
- •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

7
Spread spectrum signature ensembles for CDMA applications
7.1 Data transmission via spread spectrum
It is clear from the discussion of Sections 4.3–4.6 that in a CDMA network each of K users transmits or receives its individual data employing some user-specific signature, ensemble of K signatures being carefully designed to provide the best possible compatibility. In order to make the kth signature transport the datastream some sort of modulation is necessary, which—due to the spread spectrum nature of CDMA signa- tures—is often called spread spectrum modulation. There are two classical versions: direct sequence (DS) and frequency hopping (FH) modulation. The first is more typical of modern commercial wireless multiuser applications, and so the second will be considered below only briefly.
7.1.1Direct sequence spreading: BPSK data modulation and binary signatures
The general idea of direct spread spectrum is APSK modulation of the APSK signature by a datastream. To make the concept easier to grasp, let us start with the simplest case of BPSK non-spread spectrum data transmission. Let Bk(t) be the data waveform of the kth user (Figure 7.1) where positive and negative polarities during one bit interval Tb correspond to transmitting a bit equal to 0 and 1, respectively. If bk ¼ ( . . . , bk, 1, bk, 0, bk, 1, . . . ), bk, i ¼ 1, is, as it was in Chapter 4, the kth user bit (or binary symbol) stream, then Bk(t) ¼ bk, i ¼ 1, (i 1)Tb < t iTb. Transmitting Bk(t) by
Spread Spectrum and CDMA: Principles and Applications Valery P. Ipatov
2005 John Wiley & Sons, Ltd

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Figure 7.1 Illustration of BPSK data transmission
BPSK just means multiplying it with a CW carrier of frequency f0 to come to a sent modulated signal (see Figure 7.1):
skðt; bkÞ ¼ BkðtÞ cosð2 f0tÞ |
ð7:1Þ |
Therefore, technically a BPSK modulator is just the multiplier shown in Figure 7.2a. After passing through the channel the signal assumes time delay k and initial phase k as well as attenuation, the latter being ignored as immaterial in our current study. Then the received useful signal:
skrðt; bkÞ ¼ Bkðt kÞ cosð2 f0t þ kÞ |
ð7:2Þ |
A typical receiver of BPSK data contains timing and carrier phase recovery loops, which estimate running values of delay k and initial phase k. At the moment the issue of estimation precision may be left aside, and we assume that the receiver knows ‘genuine’k and k. If the signal above is corrupted by AWGN, the optimal (ML) procedure (see Section 2.2) to retrieve the ith transmitted bit is to calculate the correlation of the observation y(t) ¼ skr(t; bk) þ n(t) with the difference of signals carrying bit contents 0 and 1, respectively, which in the considered case is just 2 cos (2 f0t þ k). Since only polarity of the correlation is used for the decision on the received bit, and since the ith
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∫
Sampling at iTb + τk
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Figure 7.2 BPSK modulator (a) and demodulator (b)
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bit at the channel output occupies time interval ((i 1)Tb þ k, iTb þ k], the correlation discussed is:
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and the decision bk, i ¼ 0 or bk, i ¼ 1 is taken depending on the positive or negative sign
of zk. A possible and very popular structure of a demodulator implementing this rule is given in Figure 7.2b. It contains the correlator realized as a multiplier multiplying the observation with a locally generated CW reference cos (2 f0t þ k) and an integration- and-reset unit. At the end of every consecutive bit interval a sample is taken from the integrator output, a decision on the current bit is made according to its polarity, and the integrator is zeroed in preparation for operation over the next bit interval.
Consider now the changes that need to be done for transmitting BPSK data with BPSK DS spreading. Let sk(t) be the kth user signature, i.e. a discrete signal consisting of chips of duration D, manipulated by some user-specific binary sequence. Let there be N signature chips per one data bit. Then DS spreading of the BPSK signal just involves
inserting one more multiplication in (7.1)—by a signature sk(t): |
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skðt; bkÞ ¼ skðtÞBkðtÞ cosð2 f0tÞ |
ð7:3Þ |
Since the bandwidths of signals (7.1) and (7.3) are inverse to bit duration Tb ¼ 1/R and chip duration D ¼ Tb/N ¼ 1/RN, respectively, the DS spreading widens the spectrum N times. This explains one more name: the spreading factor for the time–frequency product or processing gain WT ¼ N. In practice, multiplications in (7.3) may be fulfilled in an arbitrary order, e.g. as Figures 7.3 (spreading by a binary m-sequence of length N ¼ 7, Tb ¼ ND) and 7.5a show, the bit stream Bk(t) may first be multiplied with a signature sk(t) to further modulate the CW carrier by the product sk(t)Bk(t). We may say in this case that the bit stream first modulates the baseband signature and then the result BPSK-modulates the carrier.
After passing the channel and acquiring delay k and phase k, the signal takes the form:
skrðt; bkÞ ¼ skðt kÞBkðt kÞ cosð2 f0t þ kÞ |
ð7:4Þ |
Assuming again a perfect knowledge of parameters k, k, the receiver for retrieving the current (ith) bit just needs to distinguish between the signal sk(t k) cos (2 f0t þ k) and its antipodal copy. To perform it optimally a correlation:
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sk(t k) cos (2 f0t þ k) may be found and its polarity used for the decision. Interestingly, however, the same optimal operation may be realized in two stages, first removing

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Figure 7.3 DS spreading of BPSK data with binary signature
the spreading and then demodulating the data as though they had been transmitted directly with no spreading. Let the observation y(t) be multiplied by the local replica sk(t k) of the baseband signature synchronized accurately with the arriving signal. The useful component (7.4) of the observation after this operation changes as:
skrðt; bkÞskðt kÞ ¼ s2kðt kÞBkðt kÞ cosð2 f0t þ kÞ ¼ Bkðt kÞ cosð2 f0t þ kÞ
where the binary nature of the signature (sk(t) ¼ 1) is used, on the strength of which s2k(t) ¼ 1. As is seen, after this step the received signal has no more features of spread spectrum, coinciding entirely with the plain signal (7.2) BPSK-modulated by the datastream. Due to this, multiplying of the observation by a signature replica is called despreading. Figure 7.4 shows the procedure of transforming a DS-spread signal into a conventional BPSK data-modulated signal.
Since a despread signal is a conventional BPSK data-modulated CW carrier, further data recovery is fulfilled by an ordinary BPSK demodulator, e.g. by the one of Figure 7.2b. The entire spreading–despreading cycle is illustrated in Figure 7.5.
To support the discussion in terms of the frequency domain, consider Figure 7.6,
~ ~
which shows the power spectra densities Sb(f ), Sbs(f ) of the initial datastream Bk(t) and its spread version sk(t)Bk(t), respectively. For a sequence Bk(t) of bit-pulses of duration
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whose polarities are random and independent, the power spectrum |
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( fTb). Treating the spread datastream again as a random sequence of |
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(f ) ¼ Tbsinc |
pulses with independent polarities—this time of duration D—leads to a power spectrum
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shape, but occupying N times wider bandwidth: |
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on the |
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utilizes all the benefits of spread spectrum (see Chapters 3 and 4) but at the receiving end despreading returns the spectrum into its original bandwidth, converting the signal into narrowband and allowing use of the simplest technologies of data demodulation.