
- •Contents
- •Preface
- •1 Spread spectrum signals and systems
- •1.1 Basic definition
- •1.2 Historical sketch
- •2 Classical reception problems and signal design
- •2.1 Gaussian channel, general reception problem and optimal decision rules
- •2.2 Binary data transmission (deterministic signals)
- •2.3 M-ary data transmission: deterministic signals
- •2.4 Complex envelope of a bandpass signal
- •2.5 M-ary data transmission: noncoherent signals
- •2.6 Trade-off between orthogonal-coding gain and bandwidth
- •2.7 Examples of orthogonal signal sets
- •2.7.1 Time-shift coding
- •2.7.2 Frequency-shift coding
- •2.7.3 Spread spectrum orthogonal coding
- •2.8 Signal parameter estimation
- •2.8.1 Problem statement and estimation rule
- •2.8.2 Estimation accuracy
- •2.9 Amplitude estimation
- •2.10 Phase estimation
- •2.11 Autocorrelation function and matched filter response
- •2.12 Estimation of the bandpass signal time delay
- •2.12.1 Estimation algorithm
- •2.12.2 Estimation accuracy
- •2.13 Estimation of carrier frequency
- •2.14 Simultaneous estimation of time delay and frequency
- •2.15 Signal resolution
- •2.16 Summary
- •Problems
- •Matlab-based problems
- •3 Merits of spread spectrum
- •3.1 Jamming immunity
- •3.1.1 Narrowband jammer
- •3.1.2 Barrage jammer
- •3.2 Low probability of detection
- •3.3 Signal structure secrecy
- •3.4 Electromagnetic compatibility
- •3.5 Propagation effects in wireless systems
- •3.5.1 Free-space propagation
- •3.5.2 Shadowing
- •3.5.3 Multipath fading
- •3.5.4 Performance analysis
- •3.6 Diversity
- •3.6.1 Combining modes
- •3.6.2 Arranging diversity branches
- •3.7 Multipath diversity and RAKE receiver
- •Problems
- •Matlab-based problems
- •4 Multiuser environment: code division multiple access
- •4.1 Multiuser systems and the multiple access problem
- •4.2 Frequency division multiple access
- •4.3 Time division multiple access
- •4.4 Synchronous code division multiple access
- •4.5 Asynchronous CDMA
- •4.6 Asynchronous CDMA in the cellular networks
- •4.6.1 The resource reuse problem and cellular systems
- •4.6.2 Number of users per cell in asynchronous CDMA
- •Problems
- •Matlab-based problems
- •5 Discrete spread spectrum signals
- •5.1 Spread spectrum modulation
- •5.2 General model and categorization of discrete signals
- •5.3 Correlation functions of APSK signals
- •5.4 Calculating correlation functions of code sequences
- •5.5 Correlation functions of FSK signals
- •5.6 Processing gain of discrete signals
- •Problems
- •Matlab-based problems
- •6 Spread spectrum signals for time measurement, synchronization and time-resolution
- •6.1 Demands on ACF: revisited
- •6.2 Signals with continuous frequency modulation
- •6.3 Criterion of good aperiodic ACF of APSK signals
- •6.4 Optimization of aperiodic PSK signals
- •6.5 Perfect periodic ACF: minimax binary sequences
- •6.6 Initial knowledge on finite fields and linear sequences
- •6.6.1 Definition of a finite field
- •6.6.2 Linear sequences over finite fields
- •6.6.3 m-sequences
- •6.7 Periodic ACF of m-sequences
- •6.8 More about finite fields
- •6.9 Legendre sequences
- •6.10 Binary codes with good aperiodic ACF: revisited
- •6.11 Sequences with perfect periodic ACF
- •6.11.1 Binary non-antipodal sequences
- •6.11.2 Polyphase codes
- •6.11.3 Ternary sequences
- •6.12 Suppression of sidelobes along the delay axis
- •6.12.1 Sidelobe suppression filter
- •6.12.2 SNR loss calculation
- •6.13 FSK signals with optimal aperiodic ACF
- •Problems
- •Matlab-based problems
- •7 Spread spectrum signature ensembles for CDMA applications
- •7.1 Data transmission via spread spectrum
- •7.1.1 Direct sequence spreading: BPSK data modulation and binary signatures
- •7.1.2 DS spreading: general case
- •7.1.3 Frequency hopping spreading
- •7.2 Designing signature ensembles for synchronous DS CDMA
- •7.2.1 Problem formulation
- •7.2.2 Optimizing signature sets in minimum distance
- •7.2.3 Welch-bound sequences
- •7.3 Approaches to designing signature ensembles for asynchronous DS CDMA
- •7.4 Time-offset signatures for asynchronous CDMA
- •7.5 Examples of minimax signature ensembles
- •7.5.1 Frequency-offset binary m-sequences
- •7.5.2 Gold sets
- •7.5.3 Kasami sets and their extensions
- •7.5.4 Kamaletdinov ensembles
- •Problems
- •Matlab-based problems
- •8 DS spread spectrum signal acquisition and tracking
- •8.1 Acquisition and tracking procedures
- •8.2 Serial search
- •8.2.1 Algorithm model
- •8.2.2 Probability of correct acquisition and average number of steps
- •8.2.3 Minimizing average acquisition time
- •8.3 Acquisition acceleration techniques
- •8.3.1 Problem statement
- •8.3.2 Sequential cell examining
- •8.3.3 Serial-parallel search
- •8.3.4 Rapid acquisition sequences
- •8.4 Code tracking
- •8.4.1 Delay estimation by tracking
- •8.4.2 Early–late DLL discriminators
- •8.4.3 DLL noise performance
- •Problems
- •Matlab-based problems
- •9 Channel coding in spread spectrum systems
- •9.1 Preliminary notes and terminology
- •9.2 Error-detecting block codes
- •9.2.1 Binary block codes and detection capability
- •9.2.2 Linear codes and their polynomial representation
- •9.2.3 Syndrome calculation and error detection
- •9.2.4 Choice of generator polynomials for CRC
- •9.3 Convolutional codes
- •9.3.1 Convolutional encoder
- •9.3.2 Trellis diagram, free distance and asymptotic coding gain
- •9.3.3 The Viterbi decoding algorithm
- •9.3.4 Applications
- •9.4 Turbo codes
- •9.4.1 Turbo encoders
- •9.4.2 Iterative decoding
- •9.4.3 Performance
- •9.4.4 Applications
- •9.5 Channel interleaving
- •Problems
- •Matlab-based problems
- •10 Some advancements in spread spectrum systems development
- •10.1 Multiuser reception and suppressing MAI
- •10.1.1 Optimal (ML) multiuser rule for synchronous CDMA
- •10.1.2 Decorrelating algorithm
- •10.1.3 Minimum mean-square error detection
- •10.1.4 Blind MMSE detector
- •10.1.5 Interference cancellation
- •10.1.6 Asynchronous multiuser detectors
- •10.2 Multicarrier modulation and OFDM
- •10.2.1 Multicarrier DS CDMA
- •10.2.2 Conventional MC transmission and OFDM
- •10.2.3 Multicarrier CDMA
- •10.2.4 Applications
- •10.3 Transmit diversity and space–time coding in CDMA systems
- •10.3.1 Transmit diversity and the space–time coding problem
- •10.3.2 Efficiency of transmit diversity
- •10.3.3 Time-switched space–time code
- •10.3.4 Alamouti space–time code
- •10.3.5 Transmit diversity in spread spectrum applications
- •Problems
- •Matlab-based problems
- •11 Examples of operational wireless spread spectrum systems
- •11.1 Preliminary remarks
- •11.2 Global positioning system
- •11.2.1 General system principles and architecture
- •11.2.2 GPS ranging signals
- •11.2.3 Signal processing
- •11.2.4 Accuracy
- •11.2.5 GLONASS and GNSS
- •11.2.6 Applications
- •11.3 Air interfaces cdmaOne (IS-95) and cdma2000
- •11.3.1 Introductory remarks
- •11.3.2 Spreading codes of IS-95
- •11.3.3 Forward link channels of IS-95
- •11.3.3.1 Pilot channel
- •11.3.3.2 Synchronization channel
- •11.3.3.3 Paging channels
- •11.3.3.4 Traffic channels
- •11.3.3.5 Forward link modulation
- •11.3.3.6 MS processing of forward link signal
- •11.3.4 Reverse link of IS-95
- •11.3.4.1 Reverse link traffic channel
- •11.3.4.2 Access channel
- •11.3.4.3 Reverse link modulation
- •11.3.5 Evolution of air interface cdmaOne to cdma2000
- •11.4 Air interface UMTS
- •11.4.1 Preliminaries
- •11.4.2 Types of UMTS channels
- •11.4.3 Dedicated physical uplink channels
- •11.4.4 Common physical uplink channels
- •11.4.5 Uplink channelization codes
- •11.4.6 Uplink scrambling
- •11.4.7 Mapping downlink transport channels to physical channels
- •11.4.8 Downlink physical channels format
- •11.4.9 Downlink channelization codes
- •11.4.10 Downlink scrambling codes
- •11.4.11 Synchronization channel
- •11.4.11.1 General structure
- •11.4.11.2 Primary synchronization code
- •11.4.11.3 Secondary synchronization code
- •References
- •Index

Operational wireless spread spectrum systems |
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The accuracy of GLONASS is of the same level as that of GPS. Both systems are now considered as cooperative, entering the integrated Global Navigation Satellite System (GNSS). It has already been emphasized that increasing the number of processed satellite signals improves the positioning precision, so joint use of both constellations is obviously profitable. In addition, scenarios are not rare, where some satellites over the horizon are obstructed (e.g. by an aircraft wing), so that the total number of available ranging signals within only GPS or GLONASS is not sufficient for positioning. Then again, joint processing of GPS and GLONASS signals may give a considerable gain in positioning integrity. A great number of receiver models presently on the market or in development are capable of combined processing of signals of both systems.
11.2.6 Applications
The role of satellite-based global navigation systems in the modern and future world can hardly be overestimated. Just simply naming the areas of their involvement forms rather a long list, including traditional navigation of ships, aircraft and terrestrial moving objects (cars, trucks etc.), transit systems, mapping utilities (e.g. pipelines), monitoring forestry and natural resources, farming, civil engineering, geodetic surveying, seismic forecasting, airborne mapping, seafloor investigations and many more. Not being able to go deeper into this fascinating topic, we direct the interested reader to the sources [117–119] and their references.
11.3 Air interfaces cdmaOne (IS-95) and cdma2000
11.3.1 Introductory remarks
The first interim specifications of the 2G CDMA cellular telephone of standard IS-95 (presently referred to as cdmaOne, too) were published in 1993–1995, and the operational phase of IS-95 networks started in 1996. Nowadays networks of this standard cover huge territories serving tens of millions of consumers. Its impressive commercial success, widely recognized high quality of service and openness to further modernizations were among the decisive factors favouring the CDMA philosophy as the basic platform for the next generations of mobile radio (3G and beyond).
Initially IS-95 was meant to gradually replace (maintaining compatibility with) an American analog standard, AMPS, operating in the 800 MHz range. The IS-95 documents set up frequency division separation of forward (869–894 MHz) and reverse (824– 849 MHz) links,1 while no limitation on frequency reuse in neighbouring cells or sectors was stipulated. The nominal bandwidth of the IS-95 signal is about 1.25 MHz, so that within the total assigned 25 MHz band an operator has remarkable freedom in carrier selection and frequency planning of the network. All the BSs entering a network are strictly synchronized via GPS to operate in a unified time scale, allowing MS easier
1 The terms ‘forward’ and ‘reverse’ links are synonyms of downlink (BS to MS) and uplink (MS to BS) adopted in the cdmaOne and cdma2000 specifications.
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Spread Spectrum and CDMA |
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switching from one BS to another (handover). IS-95 and its 3G evolution cdma2000 are typical DS spread spectrum systems, which clearly manifest all the benefits of this technology. They also possess very high educational value, since they demonstrate in a lucid form practical ways of realizing many ideas studied above. In the text to follow we are going to dwell on only the most general principles of spreading, channelization, coding and modulation in the IS-95 and cdma2000 air interfaces. Readers who wish to acquire deeper knowledge may consult the sources [18,69,83,120,121] and many others.
11.3.2 Spreading codes of IS-95
The spreading sequences used in the IS-95 standard were partly mentioned in examples earlier. They are designed to provide CDMA separation of physical channels, distinguishability of signals of different BS arriving at the MS receiver and privacy of the transmitted data. Synchronous CDMA multiplexing of physical channels of the forward link served by a fixed BS is realized on the basis of Walsh sequences (see Section 2.7.3) of length N ¼ 64. The orthogonality of Walsh sequences allows separating the corresponding 64 physical channels theoretically with no MAI. The duration of a chip of Walsh sequences is nearly 0:81 ms and the chip rate is 1.2288 Mcps (megachip per second), resulting in the abovementioned bandwidth of 1.25 MHz. Certainly, the number of forward-link physical channels thus implemented is 64 and, consistent with the CDMA principle, they occupy the same common bandwidth with no frequency or time offset. All of the base stations use the same set of 64 Walsh functions, and the spreading by so-called short codes makes the signals of different base stations separable from each other in the MS receiver. There are two different basic binary short-code pseudonoise sequences, PN-I and PN-Q, used in the in-phase and quadrature-phase branches of the BS modulator, respectively. They are primarily generated as two m-sequences whose LFSR generators (see Section 6.6) contain 15 flip-flops and are defined by
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the |
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fQ(x) ¼ x |
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PN-Q. The sequences |
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obtained have length L ¼ 2 |
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1, but to come to the short codes PN-I and PN-Q they |
are extended by one more zero symbol following after 14 consecutive zeros. This brings the lengths to N ¼ L þ 1 ¼ 215 ¼ 32 768 chips, and with the same chip rate as for Walsh codes there are 37.5 periods of the short codes per second or 75 periods over two seconds. To discriminate between different base stations every one of them employs its BS-specific time-offset replica of the basic short-code sequences. There are 512 such pairs of replicas, every pair being shifted compared to the previous one by 64 chips or about 52 ms. The network planning should assign short-code pairs to the base stations in a way guaranteeing low risk of any MS receiving a signal from an unintended BS, whose timing due to propagation delay is about the same as that of the desired signal and whose strength is sufficient for mixing them up. It should be stressed that relative timeoffsets between the base stations entering a network, once set up, remain constant forever, since all the BS use GPS receivers to synchronize their clock oscillators with each other.
One more spreading code is a long code, generated primarily as a binary m-sequence of memory 42. According to the specification a primitive polynomial of the LSFR