//INTEGRAS/KCG/PAGINATION/WILEY/SSPA/FINALS_17-02-05/PRELIMS.3D – 3 – [1–12/12] 18.2.2005 5:15PM
Spread Spectrum and CDMA Principles and Applications
Valery P. Ipatov University of Turku, Finland and St. Petersburg Electrotechnical University ‘LETI’, Russia //INTEGRAS/KCG/PAGINATION/WILEY/SSPA/FINALS_17-02-05/PRELIMS.3D – 2 – [1–12/12] 18.2.2005 5:15PM //INTEGRAS/KCG/PAGINATION/WILEY/SSPA/FINALS_17-02-05/PRELIMS.3D – 1 – [1–12/12] 18.2.2005 5:15PM
Spread Spectrum and CDMA //INTEGRAS/KCG/PAGINATION/WILEY/SSPA/FINALS_17-02-05/PRELIMS.3D – 2 – [1–12/12] 18.2.2005 5:15PM //INTEGRAS/KCG/PAGINATION/WILEY/SSPA/FINALS_17-02-05/PRELIMS.3D – 3 – [1–12/12] 18.2.2005 5:15PM
Spread Spectrum and CDMA Principles and Applications
Valery P. Ipatov University of Turku, Finland and St. Petersburg Electrotechnical University ‘LETI’, Russia //INTEGRAS/KCG/PAGINATION/WILEY/SSPA/FINALS_17-02-05/PRELIMS.3D – 4 – [1–12/12] 18.2.2005 5:15PM
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Contents
Preface xi
1 Spread spectrum signals and systems 1 1.1 Basic definition 1 1.2 Historical sketch 5
2 Classical reception problems and signal design 7 2.1 Gaussian channel, general reception problem and optimal decision rules 7 2.2 Binary data transmission (deterministic signals) 11 2.3 M-ary data transmission: deterministic signals 17 2.4 Complex envelope of a bandpass signal 23 2.5 M-ary data transmission: noncoherent signals 26 2.6 Trade-off between orthogonal-coding gain and bandwidth 28 2.7 Examples of orthogonal signal sets 31 2.7.1 Time-shift coding 31 2.7.2 Frequency-shift coding 33 2.7.3 Spread spectrum orthogonal coding 33 2.8 Signal parameter estimation 37 2.8.1 Problem statement and estimation rule 37 2.8.2 Estimation accuracy 39 2.9 Amplitude estimation 41 2.10 Phase estimation 43 2.11 Autocorrelation function and matched filter response 43 2.12 Estimation of the bandpass signal time delay 46 2.12.1 Estimation algorithm 46 2.12.2 Estimation accuracy 48 2.13 Estimation of carrier frequency 53 2.14 Simultaneous estimation of time delay and frequency 55 2.15 Signal resolution 58 2.16 Summary 61 Problems 62 Matlab-based problems 68
3 Merits of spread spectrum 77 3.1 Jamming immunity 77 3.1.1 Narrowband jammer 78 3.1.2 Barrage jammer 80 //INTEGRAS/KCG/PAGINATION/WILEY/SSPA/FINALS_17-02-05/PRELIMS.3D – 6 – [1–12/12] 19.2.2005 12:52PM
vi Contents
3.2 Low probability of detection 82 3.3 Signal structure secrecy 87 3.4 Electromagnetic compatibility 88 3.5 Propagation effects in wireless systems 89 3.5.1 Free-space propagation 90 3.5.2 Shadowing 90 3.5.3 Multipath fading 91 3.5.4 Performance analysis 95 3.6 Diversity 98 3.6.1 Combining modes 98 3.6.2 Arranging diversity branches 100 3.7 Multipath diversity and RAKE receiver 102 Problems 106 Matlab-based problems 109
4 Multiuser environment: code division multiple access 115 4.1 Multiuser systems and the multiple access problem 115 4.2 Frequency division multiple access 117 4.3 Time division multiple access 118 4.4 Synchronous code division multiple access 119 4.5 Asynchronous CDMA 121 4.6 Asynchronous CDMA in the cellular networks 124 4.6.1 The resource reuse problem and cellular systems 124 4.6.2 Number of users per cell in asynchronous CDMA 125 Problems 129 Matlab-based problems 130
5 Discrete spread spectrum signals 135 5.1 Spread spectrum modulation 135 5.2 General model and categorization of discrete signals 136 5.3 Correlation functions of APSK signals 137 5.4 Calculating correlation functions of code sequences 139 5.5 Correlation functions of FSK signals 142 5.6 Processing gain of discrete signals 145 Problems 145 Matlab-based problems 146
6 Spread spectrum signals for time measurement, synchronization and time-resolution 149 6.1 Demands on ACF: revisited 149 6.2 Signals with continuous frequency modulation 151 6.3 Criterion of good aperiodic ACF of APSK signals 154 6.4 Optimization of aperiodic PSK signals 155 6.5 Perfect periodic ACF: minimax binary sequences 159 6.6 Initial knowledge on finite fields and linear sequences 161 6.6.1 Definition of a finite field 161 6.6.2 Linear sequences over finite fields 163 6.6.3 m-sequences 165 6.7 Periodic ACF of m-sequences 167 6.8 More about finite fields 170 //INTEGRAS/KCG/PAGINATION/WILEY/SSPA/FINALS_17-02-05/PRELIMS.3D – 7 – [1–12/12] 19.2.2005 12:52PM
Contents vii
6.9 Legendre sequences 172 6.10 Binary codes with good aperiodic ACF: revisited 174 6.11 Sequences with perfect periodic ACF 176 6.11.1 Binary non-antipodal sequences 177 6.11.2 Polyphase codes 179 6.11.3 Ternary sequences 181 6.12 Suppression of sidelobes along the delay axis 185 6.12.1 Sidelobe suppression filter 186 6.12.2 SNR loss calculation 187 6.13 FSK signals with optimal aperiodic ACF 192 Problems 194 Matlab-based problems 196
7 Spread spectrum signature ensembles for CDMA applications 203 7.1 Data transmission via spread spectrum 203 7.1.1 Direct sequence spreading: BPSK data modulation and binary signatures 203 7.1.2 DS spreading: general case 207 7.1.3 Frequency hopping spreading 212 7.2 Designing signature ensembles for synchronous DS CDMA 214 7.2.1 Problem formulation 214 7.2.2 Optimizing signature sets in minimum distance 215 7.2.3 Welch-bound sequences 223 7.3 Approaches to designing signature ensembles for asynchronous DS CDMA 227 7.4 Time-offset signatures for asynchronous CDMA 232 7.5 Examples of minimax signature ensembles 235 7.5.1 Frequency-offset binary m-sequences 235 7.5.2 Gold sets 236 7.5.3 Kasami sets and their extensions 239 7.5.4 Kamaletdinov ensembles 241 Problems 243 Matlab-based problems 246
8 DS spread spectrum signal acquisition and tracking 251 8.1 Acquisition and tracking procedures 251 8.2 Serial search 253 8.2.1 Algorithm model 253 8.2.2 Probability of correct acquisition and average number of steps 254 8.2.3 Minimizing average acquisition time 258 8.3 Acquisition acceleration techniques 261 8.3.1 Problem statement 261 8.3.2 Sequential cell examining 262 8.3.3 Serial-parallel search 263 8.3.4 Rapid acquisition sequences 264 8.4 Code tracking 265 8.4.1 Delay estimation by tracking 265 8.4.2 Early–late DLL discriminators 267 8.4.3 DLL noise performance 270 Problems 273 Matlab-based problems 274 //INTEGRAS/KCG/PAGINATION/WILEY/SSPA/FINALS_17-02-05/PRELIMS.3D – 8 – [1–12/12] 19.2.2005 12:52PM
viii Contents
9 Channel coding in spread spectrum systems 277 9.1 Preliminary notes and terminology 277 9.2 Error-detecting block codes 279 9.2.1 Binary block codes and detection capability 279 9.2.2 Linear codes and their polynomial representation 281 9.2.3 Syndrome calculation and error detection 284 9.2.4 Choice of generator polynomials for CRC 285 9.3 Convolutional codes 286 9.3.1 Convolutional encoder 286 9.3.2 Trellis diagram, free distance and asymptotic coding gain 289 9.3.3 The Viterbi decoding algorithm 292 9.3.4 Applications 296 9.4 Turbo codes 296 9.4.1 Turbo encoders 296 9.4.2 Iterative decoding 299 9.4.3 Performance 300 9.4.4 Applications 301 9.5 Channel interleaving 302 Problems 302 Matlab-based problems 304
10 Some advancements in spread spectrum systems development 307 10.1 Multiuser reception and suppressing MAI 307 10.1.1 Optimal (ML) multiuser rule for synchronous CDMA 307 10.1.2 Decorrelating algorithm 309 10.1.3 Minimum mean-square error detection 311 10.1.4 Blind MMSE detector 314 10.1.5 Interference cancellation 315 10.1.6 Asynchronous multiuser detectors 316 10.2 Multicarrier modulation and OFDM 316 10.2.1 Multicarrier DS CDMA 317 10.2.2 Conventional MC transmission and OFDM 318 10.2.3 Multicarrier CDMA 322 10.2.4 Applications 325 10.3 Transmit diversity and space–time coding in CDMA systems 326 10.3.1 Transmit diversity and the space–time coding problem 326 10.3.2 Efficiency of transmit diversity 327 10.3.3 Time-switched space–time code 329 10.3.4 Alamouti space–time code 331 10.3.5 Transmit diversity in spread spectrum applications 333 Problems 334 Matlab-based problems 336
11 Examples of operational wireless spread spectrum systems 339 11.1 Preliminary remarks 339 11.2 Global positioning system 339 11.2.1 General system principles and architecture 340 11.2.2 GPS ranging signals 341 11.2.3 Signal processing 343 //INTEGRAS/KCG/PAGINATION/WILEY/SSPA/FINALS_17-02-05/PRELIMS.3D – 9 – [1–12/12] 19.2.2005 12:52PM
Contents ix
11.2.4 Accuracy 344 11.2.5 GLONASS and GNSS 344 11.2.6 Applications 345 11.3 Air interfaces cdmaOne (IS-95) and cdma2000 345 11.3.1 Introductory remarks 345 11.3.2 Spreading codes of IS-95 346 11.3.3 Forward link channels of IS-95 347 11.3.3.1 Pilot channel 347 11.3.3.2 Synchronization channel 347 11.3.3.3 Paging channels 348 11.3.3.4 Traffic channels 349 11.3.3.5 Forward link modulation 351 11.3.3.6 MS processing of forward link signal 352 11.3.4 Reverse link of IS-95 353 11.3.4.1 Reverse link traffic channel 353 11.3.4.2 Access channel 355 11.3.4.3 Reverse link modulation 355 11.3.5 Evolution of air interface cdmaOne to cdma2000 356 11.4 Air interface UMTS 357 11.4.1 Preliminaries 357 11.4.2 Types of UMTS channels 358 11.4.3 Dedicated physical uplink channels 359 11.4.4 Common physical uplink channels 360 11.4.5 Uplink channelization codes 361 11.4.6 Uplink scrambling 362 11.4.7 Mapping downlink transport channels to physical channels 363 11.4.8 Downlink physical channels format 364 11.4.9 Downlink channelization codes 365 11.4.10 Downlink scrambling codes 365 11.4.11 Synchronization channel 366 11.4.11.1 General structure 366 11.4.11.2 Primary synchronization code 366 11.4.11.3 Secondary synchronization code 367
References 369
Index 375 //INTEGRAS/KCG/PAGINATION/WILEY/SSPA/FINALS_17-02-05/PRELIMS.3D – 10 – [1–12/12] 19.2.2005 12:52PM //INTEGRAS/KCG/PAGINATION/WILEY/SSPA/FINALS_17-02-05/PRELIMS.3D – 11 – [1–12/12] 18.2.2005 5:15PM
Preface
Spread spectrum and CDMA (code division multiple access) are up-to-date technologies widely used in operational radar, navigation and telecommunication systems and play- ing a dominant role in the philosophy of the forthcoming generations of systems and networks. The amount of interest and effort invested in this encouraging area by research institutions and industry is gigantic and constantly growing, especially after the prominent commercial success of CDMA mobile telephone IS-95 and the use of CDMA as the basic platform of 3G (and beyond) mobile radio. No wonder that the fundamentals of spread spectrum theory have assumed a solid place in the basic university disciplines, while the detailed issues form the contents of numerous advanced courses. This book was conceived as a textbook for postgraduate and undergraduate students, and is also expected to be useful in training industry personnel and in the daily work of researchers. It is based on the experience and knowledge gained by the author during more than three decades of research activity in the area, as well as on his lecture courses. The original version of such a course started in the late 1970s at the Saint Petersburg Electrotechnical University ‘LETI’ and has since been continually developed and mod- ernized, absorbing many state-of-the-art achievements and being presented to audiences from Russia, the UK, Australia, China, Finland and other countries. The intention of the author in preparing this book was to present the key ideas of spread spectrum in the most general form equally applicable to both systems of collect- ing and recovering information (such as radar and navigation) and telecommunication systems or networks. The author’s second concern was to link the material as tightly as possible to classical signal and communication theory, which gives Chapter 2 a special role. The goal pursued everywhere was harmony between mathematical rigour and physical transparency of some or other issue under discussion and the reader’s deep understanding of the reasons underlying the preference for spread spectrum and CDMA. The main question the author tried to answer in considering this or that problem was ‘Why?’—i.e. why a designer may or should prefer one solution over others. A particular emphasis of the book is designing spread spectrum signals. Many popular books, although deservedly reputable, do not go into this problem beyond presenting a brief survey of m-sequences and Gold codes. A reader may thereby get a false idea that nothing valuable exists outside this narrow range of attractive signal families. In Chapters 6 and 7 we try to show that the designer’s freedom and the //INTEGRAS/KCG/PAGINATION/WILEY/SSPA/FINALS_17-02-05/PRELIMS.3D – 12 – [1–12/12] 18.2.2005 5:15PM
xii Preface
multitude of alternatives are much broader and comprise many solutions potentially competitive or clearly superior to those mentioned above. In no way is this book intended to be looked upon as a manual introducing concrete operational or projected systems and standards. However, some such systems give a rich soil to illustrate the theory and for this reason are frequently mentioned in the text as examples of practical realization of spread spectrum principles. Another aid for better adoption of the contents is offered by the problems at the end of every theoretical chapter. Especially recommended are the Matlab-based problems, since their running involves and develops investigatory skills and allows execution of an extensive experi- mental study. The book is supported by the companion website on which instructors and lecturers can find a solutions manual for the problems and matlab programming within the book, electronic versions of some of the figures and other useful resources such as a list of abbreviations etc. Please go to ftp://ftp.wiley.co.uk/pub/books/ipatov. If you have any comments regarding the book please feel free to contact the author directly at [email protected]. The author is sceptical enough to realize that no book—including this one—can be totally free of shortcomings. In our case the difficulties were greatly intensified by the necessity of writing in a non-mother tongue. Nevertheless, the author is entirely respon- sible for all of the statements as well as the drawbacks of the book and is ready to accept any constructive remarks or criticism. I would like to express my sincere gratitude to the Department of Information Technology of the University of Turku for the friendly and creative atmosphere during my work in Finland. I address my special appreciation to Professor Jouni Isoaho and Dr Esa Tjukanoff for their daily support and cooperation. Many thanks to my colleagues Dr Nastooh Avessta and Dr Igor Samoilov, who kindly and carefully read the manuscript and, by way of innumerable discussions, helped in my endeavour to streamline it. The assistance of Jarkko Paavola and Alexey Dudkov in rectifying and debugging the manuscript can hardly be overestimated, too. This is a good opportunity to emphasize my deepest gratitude to my dear teachers Professor Yu. A. Kolomensky, Professor Yu. M. Kazarinov and Professor Yu. D. Ulianitsky, who introduced me to the fascinating world of signals and noise, and were for decades my advisors in many professional as well as personal matters. Warmest thanks to all my colleagues at the Department of Radio Systems of Saint Petersburg State Electrotechnical University ‘LETI’ for a long-standing collaboration. I bring my gratitude also to Sarah Hinton and her colleagues at John Wiley & Sons, Ltd for initiating this project and inspiring me in the course of writing, and my special thanks to the Nokia Foundation for the grant awarded to me at the final stage of preparing the manuscript. And finally I cannot help mentioning my wife’s patience and care during the year of my working on this book.
Valery P. Ipatov //INTEGRAS/KCG/PAGINATION/WILEY/SSPA/FINALS_17-02-05/C01.3D – 1 – [1–6/6] 18.2.2005 5:15PM
1
Spread spectrum signals and systems
1.1 Basic definition The term spread spectrum is today one of the most popular in the radio engineering and communication community. At the same time, it may appear difficult to formulate an unequivocal and precise definition distinctively separating the spread spectrum philosophy from a ‘non-spread spectrum’ one. Certainly, every expert in system design and every experienced researcher has an intuitive understanding of the core of the issue, but—unlike a newcomer—such a person does not need to think about definitions in order to respond successfully to his or her professional challenges. From the point of view of the target audience of the book it seems worthwhile to dedicate some space to elaborating an appropriate explanation of what is implied in the following text under the spread spectrum concept. Let us start with a reminder of the basics of spectral analysis. Every signal s(t) of finite energy can be synthesized as a sum of an uncountable number of harmonics whose amplitudes and phases within the infinitesimal frequency range [f , f þ df ] are determined by a spectral density or spectrum ss~(f ). It is the pair of inverse and direct Fourier transforms that expresses this fact mathematically:
Z1 Z1 sðtÞ¼ ss~ðf Þ expðj2 ftÞ df ss~ðf Þ¼ sðtÞ expð j2 ftÞ dt ð1:1Þ 1 1
Due to the one-to-one correspondence between the signal representation in the time domain s(t) and in the frequency domain ss~(f ), we are able to switch arbitrarily between these two tools, selecting the more convenient one for any specific task. To characterize the size of the zones occupied by signal energy in the time and frequency domains we use the notions of signal duration T and bandwidth W, respectively. A signal whose energy
Spread Spectrum and CDMA: Principles and Applications Valery P. Ipatov Ó 2005 John Wiley & Sons, Ltd //INTEGRAS/KCG/PAGINATION/WILEY/SSPA/FINALS_17-02-05/C01.3D – 2 – [1–6/6] 18.2.2005 5:15PM
2 Spread Spectrum and CDMA
is concentrated within strictly limited space in the time domain cannot have finite (i.e. non-zero in only limited frequency interval) spectrum and vice versa. Because of this, to define at least one of the parameters T, W, or both, some agreement is necessary about what is meant by duration or bandwidth. In this way effective, root mean square, etc. duration and bandwidth came into existence, showing the size of a zone spanned by a substantial part of signal energy in the time and frequency domains, respectively [1]. It is absolutely obvious that one way or another, the word ‘spread’ is indicative of wide spectrum, i.e. broad bandwidth W of a signal. But against what is the spectrum wide? Where is the reference for comparison? To demonstrate how a definition of spread spectrum may provoke debate, let us consult with several excellent and world-renowned books. A rather frequent way to explain the concept consists in the statement that a system or a signal is of spread spectrum type if its bandwidth significantly exceeds the minimum bandwidth necessary to send the information [1–6]. What may seem mentally problematic in this definition is the very idea of minimum bandwidth of information or message. According to the fundamental Shannon’s bound, spectral efficiency (the ratio between the data rate R and the signal bandwidth W ) of a communication system operating over the Gaussian channel obeys the inequality: