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Contents
Preface page xv Acknowledgements xviii
- 1 Introduction List of notation xx
- 1.1 Book objective
- 1.2 Wireless systems
- 1.3 Book outline
- 2.1 Physical modeling for wireless channels
- 2.1.1 Free space, fixed transmit and receive antennas
- 2.1.2 Free space, moving antenna
- 2.1.3 Reflecting wall, fixed antenna
- 2.1.4 Reflecting wall, moving antenna
- 2.1.5 Reflection from a ground plane
- 2.1.6 Power decay with distance and shadowing
- 2.1.7 Moving antenna, multiple reflectors
- 2.2 Input /output model of the wireless channel
- 2.2.1 The wireless channel as a linear time-varying system
- 2.2.2 Baseband equivalent model
- 2.2.3 A discrete-time baseband model - Discussion 2.1 Degrees of freedom
- 2.2.4 Additive white noise
- 2.3 Time and frequency coherence
- 2.3.1 Doppler spread and coherence time
- 2.3.2 Delay spread and coherence bandwidth
- 2.4 Statistical channel models
- 2.4.1 Modeling philosophy
- 2.4.2 Rayleigh and Rician fading
- 2.4.3 Tap gain auto-correlation function viii Contents - Example 2.2 Clarke’s model - Chapter 2 The main plot
- 2.5 Bibliographical notes
- 2.6 Exercises - and channel uncertainity 3 Point-to-point communication: detection, diversity
- 3.1 Detection in a Rayleigh fading channel
- 3.1.1 Non-coherent detection
- 3.1.2 Coherent detection - of freedom 3.1.3 From BPSK to QPSK: exploiting the degrees
- 3.1.4 Diversity
- 3.2.1 Repetition coding
- 3.2.2 Beyond repetition coding - Summary 3.1 Time diversity code design criterion - Example 3.1 Time diversity in GSM
- 3.3.1 Receive diversity
- 3.3.2 Transmit diversity: space-time codes
- 3.3.3 MIMO: a 2 × 2 example - Summary 3.2 2 × 2 MIMO schemes
- 3.4.1 Basic concept
- 3.4.2 Single-carrier with ISI equalization
- 3.4.3 Direct-sequence spread-spectrum
- 3.4.4 Orthogonal frequency division multiplexing - Summary 3.3 Communication over frequency-selective channels
- 3.5 Impact of channel uncertainty
- 3.5.1 Non-coherent detection for DS spread-spectrum
- 3.5.2 Channel estimation
- 3.5.3 Other diversity scenarios - Chapter 3 The main plot
- 3.6 Bibliographical notes
- 3.7 Exercises
- 4 Cellular systems: multiple access and interference management
- 4.1 Introduction
- 4.2 Narrowband cellular systems
- 4.2.1 Narrowband allocations: GSM system
- 4.2.2 Impact on network and system design
- 4.2.3 Impact on frequency reuse ix Contents - Summary 4.1 Narrowband systems
- 4.3 Wideband systems: CDMA
- 4.3.1 CDMA uplink
- 4.3.2 CDMA downlink
- 4.3.3 System issues - Summary 4.2 CDMA
- 4.4 Wideband systems: OFDM
- 4.4.1 Allocation design principles
- 4.4.2 Hopping pattern
- 4.4.3 Signal characteristics and receiver design
- 4.4.4 Sectorization - Example 4.1 Flash-OFDM - Chapter 4 The main plot
- 4.5 Bibliographical notes
- 4.6 Exercises
- 5 Capacity of wireless channels
- 5.1 AWGN channel capacity
- 5.1.1 Repetition coding
- 5.1.2 Packing spheres - channel codes Discussion 5.1 Capacity-achieving AWGN - and capacity Summary 5.1 Reliable rate of communication
- 5.2 Resources of the AWGN channel
- 5.2.1 Continuous-time AWGN channel
- 5.2.2 Power and bandwidth - Example 5.2 Bandwidth reuse in cellular systems
- 5.3 Linear time-invariant Gaussian channels
- 5.3.1 Single input multiple output (SIMO) channel
- 5.3.2 Multiple input single output (MISO) channel
- 5.3.3 Frequency-selective channel
- 5.4 Capacity of fading channels
- 5.4.1 Slow fading channel
- 5.4.2 Receive diversity
- 5.4.3 Transmit diversity - Summary 5.2 Transmit and recieve diversity
- 5.4.4 Time and frequency diversity - Summary 5.3 Outage for parallel channels
- 5.4.5 Fast fading channel
- 5.4.6 Transmitter side information - Example 5.3 Rate adaptation in IS-856
- 5.4.7 Frequency-selective fading channels
- 5.4.8 Summary: a shift in point of view x Contents - Chapter 5 The main plot
- 5.5 Bibliographical notes
- 5.6 Exercises
- 6 Multiuser capacity and opportunistic communication
- 6.1 Uplink AWGN channel
- 6.1.1 Capacity via successive interference cancellation
- 6.1.2 Comparison with conventional CDMA
- 6.1.3 Comparison with orthogonal multiple access
- 6.1.4 General K-user uplink capacity
- 6.2 Downlink AWGN channel
- 6.2.1 Symmetric case: two capacity-achieving schemes
- 6.2.2 General case: superposition coding achieves capacity - Summary 6.1 Uplink and downlink AWGN capacity - Discussion 6.1 SIC: implementation issues
- 6.3 Uplink fading channel
- 6.3.1 Slow fading channel
- 6.3.2 Fast fading channel
- 6.3.3 Full channel side information - Summary 6.2 Uplink fading channel
- 6.4 Downlink fading channel
- 6.4.1 Channel side information at receiver only
- 6.4.2 Full channel side information
- 6.5 Frequency-selective fading channels
- 6.6 Multiuser diversity
- 6.6.1 Multiuser diversity gain
- 6.6.2 Multiuser versus classical diversity
- 6.7 Multiuser diversity: system aspects
- 6.7.1 Fair scheduling and multiuser diversity
- 6.7.2 Channel prediction and feedback
- 6.7.3 Opportunistic beamforming using dumb antennas
- 6.7.4 Multiuser diversity in multicell systems
- 6.7.5 A system view - Chapter 6 The main plot
- 6.8 Bibliographical notes
- 6.9 Exercises
- 7 MIMO I: spatial multiplexing and channel modeling
- 7.1 Multiplexing capability of deterministic MIMO channels
- 7.1.1 Capacity via singular value decomposition
- 7.1.2 Rank and condition number
- 7.2 Physical modeling of MIMO channels xi Contents
- 7.2.1 Line-of-sight SIMO channel
- 7.2.2 Line-of-sight MISO channel
- 7.2.3 Antenna arrays with only a line-of-sight path
- 7.2.4 Geographically separated antennas
- 7.2.5 Line-of-sight plus one reflected path - Summary 7.1 Multiplexing capability of MIMO channels
- 7.3 Modeling of MIMO fading channels
- 7.3.1 Basic approach
- 7.3.2 MIMO multipath channel
- 7.3.3 Angular domain representation of signals
- 7.3.4 Angular domain representation of MIMO channels
- 7.3.5 Statistical modeling in the angular domain
- 7.3.6 Degrees of freedom and diversity - response models Example 7.1 Degrees of freedom in clustered
- 7.3.7 Dependency on antenna spacing
- 7.3.8 I.i.d. Rayleigh fading model - Chapter 7 The main plot
- 7.4 Bibliographical notes
- 7.5 Exercises
- 8 MIMO II: capacity and multiplexing architectures
- 8.1 The V-BLAST architecture
- 8.2 Fast fading MIMO channel
- 8.2.1 Capacity with CSI at receiver
- 8.2.2 Performance gains
- 8.2.3 Full CSI - Summary 8.1 Performance gains in a MIMO channel
- 8.3 Receiver architectures
- 8.3.1 Linear decorrelator
- 8.3.2 Successive cancellation
- 8.3.3 Linear MMSE receiver
- 8.3.4 Information theoretic optimality - and ISI equalization Discussion 8.1 Connections with CDMA multiuser detection
- 8.4 Slow fading MIMO channel
- 8.5 D-BLAST: an outage-optimal architecture
- 8.5.1 Suboptimality of V-BLAST
- 8.5.2 Coding across transmit antennas: D-BLAST
- 8.5.3 Discussion - Chapter 8 The main plot
- 8.6 Bibliographical notes
- 8.7 Exercises - space-time codes 9 MIMO III: diversity–multiplexing tradeoff and universal - 9.1 Diversity–multiplexing tradeoff
- 9.1.1 Formulation
- 9.1.2 Scalar Rayleigh channel
- 9.1.3 Parallel Rayleigh channel
- 9.1.4 MISO Rayleigh channel
- 9.1.5 2 × 2 MIMO Rayleigh channel
- 9.1.6 nt × nr MIMO i.i.d. Rayleigh channel - tradeoff 9.2 Universal code design for optimal diversity–multiplexing
- 9.2.1 QAM is approximately universal for scalar channels - Summary 9.1 Approximate universality
- 9.2.2 Universal code design for parallel channels - Summary 9.2 Universal codes for the parallel channel
- 9.2.3 Universal code design for MISO channels - Summary 9.3 Universal codes for the MISO channel
- 9.2.4 Universal code design for MIMO channels - Discussion 9.1 Universal codes in the downlink - Chapter 9 The main plot - 9.3 Bibliographical notes - 9.4 Exercises - 10 MIMO IV: multiuser communication
- 10.1 Uplink with multiple receive antennas
- 10.1.1 Space-division multiple access
- 10.1.2 SDMA capacity region
- 10.1.3 System implications - Summary 10.1 SDMA and orthogonal multiple access
- 10.1.4 Slow fading
- 10.1.5 Fast fading
- 10.1.6 Multiuser diversity revisited - receive antennas Summary 10.2 Opportunistic communication and multiple - 10.2 MIMO uplink
- 10.2.1 SDMA with multiple transmit antennas
- 10.2.2 System implications
- 10.2.3 Fast fading - 10.3 Downlink with multiple transmit antennas
- 10.3.1 Degrees of freedom in the downlink
- 10.3.2 Uplink–downlink duality and transmit beamforming
- 10.3.3 Precoding for interference known at transmitter
- 10.3.4 Precoding for the downlink
- 10.3.5 Fast fading - 10.4 MIMO downlink xiii Contents - 10.5 Multiple antennas in cellular networks: a system view - multiple access Summary 10.3 System implications of multiple antennas on
- 10.5.1 Inter-cell interference management
- 10.5.2 Uplink with multiple receive antennas
- 10.5.3 MIMO uplink
- 10.5.4 Downlink with multiple receive antennas
- 10.5.5 Downlink with multiple transmit antennas - Example 10.1 SDMA in ArrayComm systems - Chapter 10 The main plot - 10.6 Bibliographical notes - 10.7 Exercises
- Appendix A Detection and estimation in additive Gaussian noise - A.1 Gaussian random variables - A.1.1 Scalar real Gaussian random variables - A.1.2 Real Gaussian random vectors - A.1.3 Complex Gaussian random vectors - Summary A.1 Complex Gaussian random vectors - A.2 Detection in Gaussian noise - A.2.1 Scalar detection - A.2.2 Detection in a vector space - A.2.3 Detection in a complex vector space - Summary A.2 Vector detection in complex Gaussian noise - A.3 Estimation in Gaussian noise - A.3.1 Scalar estimation - A.3.2 Estimation in a vector space - A.3.3 Estimation in a complex vector space - Summary A.3 Mean square estimation in a complex vector space - A.4 Exercises
- Appendix B Information theory from first principles - B.1 Discrete memoryless channels - Example B.1 Binary symmetric channel - Example B.2 Binary erasure channel - B.2 Entropy, conditional entropy and mutual information - Example B.3 Binary entropy - B.3 Noisy channel coding theorem - B.3.1 Reliable communication and conditional entropy - B.3.2 A simple upper bound - B.3.3 Achieving the upper bound - Example B.4 Binary symmetric channel - Example B.5 Binary erasure channel - B.3.4 Operational interpretation - B.4 Formal derivation of AWGN capacity xiv Contents
- B.4.1 Analog memoryless channels
- B.4.2 Derivation of AWGN capacity - B.5 Sphere-packing interpretation
- B.5.1 Upper bound
- B.5.2 Achievability - B.6 Time-invariant parallel channel - B.7 Capacity of the fast fading channel
- B.7.1 Scalar fast fading channnel
- B.7.2 Fast fading MIMO channel - B.8 Outage formulation - B.9 Multiple access channel
- B.9.1 Capacity region
- B.9.2 Corner points of the capacity region
- B.9.3 Fast fading uplink
- B.10 Exercises - References - Index
xvi Preface
- (^) Chapter 2: basic properties of multipath wireless channels and their mod- eling (level 1).
- (^) Chapter 3: point-to-point communication techniques that increase reliability by exploiting time, frequency and spatial diversity (2).
- (^) Chapter 4: cellular system design via a case study of three systems, focusing on multiple access and interference management issues (3).
- (^) Chapter 5: point-to-point communication revisited from a more fundamental capacity point of view, culminating in the modern concept of opportunistic communication (2).
- (^) Chapter 6: multiuser capacity and opportunistic communication, and its application in a third-generation wireless data system (3).
- (^) Chapter 7: MIMO channel modeling (1).
- (^) Chapter 8: MIMO capacity and architectures (2).
- (^) Chapter 9: diversity–multiplexing tradeoff and space-time code design (2).
- (^) Chapter 10: MIMO in multiuser channels and cellular systems (3).
How to use this book
This book is written as a textbook for a first-year graduate course in wireless communication. The expected background is solid undergraduate/beginning graduate courses in signals and systems, probability and digital communica- tion. This background is supplemented by the two appendices in the book. Appendix A summarizes some basic facts in vector detection and estimation in Gaussian noise which are used repeatedly throughout the book. Appendix B covers the underlying information theory behind the channel capacity results used in this book. Even though information theory has played a significant role in many of the recent developments in wireless communication, in the main text we only introduce capacity results in a heuristic manner and use them mainly to motivate communication concepts and techniques. No back- ground in information theory is assumed. The appendix is intended for the reader who wants to have a more in-depth and unified understanding of the capacity results. At Berkeley and Urbana-Champaign, we have used earlier versions of this book to teach one-semester (15 weeks) wireless communication courses. We have been able to cover most of the materials in Chapters 1 through 8 and parts of 9 and 10. Depending on the background of the students and the time available, one can envision several other ways to structure a course around this book. Examples:
- (^) A senior level advanced undergraduate course in wireless communication: Chapters 2, 3, 4.
- (^) An advanced graduate course for students with background in wireless channels and systems: Chapters 3, 5, 6, 7, 8, 9, 10.
xvii Preface
- (^) A short (quarter) course focusing on MIMO and space-time coding: Chap- ters 3, 5, 7, 8, 9.
The more than 230 exercises form an integral part of the book. Working on at least some of them is essential in understanding the material. Most of them elaborate on concepts discussed in the main text. The exercises range from relatively straightforward derivations of results in the main text, to “back- of-envelope” calculations for actual wireless systems, to “get-your-hands- dirty” MATLAB types, and to reading exercises that point to current research literature. The small bibliographical notes at the end of each chapter provide pointers to literature that is very closely related to the material discussed in the book; we do not aim to exhaust the immense research literature related to the material covered here.
xix Acknowledgements
Rajiv Laroia have significantly influenced our view of the system aspects of wireless communication. Several of his ideas have found their way into the “system view” discussions in the book. Finally we would like to thank the National Science Foundation, whose continual support of our research led to this book.
Notation
Some specific sets Real numbers Complex numbers A subset of the users in the uplink of a cell
Scalars m Non-negative integer representing discrete-time L Number of diversity branches Scalar, indexing the diversity branches K Number of users N Block length Nc Number of tones in an OFDM system Tc Coherence time Td Delay spread W Bandwidth nt Number of transmit antennas nr Number of receive antennas nmin Minimum of number of transmit and receive antennas hm Scalar channel, complex valued, at time m h∗^ Complex conjugate of the complex valued scalar h xm Channel input, complex valued, at time m ym Channel output, complex valued, at time m ^2 Real Gaussian random variable with mean and variance ^2 0 ^2 Circularly symmetric complex Gaussian random variable: the real and imaginary parts are i.i.d. 0 ^2 / 2 N 0 Power spectral density of white Gaussian noise wm Additive Gaussian noise process, i.i.d. 0 N 0 with time m zm Additive colored Gaussian noise, at time m P Average power constraint measured in joules/symbol P^ ¯ Average power constraint measured in watts SNR Signal-to-noise ratio SINR Signal-to-interference-plus-noise ratio
xx
C H A P T E R
Introduction
1.1 Book objective
Wireless communication is one of the most vibrant areas in the commu- nication field today. While it has been a topic of study since the 1960s, the past decade has seen a surge of research activities in the area. This is due to a confluence of several factors. First, there has been an explosive increase in demand for tetherless connectivity, driven so far mainly by cellu- lar telephony but expected to be soon eclipsed by wireless data applications. Second, the dramatic progress in VLSI technology has enabled small-area and low-power implementation of sophisticated signal processing algorithms and coding techniques. Third, the success of second-generation (2G) digital wireless standards, in particular, the IS-95 Code Division Multiple Access (CDMA) standard, provides a concrete demonstration that good ideas from communication theory can have a significant impact in practice. The research thrust in the past decade has led to a much richer set of perspectives and tools on how to communicate over wireless channels, and the picture is still very much evolving. There are two fundamental aspects of wireless communication that make the problem challenging and interesting. These aspects are by and large not as significant in wireline communication. First is the phenomenon of fading: the time variation of the channel strengths due to the small-scale effect of multipath fading, as well as larger-scale effects such as path loss via dis- tance attenuation and shadowing by obstacles. Second, unlike in the wired world where each transmitter–receiver pair can often be thought of as an isolated point-to-point link, wireless users communicate over the air and there is significant interference between them. The interference can be between transmitters communicating with a common receiver (e.g., uplink of a cellu- lar system), between signals from a single transmitter to multiple receivers (e.g., downlink of a cellular system), or between different transmitter–receiver pairs (e.g., interference between users in different cells). How to deal with fad- ing and with interference is central to the design of wireless communication
1
3 1.2 Wireless systems
between wireless and wire technologies, and the choice often changes when new technologies become available. In this book, we will concentrate on cellular networks, both because they are of great current interest and also because the features of many other wireless systems can be easily understood as special cases or simple generalizations of the features of cellular networks. A cellular network consists of a large number of wireless subscribers who have cellular telephones (users), that can be used in cars, in buildings, on the street, or almost anywhere. There are also a number of fixed base-stations, arranged to provide coverage of the subscribers. The area covered by a base-station, i.e., the area from which incoming calls reach that base-station, is called a cell. One often pictures a cell as a hexagonal region with the base-station in the middle. One then pictures a city or region as being broken up into a hexagonal lattice of cells (see Figure 1.1a). In reality, the base-stations are placed somewhat irregularly, depending on the location of places such as building tops or hill tops that have good communication coverage and that can be leased or bought (see Figure 1.1b). Similarly, mobile users connected to a base-station are chosen by good communication paths rather than geographic distance. When a user makes a call, it is connected to the base-station to which it appears to have the best path (often but not always the closest base-station). The base-stations in a given area are then connected to a mobile telephone switching office (MTSO, also called a mobile switching center MSC) by high- speed wire connections or microwave links. The MTSO is connected to the public wired telephone network. Thus an incoming call from a mobile user is first connected to a base-station and from there to the MTSO and then to the wired network. From there the call goes to its destination, which might be an ordinary wire line telephone, or might be another mobile subscriber. Thus, we see that a cellular network is not an independent network, but rather an appendage to the wired network. The MTSO also plays a major role in coordinating which base-station will handle a call to or from a user and when to handoff a user from one base-station to another. When another user (either wired or wireless) places a call to a given user, the reverse process takes place. First the MTSO for the called subscriber is found,
Figure 1.1 Cells and base-stations for a cellular network. (a) An oversimplified view in which each cell is hexagonal. (b) A more realistic case where base-stations are irregularly placed and cell phones choose the best base-station. (a)^ (b)
4 Introduction
then the closest base-station is found, and finally the call is set up through the MTSO and the base-station. The wireless link from a base-station to the mobile users is interchangeably called the downlink or the forward channel, and the link from the users to a base-station is called the uplink or a reverse channel. There are usually many users connected to a single base-station, and thus, for the downlink channel, the base-station must multiplex together the signals to the various connected users and then broadcast one waveform from which each user can extract its own signal. For the uplink channel, each user connected to a given base-station transmits its own waveform, and the base-station receives the sum of the waveforms from the various users plus noise. The base-station must then separate out the signals from each user and forward these signals to the MTSO. Older cellular systems, such as the AMPS (advanced mobile phone service) system developed in the USA in the eighties, are analog. That is, a voice waveform is modulated on a carrier and transmitted without being trans- formed into a digital stream. Different users in the same cell are assigned different modulation frequencies, and adjacent cells use different sets of fre- quencies. Cells sufficiently far away from each other can reuse the same set of frequencies with little danger of interference. Second-generation cellular systems are digital. One is the GSM (global system for mobile communication) system, which was standardized in Europe but now used worldwide, another is the TDMA (time-division multiple access) standard developed in the USA (IS-136), and a third is CDMA (code division multiple access) (IS-95). Since these cellular systems, and their standards, were originally developed for telephony, the current data rates and delays in cellular systems are essentially determined by voice requirements. Third- generation cellular systems are designed to handle data and/or voice. While some of the third-generation systems are essentially evolution of second- generation voice systems, others are designed from scratch to cater for the specific characteristics of data. In addition to a requirement for higher rates, data applications have two features that distinguish them from voice:
- (^) Many data applications are extremely bursty; users may remain inactive for long periods of time but have very high demands for short periods of time. Voice applications, in contrast, have a fixed-rate demand over long periods of time.
- (^) Voice has a relatively tight latency requirement of the order of 100 ms. Data applications have a wide range of latency requirements; real-time applications, such as gaming, may have even tighter delay requirements than voice, while many others, such as http file transfers, have a much laxer requirement.
In the book we will see the impact of these features on the appropriate choice of communication techniques.
