless Sensor Networks:

Delay and Energy Trade Off 289

M. K. Chahine, C. Taddia and G. Mazzini

Cross-Layer Connection Admission Control Policies

for Packetized Systems 305

Wei Sheng and Steven D. Blostein

Advanced Access Schemes for

Future Broadband Wireless Networks 323

Gueguen Cédric and Baey Sébastien

Medium Access Control in Distributed Wireless Networks 339

Jun Peng

Secure Trust-based Cooperative Communications

in Wireless Multi-hop Networks 359

Kun Wang, Meng Wu and Subin Shen

Wireless Technologies and Business Models

for Municipal Wireless Networks 379

Zhe Yang and Abbas Mohammed

Data-Processing and Optimization Methods

for Localization-Tracking Systems 389

Giuseppe Destino, Davide Macagnano and Giuseppe Abreu

Usage of Mesh Networking in a Continuous-Global Positioning System

Array for Tectonic Monitoring 415

Hoang-Ha Tran and Kai-Juan Wong

VI

This book “Communications and Networking” focuses on the issues at the lowest two layers

of communications and networking and provides recent research results on some of these

issues. In particular, it fi rst introduces recent research results on many important issues at the

physical layer and data link layer of communications and networking and then briefl y shows

some results on some other important topics such as security and the application of wireless

networks.

This book has twenty one chapters that are authored by researchers across the world. Each

chapter introduces not only a basic problem in communications and networking but also

describes approaches to the problem. The data in most chapters are based on published

research results and provide insights on the problems of the relevant chapters. Most chapters

in this book also provide references for the relevant topics and interested readers might fi nd

these references useful if they would like to explore more on these topics.

Several chapters of this book focus on issues related to Orthogonal Frequency-Division

Multiplexing (OFDM). For example, chapter 1 and chapter 2 are on channel estimation for

OFDM-related systems. Chapter 3 is on cooperative relays in OFDM systems. Chapter 4

introduces some recent results on packet separation in OFDM based random access wireless

networks. Chapter 4 is on sub-carrier matching and power allocation in oFDM-based

multihop systems. Chapter 6 presents some results on performance evaluation of OFDM

related systems.

Multiple chapters of this book are on coding, link capacity, throughput, and optimisation. For

example, chapter 7 and chapter 9 are about source and channel coding in communications

and networking. Chapter 8 is on link capacity in distributed antenna systems. Chapter 10

introduces throughput optimisation for UWB-based ad hoc networks. Chapter 12 presents

some results on optimising radio networks.

This book also contains several chapter on forwarding, scheduling, and medium access control

in communications and networking. In particular, chapter 13 introduces packet scheduling

algorithms for communication networks. Chapter 14 is about reliable data forwarding in

wireless sensor networks. Chapter 15 introduces cross-layer connection admission control

in packetized systems. Chapter 16 presents advanced access schemes for future broadband

wireless networks. Chapter 17 introduces medium access control in distributed wireless

networks. Finally, chapter 18 is about cognitive radio networks.

In addition, this book has several chapters on some other issues of communications and

networking. For example, chapter 19 is about security of wireless LANs and wireless multihop

networks, chapter 20 is on localisation and tracking and chapter 21 introduces the use of mesh

networks in tectonic monitoring.

Preface

In summary, this book covers a wide range of interesting topics of communications and

networking. The introductions, data, and references in this book would help the readers

know more about communications and networking and help them explore this exciting and

fact-evolving fi eld.

Editor

Jun Peng

University of Texas - Pan American,

Edinburg, Texas,

United States of America

X

1

Transform Domain based Channel

Estimation for 3GPP/LTE Systems

Moussa Diallo1, Rodrigue Rabineau1, Laurent Cariou1 and Maryline Hélard2

1Orange Labs, 4 rue du Clos Courtel, 35512 Cesson-Sévigné Cedex, 2INSA Rennes, 20 Avenue des Buttes de Coesmes, 35700 Rennes Cedex

France

1. Introduction

Orthogonal frequency division multiplexing (OFDM) is now well known as a powefull

modulation scheme for high data rate wireless communications owing to its many advantages,

notably its high spectral efficiency, mitigation of intersymbol interference (ISI), robustness to

frequency selective fading environment, as well as the feasibility of low cost transceivers [1].

On the other hand multiple input multiple output (MIMO) systems can also be efficiently

used in order to increase diversity and improve performance of wireless systems [2] [3] [4].

Moreover, as OFDM allows a frequency selective channel to be considered as flat on each

subcarrier, MIMO and OFDM techniques can be well combined. Therefore, MIMO-ODFM

systems are now largely considered in the new generation of standards for wireless

transmissions, such as 3GPP/LTE [5] [6].

In most MIMO-OFDM systems, channel estimation is required at the receiver side for all

sub-carriers between each antenna link. Moreover, since radio channels are frequency

selective and time-dependent channels, a dynamic channel estimation becomes necessary.

For coherent MIMO-OFDM systems, channel estimation relies on training sequences

adapted to the MIMO configuration and the channel characteristics [7] and based on OFDM

channel estimation with pilot insertion, for which different techniques can be applied:

preamble method and comb-type pilot method.

In order to estimate the channel of an OFDM systems, one’s first apply least square (LS)

algorithm to estimate the channel on the pilot tones in the frequency domain. A second step

can be performed to improve the quality of the estimation and provide interpolation to find

estimates on all subcarriers. In a classical way, this second step is performed in the

frequency domain. An alternative is to perform this second step by applying treatment in a

transform domain, that can be reached after a discrete Fourier transform (DFT) or a discrete

cosine transform (DCT), and called transform domain channel estimation (TD-CE). The DFT

based method is considered as a promising method because it can provide very good results

by significantly reducing the noise on the estimated channel coefficients [8]. However, some

performance degradations may occur when the number of OFDM inverse fast fourier

transform (IFFT) size is different from the number of modulated subcarriers [8]. This

problem called ”border effect” phenomenon is due to the insertion of null carriers at the

spectrum extremities (virtual carriers) to limit interference with the adjacent channels, and

can be encountered in most of multicarrier systems.

2 Communications and Networking

To cope for this problem, DCT has been proposed instead of DFT, for its capacity to reduce

the high frequency components in the transform domain [9]. Its improvements are however

not sufficient in systems designed with a great amount of virtual subcarriers, which suffer

from a huge border effect [10]. This is the case of a 3GPP/LTE system.

The aim of the paper is to study, for a 3GPP/LTE system, two improved DCT based channel

estimations, designed to correctly solve the problem of null carriers at the border of the

spectrum. These two TD-CE will also be compared in terms of performance and complexity.

In the first approach, a truncated singular value decomposition (TSVD) of pilots matrix is

used to mitigate the impact of the “border effect”. The second approach is based on the

division of the whole DCT window into 2 overlapping blocks.

The paper is organized as follows. Section II introduces the mobile wireless channel and

briefly describes the MIMO-OFDM system with channel estimation component. Section III is

dedicated to transform domain channel estimations (TD-CE), with description of the

classical Least Square algorithm in III-A, and presents the conventional DFT and DCT based

channel estimation in III-B and III-C, respectively. Next, the two proposed DCT based

channel estimation are described in the sections IV and V. Finally, a performance evaluation

and comparison is shown in section VI.

2. MIMO-OFDM system description

In this paper we consider a coherent MIMO-OFDM system, with Nt transmit antennas and

Nr receive antennas. As shown in Fig.1, the MIMO scheme is first applied on data

modulation symbols (e.g. PSK or QAM), then an OFDM modulation is performed per

transmit antenna. Channel estimation is then required at receive side for both the one tap

per sub-carrier equalization and the MIMO detection.

The OFDM signal transmitted from the i-th antenna after performing IFFT (OFDM

modulation) to the frequency domain signal Xi ∈CN×1 can be given by:

2 1

0

1 () () , 0 (,)

N kn j N i i

k

x n X ke nk N

N

π −

=

= ∑ ≤ ≤ (1)

where N is the number of FFT points.

The time domain channel response between the transmitting antenna i and the receiving

antenna j under the multipath fading environments can be expressed by the following

equation:

1

, ,

0

() ( )

L

ij ij l ij l

l

hn h n δ τ

−

=

= − ∑ (2)

CP

removal

CP

removal

Nr

1

CP

insert

CP

insert

Nt

1

extraction

Pilots

extraction

Pilots

Least square

channel estimation channel estimation

DCT

STBC

Insertion

Insertion

OFDM

OFDM

of pilot symbols

of pilot symbols

modulation

modulation

OFDM

demodulation

OFDM

demodulation

Equalization

Detection

&

Fig. 1. MIMO-OFDM block diagram.

Transform Domain based Channel Estimation for 3GPP/LTE Systems 3

with L the number of paths, hij,l and τij,l the complex time varying channel coefficient and

delay of the l-th path.

The use of a guard interval allows both the preservation of the orthogonality between the

tones and the elimination of the inter symbol interference (ISI) between consecutive OFDM

symbols. Thus by using (1) and (2), the received frequency domain signal is given by:

1

0

() () () ()

Nt

j i ij

i

R k X kH k k

−

=

= +Ξ ∑ (3)

where Hij(k) is the discrete response of the channel on subcarrier k between the i-th transmit

antenna and the j-th receive antenna and Ξk the zero-mean complex Gaussian noise after the

FFT (OFDM demodulation) process.

3. Transform Domain Channel Estimation (TD-CE)

In a classical coherent SISO-OFDM system, channel estimation is required for OFDM

demodulation. When no knowledge of the statistics on the channel is available, a least

square (LS) algorithm can be used in order to estimate the frequency response on the known

pilots that had been inserted in the transmit frame. An interpolation process allows then the

estimation of the frequency response of the channel, i.e. for each sub-carrier. In a MIMO￾OFDM system, since the received signal is a superposition of the transmitted signals,

orthogonally between pilots is mandatory to get the channel estimation without co-antenna

interference (CAI).

We choose to apply TD-CE to a 3GPP/LTE system where the orthogonality between

training sequences is based on the simultaneous transmission on each subcarrier of pilot

symbols on one antenna and null symbols on the other antennas as depicted in Fig.2.

A. Least Square channel estimation (LS)

Assuming orthogonality between pilots dedicated to each transmit antenna, the LS

estimates can be expressed as follows:

1

, ( ( )) . H H diag X ij LS ij

− = + Ξ (4)

Therefore LS estimates can be only calculated for t

M

N subcarriers where M is the number of

modulated subcarriers. Then interpolation has to be performed to obtain an estimation for

all the subcarriers.

B. DFT based channel estimation

From (4), it can be observed that LS estimates can be strongly affected by a noise component.

To improve the accuracy of the channel estimation, the DFT-based method has been

proposed in order to reduce the noise component in the time domain [8]. Fig.3 illustrates the

transform domain channel estimation process using DFT. After removing the unused

subcarriers, the LS estimates are first converted into the time domain by the IDFT algorithm

and a smoothing filter (as described in Fig.3) is applied in the time domain assuming that

the maximum multi-path delay is within the cyclic prefix of the OFDM symbols. After the

smoothing, the DFT is applied to return in the frequency domain.