MIMO Systems Theory and Applications Part 1 ppt

Polarization Diversity and Antenna Selection 3Kosai Raoof, Maha Ben Zid, Nuttapol Prayongpun and Ammar Bouallegue Geometrical Detection Algorithm for MIMO Systems 57 Z.. Yuk Joint LS Est

MIMO SYSTEMS,

THEORY AND APPLICATIONSEdited by H Khaleghi Bizaki

MIMO Systems, Theory and Applications

Edited by H Khaleghi Bizaki

Published by InTech

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Copyright © 2011 InTech

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Image Copyright Bella D, 2010 Used under license from Shutterstock.com

First published March, 2011

Printed in India

A free online edition of this book is available at www.intechopen.com

Additional hard copies can be obtained from orders@intechweb.org

MIMO Systems, Theory and Applications, Edited by H Khaleghi Bizaki

p cm

ISBN 978-953-307-245-6

free online editions of InTech

Books and Journals can be found at

www.intechopen.com

Polarization Diversity and Antenna Selection 3

Kosai Raoof, Maha Ben Zid, Nuttapol Prayongpun and Ammar Bouallegue

Geometrical Detection Algorithm for MIMO Systems 57

Z Y Shao, S W Cheung and T I Yuk

Joint LS Estimation and ML Detection for Flat Fading MIMO Channels 69

Shahriar Shirvani Moghaddam and Hossein Saremi

Semi-Deterministic Single Interaction MIMO Channel Model 87

Arghavan Emami-Forooshani and Sima Noghanian

Information Theory Aspects 113 Another Interpretation

of Diversity Gain of MIMO Systems 115

Shuichi Ohno and Kok Ann Donny Teo

Rate-Adaptive Information Transmission over MIMO Channels 133

Marco Zoffoli, Jerry D Gibson and Marco Chiani

Analysis of MIMO Systems in the Presence

of Co-channel Interference and Spatial Correlation 155

Dian-Wu Yue and Qian WangContents

Cellular MIMO Systems 187

Xiang Chen, Min Huang,Ming Zhao, Shidong Zhou and Jing Wang

Iterative Optimization Algorithms to Determine Transmit and Receive Weights for MIMO Systems 265

Osamu Muta, Takayuki Tominaga, Daiki Fujii, and Yoshihiko Akaiwa

Beamforming Based on Finite-Rate Feedback 285

Pengcheng Zhu, Lan Tang, YanWang and Xiaohu You

Computationally Efficient Symbol Detection Schemes in Multi-Device STBC-MIMO Systems 315

Daniel C Lee and Muhammad Naeem

Application and Case Studies 333 Analysis and Mitigation of Phase Noise

in Centralized/De-centralized MIMO Systems 335

Wei Zhang, Xiujun Zhang, Shidong Zhou and Jing Wang

A List Based Detection Technique for MIMO Systems 351

Having Polarization Diversity 415

Kadir M.F A., Suaidi M.K and Aziz M Z A

VLSI Implementation of Least Square Channel Estimation and QPSK Modulation Technique for 2×2 MIMO System 421

Sudhakar Reddy Penubolu and Ramachandra Reddy Gudheti

Efficient Implementation of MIMO Decoders 439

Muhammad S Khairy, Mohamed M Abdallah and S E.-D Habib

MIMO System Implementation

for WSN Using Xilinx Tools 455

Wael M El-Medany

Experimental Evaluation of MIMO Coded

Modulation Systems: are Space-Time

Block Codes Really Necessary? 463

Francisco J Vázquez Araújo, José A García-Naya,

Miguel González-López, Luis Castedo and Javier Garcia-Frias

Chapter 19

Chapter 20

Chapter 21

Chapter 22

In recent years, it was realized that the Multiple Input Multiple Output (MIMO) munication systems seems to be inevitable in accelerated evolution of high data rates applications The MIMO systems, have received considerable att ention of researchers and commercial companies due to their potential to dramatically increase the spectral

com-effi ciency and simultaneously sending individual information to the corresponding

users in wireless systems Today, the main question is how to include multiple antennas

at transmitt er and receiver side and, what are the appropriate methods of detection and signal processing strategies for specifi c applications?

This book, intends to provide highlights of the current research topics in the fi eld of MIMO system, to off er a snapshot of the recent advances in this area This work is mainly destined to cover an overview of the major issues faced today by researchers

in the MIMO related areas Also, it is accessible to anyone with a scientifi c background desiring to have an up-to-date overview of this domain

The book is writt en by specialists working in universities and research centers all over the world to cover the fundamental principles and main advanced topics on high data rates wireless communications systems over MIMO channels Various aspects of the-ses systems are deeply discussed by emphasis of their recent applications in fi ve part and twenty-two chapters Moreover, the book has the advantage of providing a collec-tion of applications that are completely independent and self-contained; thus, the in-terested reader can choose any chapter and skip to another without losing continuity Each chapter provides a comprehensive survey of the subject area and terminates with

a rich list of references to provide an in-depth coverage of the application at hand.The fi ve parts of the book is managed as follows:

Part 1 Introduction, Detection and Channel Estimation Strategies

The fi rst part contains four chapters that investigate an introduction to MIMO systems models together with discussion about diversity, beam forming and, space time cod-ing The geometrical decoding in MIMO channels by name of latt ice decoding, and other type of decoding such as: LS, LMMSE, ML, MAP and joint LSML are considered, too Finally, the mathematical semi-deterministic MIMO channel model based on elec-tromagnetic scatt ering and refl ecting is developed and discussed in details

Part 2 Information Theory Aspects

Part2 focus on information theory aspects of MIMO systems, including diversity-gain

of MIMO systems, which highlight the trade-off between capacity and bandwidth

effi ciency and rate adaptive source encoding, where the rate is adapted to follow the slow variations of the MIMO channel Then, the capacity of MIMO system is investi-gated in the presence of both co-channel interference and spatial correlation Finally, theoretical analysis for both ergodic and outage capacities of downlink transmission together with capacity analysis of uplink cellular MIMO systems by considering the co-channel interference as well as the eff ect of transmit power control are presented.

Part 3 Pre-processing and Post-processing in MIMO Systems

The non-linear precoder by name of Tomlinson-Harashima Precoder and ing are the main core of this part At fi rst, the capacity of MIMO- THP in perfect and non-perfect CSI is obtained In continue the conventional THP design is developed for imperfect, correlated and channel estimator error as robust, improved and joint opti-mization, respectively Joint THP transceiver design for the multi-user MIMO down-link system under both perfect and imperfect CSI is developed, too Iterative optimi-zation algorithm to determination of transmit and receive beam forming weights for eigen-beam SDM in multi-user MIMO systems is discussed under constraint of both total transmit power and the maximum transmit power The recent advance in beam forming based on fi nite-rate feedback from a communication-theoretic prespective is addressed as ideal and non-ideal factors of feedback link Finally, the problem of sym-bol detection in Multi-Device STBC-MIMO systems is addressed So, two evolutionary optimization methods by names, Biogeography-Based Optimization and Estimation of Distribution Algorithm are proposed to solve the problem of detection in a MD-STBC-MIMO system

beamfom-Part 4 Application and Case Studies

This part contain some advanced application of MIMO systems and some main notes

in their implementations, which started by MIMO-OFDM technique that investigate the eff ects of phase noise in centralized and distributed narrowband MIMO systems, and discuss the feasibility of phase and frequency synchronization problem In con-tinue a novel threshold list subset detector that extends the List subset detector for an iterative turbo-MIMO system is considered Then, a narrowband interference suppres-sion technique is discussed in MIMO systems This part terminates with fundamentals for pragmatic MIMO performance evaluation which consider some important notes in implementation from antenna and propagation perspectives

Part 5 Implementation and Experimental Evaluation

This part starts with some practical methods for capacity measurement and hardware implementation of MIMO system with QPSK modulation The implementation of the MIMO system together with sphere decoding and space time coding is discussed, es-pecially with emphasis on wireless sensor network perspective The above hardware implementation and practical measurement of MIMO system emphasis their potential

of dramatically increase of spectral effi ciency and their bott lenecks where should be considered in practice

Finally, the editor would like to thank all the authors for their excellent contributions

in the diff erent areas of MIMO systems and hopes that this book will be of valuable help to the readers

H Khaleghi Bizaki

Iran

Kosai RAOOF1, Maha BEN ZID2,4, Nuttapol PRAYONGPUN3and Ammar

1,2UJF-Grenoble I, Gipsa Lab - UMR 5216 CNRS

3College of Industrial Technology

4National Engineering School of Tunis (ENIT), 6’Com Lab

as polarization diversity and antenna selection We gradually provide an overview of theMIMO features from basic to more advanced topics The first sections of this chapter start byintroducing the key aspects of the MIMO theory The MIMO system model is first presented in

a generic way Then, we proceed to describe diversity schemes used in MIMO systems MIMOtechnology could exploit several diversity techniques beyond the spatial diversity Thesetechniques essentially cover frequency diversity, time diversity and polarization diversity

We further provide the reader with a geometrically based models for MIMO systems Thevirtue of this channel modeling is to adopt realistic methods for modeling the spatio-temporalchannel statistics from a physical wave-propagation viewpoint Two classes for MIMOchannel modeling will be described These models involve the Geometry-based StochasticChannel Models (GSCM) and the Stochastic channel models Besides the listed MIMO channelmodels already described, we derive and discuss capacity formulas for transmission overMIMO systems The achieved MIMO capacities highlight the potential of spatial diversity forimproving the spectral efficiency of MIMO channels When Channel State Information (CSI)

is available at both ends of the transmission link, the MIMO system capacity is optimallyderived by using adaptive power allocation based on water-filling technique The chaptercontinues by examining the combining techniques for multiple antenna systems Combiningtechniques are motivated for MIMO systems since they enable the signal to noise ratio (SNR)maximization at the combiner output The fundamental combing techniques are the MaximalRatio Combining (MRC), the Selection Combining (SC) and the Equal Gain Combining(EGC).Once the combining techniques are analyzed, the reader is introduced to the beamformingprocessing as an optimal strategy for combining The use of multiple antennas significantly

Advanced MIMO Techniques: Polarization

Diversity and Antenna Selection

1

improves the channel spectral efficiency Nevertheless, this induces higher system complexity

of the communication system and the communication system performance is effected due tocorrelation between antennas that need to be deployed at the same terminal As such, theantenna selection algorithm for MIMO systems is presented To elaborate on this point, weintroduce Space time coding techniques for MIMO systems and we evaluate by simulationthe performance of the communication system Next, we emphasis on multi polarizationtechniques for MIMO systems As a background, we presume that the reader has a thoroughunderstanding of antenna theory We recall the basic antenna theory and concepts that areused throughout the rest of the chapter We rigorously introduce the 3D channel modelover the Non-Line of Sight (NLOS) propagation channel for MIMO system with polarizedantennas We treat the depolarization phenomena and we study its effect on MIMO systemcapacity The last section of the chapter provides a scenario for collaborative sensor nodesperforming distributed MIMO system model which is devoted to sensor node localization inWireless Sensor Networks The localization algorithm is based on beamforming processingand was tested by simulation Our chapter provides the reader by simulation examples foralmost all the topics that have been treated for MIMO system development and key issuesaffecting achieved performance

2 MIMO literature and mathematical model

This section gives an overview of the MIMO literature MIMO technology has been asubject of research since the last decade of the twentieth century In 1984, Jack Winters

at Bell Laboratories wrote a patent on wireless communications using multiple antennas.Jack Winters in (Winters, 1987) presented a study of the fundamental limits on the datarate of multiple antenna systems in a Rayleigh fading environment The concept of MIMOwas introduced for two basic communication systems which are a communication systembetween multiple mobiles and a base station with multiple antennas and another one betweentwo mobiles with multiple antennas In 1993, Arogyaswami Paulraj and Thomas Kailathproposed the concept of spatial multiplexing using MIMO They filed a patent on spatialmultiplexing emphasized applications to wireless broadcast Several articles which focused

on MIMO concept were published in the period from 1986 to 1995 We mainly cite the article

of Emre Teletar titled "Capacity of multi-antenna gaussian channels" (Telatar, 1995) This wasfollowed by the work of Greg Raleigh and Gerard Joseph Foschini in 1996 (Foshini, 1996)which invented new approaches involving space time coding techniques These approacheswere proved to increase the spectral efficiency of MIMO systems (Raleigh & John, 1998)

In 1999, Thomas L Marzetta and Bertrand M Hochwald published an article (Marzetta &Hochwald, 1999) which provides a rigorous study on the MIMO Rayleigh fading link takinginto consideration information theory aspects Afterwards, MIMO communication techniqueshave been developed and brought completely on new perspectives wireless channels Thefirst commercial MIMO system was developed in 2001 by Iospan Wireless Inc Since 2006,several companies such as Broadcom and Intel have concerned a novel communicationtechnique based on the MIMO technology for improving the performance of wireless LocalArea Network(LAN) systems The new standard of wireless LAN systems is named IEEE802.11n MIMO technology has attracted more attention in wireless communications In fact,

it was used to boost the link capacity and to enhance the reliability of the communicationlink MIMO scheme is the major candidate technology in various standard proposals forthe fourth-generation of wireless communication systems Enhanced techniques for MIMOcommunications led to advanced technologies for achieving successful radio transmission It

promises significant improvements in spectral efficiency and network coverage We mainlycite multiple access MIMO systems, Ad-hoc MIMO , cooperative MIMO (Wang et al., 2010)and cooperative MIMO in sensor networks (Shuguang et al., 2004) Note that cooperativeMIMO systems use multiple distributed transmitting devices to improve Quality of Service(QoS) at one/multiple receivers This was shown to bring saves in energy and to improvethe link reliability in Wireless Sensor Network (WSN) where multiple sensor nodes can becooperatively functioned In the following, we introduce the mathematical model for MIMOsystems We briefly describe the flat fading MIMO channel and the continuous time delayMIMO channel model.

Flat fading MIMO channel

Fig 1 Generic MIMO system model

Generic MIMO system with N T transmit antennas and N Rreceive antennas is depicted inFig 1 Such model is typically used for cases where the frequency domain channel transferfunction remains approximately constant over the bandwidth of the transmitted waveformand is referred to as the flat fading scenario The input output relationship for this MIMOsystem is defined as :

where :

• H is the(N R × N T)complex channel matrix described as :

H= [h1, , hN T]

hp = [h 1p , , h N R p]T; p=1, , N T is the complex channel vector which links the

transmit antenna Tx p to the N R receive antennas Rx1, , Rx N

5

Advanced MIMO Techniques: Polarization Diversity and Antenna Selection