Practical Guide to MIMO Radio Channel E-Book

Tim Brown

• Herausgeber: John Wiley & Sons
• Kategorie: Wissenschaft und neue Technologien
• Sprache: Englisch

Seitenzahl: 423

Veröffentlichungsjahr: 2012

Beschreibung

This book provides an excellent reference to the MIMO radio channel In this book, the authors introduce the concept of the Multiple Input Multiple Output (MIMO) radio channel, which is an intelligent communication method based upon using multiple antennas. Moreover, the authors provide a summary of the current channel modeling approaches used by industry, academia, and standardisation bodies. Furthermore, the book is structured to allow the reader to easily progress through the chapters in order to gain an understanding of the fundamental and mathematical principles behind MIMO. It also provides examples (i.e. Kroenecker model, Weicheselberger model, geometric and deterministic models, and ray tracing), system scenarios, trade-offs, and visual explanations. The authors explain and demonstrate the use and application of these models at system level. Key Features: * Provides a summary of the current channel modeling approaches used by industry, academia and standardisation bodies * Contains experimental and measurement based results * Provides a comprehensive down to earth approach with concise and visual explanations of MIMO Radio Channel * Covers a variety of system scenarios and explains the trade-offs involved in each * Accompanying website containing MATLAB code and solutions to related problems href="http://www.tim.brown76.name/MIMObook">http://www.tim.brown76.name/MIMObook) Practical Guide to the MIMO Radio Channel with MATLAB examples is an invaluable reference for R&D engineers and professionals in industry requiring familiarisation with the concept, and engineers entering the field or working in related fields seeking an introduction to the topic. Postgraduate and graduate students will also find this book of interest.

Leseprobe

Contents

Cover

Title Page

Copyright

Dedication

Preface

List of Abbreviations

List of Symbols

Chapter 1: Introduction

1.1 From SISO to MISO/SIMO to MIMO

1.2 What Do We Need MIMO For?

1.3 How Does MIMO Work? Two Analogies

1.4 Conditions for MIMO to Work

1.5 How Long Has MIMO Been Around?

1.6 Where is MIMO Being Used?

1.7 Purpose of the Book

Chapter 2: Capacity of MIMO Channels

2.1 Some Background on Digital Communication Systems

2.2 Notion of Capacity

2.3 Channel State Information and Fading

2.4 Narrowband MIMO Model

2.5 Capacity of the Time-Invariant Channel

2.6 Fast Fading Channels with CSIT Distribution: Ergodic Capacity

2.7 Slow Fading Channel with CSIT Distribution: Outage Probability and Capacity with Outage

2.8 Chapter Summary Tables

2.9 Further Reading

Chapter 3: MIMO Transceivers

3.1 MIMO Receivers

3.2 Transceivers with CSI at Transmitter and Receiver: Transmit and Receive Beamforming

3.3 Space–Time Block Codes

3.4 D-Blast

3.5 Chapter Summary Tables

3.6 Further Reading

Chapter 4: MIMO Channel Models

4.1 SISO Models and Channel Fundamentals

4.2 Challenges in MIMO Channel Modelling

4.3 Summary

Chapter 5: MIMO Antenna Design

5.1 Antenna Element Fundamentals

5.2 Single Antenna Design

5.3 Designing Array Antennas for MIMO

5.4 Impact of Antenna Design on the MIMO Radio Channel

5.5 Evaluating Antenna Impact on the MIMO Channel

5.6 Challenges in Compact MIMO Antenna Design and Examples

5.7 Summary

Chapter 6: MIMO in Current and Future Standards

6.1 Wireless Channel Modelling in Standards

6.2 Current Wireless Standards Employing MIMO and the Corresponding Channel Models

6.3 MIMO in Other Areas

6.4 Concluding Remarks and Future Wireless Systems

Appendix: Some Useful Definitions

Bibliography

Index

This edition first published 2012 © 2012 John Wiley & Sons Ltd

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Library of Congress Cataloging-in-Publication Data

Brown, Tim, 1976-

Practical guide to the MIMO radio channel with MATLAB® examples / Tim Brown, Elisabeth De Carvalho, Persefoni Kyritsi.

p. cm.

Includes bibliographical references and index.

ISBN 978-0-470-99449-8 (cloth) – ISBN 978-1-119-94495-9 (PDF) – ISBN 978-1-119-94496-6 (oBook)

1. MIMO systems. I. De Carvalho, Elisabeth. II. Kyritsi, Persefoni. III. Title.

TK5103.4836.B76 2012

621.384–dc23

2011042249

A catalogue record for this book is available from the British Library.

ISBN: 9780470994498

To Rebecca

To my family

To my teachers and my students

Preface

The purpose of this book is to introduce the concept of the Multiple Input Multiple Output (MIMO) radio channel, which is an intelligent communication method based upon using multiple antennas. The book opens by explaining MIMO in layman's terms to help students and people in industry working in related areas become easily familiarised with the concept. Therefore the structure of the book will be carefully arranged to allow a user to progress steadily through the chapters and understand the fundamental and mathematical principles behind MIMO through the visual and explanatory way in which they will be written. It is the intention that several references will also be provided, leading to further reading in this highly researched technology.

MIMO emerged in the mid 1990s from work by engineers at Bell Labs as a natural evolution of long existing beamforming and diversity techniques that use multiple antennas to improve the communication link. MIMO is able to ascertain different paths over the air interface by using multiple antennas at both ends, thus creating sub-channels within one radio channel and increasing the data transmission (or capacity) of a radio link. This is a promising and important technology to meet the growing demands of high data rate wireless communications. Research has broken out worldwide, for which there is still a great need for further research and innovation in the future. Many questions have been raised over the viability of MIMO against cost and complexity though it has already been deployed in wireless local area networks and is coming back into the third generation (3G) standards for mobile cellular communications. Many people working in several related areas such as developing low cost antennas and radio transceivers need to gain understanding of and appreciation for the concept for which they will be used.

At present there is no book written in a down-to-earth manner which explains MIMO from the perspective of the antennas and the radio channel; the vast majority of books look at it from a system encoding perspective. Therefore, in the interests of those who are concerned with the way in which MIMO uses the propagation channel, this book will be valuable to any university library and also people in industry seeking to be informed about MIMO from a more visual rather than mathematical perspective. Though the reader may be new to MIMO, they would be expected to have some competence in mathematics (mainly linear algebra), foundational aspects of antennas, radio propagation and digital communications (such as modulation and coding) as well as signal processing.

Within this book is also contained, where appropriate, some MATLAB® examples that will be invaluable in helping to implement the MIMO channel models described in the book. Illustrations are also used widely within the book, especially where describing MIMO antennas and the propagation channel is concerned. It is hoped that this will give the reader an instant opportunity to begin experimenting with MIMO channel models, as well as consideration to the most appropriate channel model for their application.

We would like to acknowledge everyone who has allowed images to be used in this publication as well as the advice they have provided on certain aspects of the book.

An accompanying website containing MATLAB® code relating to this book is available at www.tim.brown76.name/MIMObook.

Tim Brown, University of Surrey, UKElisabeth De Carvalho, Aalborg University, DenmarkPersefoni Kyritsi, Aalborg University, Denmark

List of Abbreviations

AMCAdaptive Modulation and CodingAOAAngle of ArrivalAODAngle of DepartureARQAutomatic Repeat reQuestASAngular SpreadBANBody Area NetworkBERBit Error RateBPRBranch Power RatioBSBase StationCDMACode Division Multiple AccessCDFCumulative Distribution FunctionCDLCluster Delay LineCOSTEuropean Co-operation of Science and TechnologyCSIChannel State InformationCSITChannel State Information at TransmitterCSIRChannel State Information at ReceiverDGDiversity GainDSDelay SpreadDVB-T2Second Digital Video Broadcasting Standard for TerrestrialEDGEEnhanced Data Rates for GSM EvolutionEPWBMExperimental Plane Wave Based MethodFDMAFrequency Division Multiple AccessFFFast FadingGPRSGeneral Packet Radio ServiceGSMGlobal System for MobileHARQHybrid Automatic Repeat RequestHEHorizontal EncodingHFHigh FrequencyIEEEInstitute of Electrical and Electronic Engineersi.i.dIndependently Identically DistributedIQHAIntelligent Quadrifilar Helix AntennaISIInter Stream InterferenceLOSLine of SightLTELong Term Evolution (of third generation mobile)MACMedia Access Control layerMATLABMATrix LABoratoryMEGMean Effective GainMFMatched FilterMIMOMultiple Input Multiple OutputMISOMultiple Input Single OutputMLMaximum LikelihoodMMSEMinimum Mean Squared ErrorMRCMaximal Ratio CombiningMSMobile StationMSEMean Square ErrorNBNarrowBandNLOSNon Line of SightOFDMOrthogonal Frequency Division MultiplexingPANPersonal Area NetworkPDFProbability Density FunctionPHYPhYsical LayerPIFAPlanar Inverted F AntennaQAMQuadrature Amplitude ModulationQO-STBCQuasi Orthogonal Space Time Block CodingQPSKQuadrature Phase Shift KeyingRMSRoot Mean SquareRxReceiverSDMASpace Division Multiple AccessSFShadowing Factor or Slow FadingSICSuccessive Interference CancelationSIMOSingle Input Multiple OutputSISOSingle Input Single OutputSNRSignal to Noise RatioSTCSpace Time CodeSTBCSpace Time Block CodeSTTCSpace Time Trellis CodeSVDSingular Value DecompositionTDMATime Division Multiple AccessTITime InvariantTRPTotal Radiated PowerTxTransmitterUHFUltra High FrequencyUMMSEUnbiased MMSEUTRAUniversal Terrestrial Radio AccessV-BlastVertical Bell Labs Space TimeVEVertical EncodingWBWidebandWiMAxWorldwide Interoperability for Microwave AccessWINNEREuropean project Wireless World INitiative New RadioWLANWireless Local Area NetworkWMFSpatial Whitening Matched FilterXPRCross Polar RatioZMCCSZero Mean Complex Circularly SymmetricZFZero Forcing3GPPThird Generation Partnership Project3G SCMThird Generation Spatial Channel Model

List of Symbols

The following notations defined will in many cases use a subscript and/or superscript for a specific case within the book, though the physical quantity or variable that it represents remains the same.

AQuantity of complex amplitude due to an E-fieldaVector with elements of complex amplitude due to an E-fieldaQuantity of complex amplitude due to an E-fieldαAngle relative to a lineBFrequency bandwidthbBit sequenceβPhase constant, 2π/λCMatrix for deriving phase weightsCCapacity in bits/s/HzcSpeed of light, 2.98 × 108m/sc()Codeword encryption functionDDirectivityDmMaximum antenna dimension in metresdSeparation between two points in spaceE[x]Expected value function of vector or matrix xEQuantity of complex E-fieldeVector with elements of complex incident E-fieldFMatrix for deriving phase weightsfFrequencyGPre processing matrixGAntenna gainΓReflection coefficientγPath loss exponent or eigen channel to noise coefficientHMIMO channel matrixHSISO channel in the case of a frequency dependent wideband channel impulse, or magnetic H-fieldhSISO channel coefficientIIdentity matrixKfRice factorKArbitrary constantkrScattering coefficientkInteger used for a discrete sampleLMatrix of scattering coefficients of different polarisationLLoss factorlSpatial separation between two points in metresλFree space wavelengthλiEigenvalue (used in this form where there is an integer number subscript i)MInteger numbermInteger numberμA mean valueNInteger numbernVector containing streams of additive Gaussian white noisenInteger number or additive Gaussian white noisePPower matrixProotDiagonal matrix of square root of power elementsPQuantity of powerProbability distribution functionpPower density at an angular point or probability quantityp′Normalised power density at an angular pointϕAngle or phaseϕAngle or phaseQCholesky factor of covariance matrix RqComplex coefficientθAngle or phaseθAngle or phaseRCorrelation or covariance matrixMaximal transmission rateRQuantity of resistance in Ohms, covariance or transmission rateRBit rate in bits per transmissionrPhysical separation between two points in metresrHChannel matrix rankρMean signal to noise ratio used to define capacityCorrelation between two points x and y at a transmitterCorrelation between two points x and y at a receiverSMatrix of singular valuesSPower densitysSingular valueσValue of standard deviationTTime periodTxyScattering coefficient of de-polarisation from polarisation state x to state ytQuantity of time in secondsτTime delayUxUnitary and orthogonal eigenvector matrixuxEigenvectoruEigenvector elementVUnitary and orthogonal eigenvector matrixVQuantity of voltagevEigenvector or vector describing voltages at an N-port networkEigenvector elementVelocity of a mobile terminalDigital codewordComplex phase weightwVector of complex phase weightsParameter matrix to describe the channel used in the Weichselberger modelXQuantity of reactance in Ohms or an input data packet variableInput codewordxVector of input data streamsxInput data stream or distance in metresx+Function to return the positive or zero value of x, or zero otherwisexHHermitian transpose of vector or matrix xYOutput data packet variableOutput codewordyVector of output data streamsyOutput data stream or distance in metresZImpedance matrixZQuantity of complex impedance in OhmsCandidate codewordzCandidate data streamzDistance in metresζAngle or phase

Chapter 2

Capacity of MIMO Channels

Elisabeth De Carvalho

This chapter is dedicated to the capacity of MIMO channels. Capacity is a performance measure for digital communication systems. It is the maximal transmission rate for which a reliable communication can be achieved. If the transmission rate gets larger than the capacity, the system ‘breaks down’ and the receiver makes decoding errors with a non negligible probability. Capacity is the primary tool to characterise the performance of MIMO systems and it also serves in practical system as a guide to properly design the transmitted signals as well as the processing of the received signals.

Wireless communications exhibit different characteristics and performance according to the propagation environment. Those characteristics have to be carefully taken into account when defining capacity. In this chapter, capacity is described according to two factors impacting the performance:

1.The knowledge of the channel at the transmitter and receiver. A common assumption, adopted throughout the chapter, is that the channel is perfectly known at the receiver. At the transmitter, two different types of channel knowledge are considered: either the instantaneous value of the channel is known or only its distribution is known.

2.The nature of the wireless channel. We treat three kinds of channels: the time invariant channel, the fast fading channel (the codewords spread over many channel variations) and the slow fading channel (the channel is constant across a given codeword).

We present capacity results for the following three scenarios which are the most common and practical ones.

The channel state information at the transmitter (CSIT) is the information 19 about the channel available at the transmitter while the channel state information at the receiver (CSIR) is the information about the channel available at the receiver. For each scenario, we build on the single input single output (SISO) and single input multiple output (SIMO) or multiple input single output (MISO) channels to present the multiple input multiple output (MIMO) channel. In particular we highlight the performance boost brought on by the multiplexing capabilities of systems with multiple antennas at both the transmitter and the receiver.

2.1 Some Background on Digital Communication Systems

Digital communications consist of the transfer of bits (0 or 1) from a transmitter to a receiver. The bits carry the information to be communicated, so the first important question is how the information is converted into bits and how bits can be transported through the communication medium.

2.1.1 Generation of Digital Signals

Some signals are in digital form: files in a computer or digital photographs. No further processing is needed in general before converting the signals for transmission. However, most signals are analogue, like voice or sound.

The conversion of analogue signals into digital signals is based on sampling and quantisation. The analogue signal is first sampled. The sampling rate should be sufficiently high, so that the sampling does not entail any loss of information, meaning that the original continuous time signal can be recovered from the sampled signal (Nyquist theorem). In the quantisation step, the samples are quantised to a finite number of levels. The sampled signal takes only a finite number of values. The set of possible values forms a finite alphabet. The number of elements in the finite alphabet is generally a power of 2 and hence can easily be represented by a sequence of bits.

To reduce the amount of information to be transmitted, the sampled data goes through a source coding or data compression