
- •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

176 Spread Spectrum and CDMA
Nevertheless they remain promising from the point of view of a, max, since the lower border (6.4) gives a, max 1:5/257. Applying to them the above procedure results in the sequence with maximal non-normalized aperiodic sidelobe equal to 12, i.e. a, max ¼ 12/257 or 26:6 dB (compare this with the longest binary Barker code, for which a, max ¼ 1/13 or 22:3 dB). The sequence obtained after eliminating the last symbol turns into the code of length N ¼ 256 with the same maximal non-normalized sidelobe and a, max ¼ 12/256 ¼ 3/64, i.e. again approximately 26:6 dB. Its aperiodic ACF is shown in Figure 6.16a. Interestingly, in the 3G mobile UMTS standard the primary synchronization code is a binary sequence of this very length, N ¼ 256, having aperiodic sidelobes up to 1/4 (Figure 6.16b), i.e. much higher as compared to the sequence just found. On the other hand, the choice of a code for a cell search in UMTS was subject to many other requirements, including implementation issues which might have overpowered the criterion of good autocorrelation.
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Figure 6.16 Aperiodic ACF of two binary codes of length 256: the code of Example 6.10.2 (a) and primary synchronization code of UMTS (b)
Figure 6.17 presents one more illustration of the optimization of binary codes in the maximal aperiodic ACF sidelobe, showing the dependence of a, max on length N for
presumably the best binary sequences taken from [25–27,34]. The dashed line shows the p p
curve a, max 0:77/ N approximating the dependence a, max ¼ f (N) as a/ N with a fitted by the least-squares method. As is seen, the accuracy of this approximation is rather good, especially for N > 100.
6.11 Sequences with perfect periodic ACF
As has been indicated time and again, numerous applications exist where the periodicity of the signals makes their periodic correlation properties primarily important. In other words, good periodic ACFs are not only a powerful intermediate tool to design good aperiodic sequences but very valuable in themselves. Examples of this sort include

Time measurement, synchronization and time-resolution |
177 |
Romax
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Figure 6.17 Dependence of the minimized maximal aperiodic sidelobe on length
continuous wave ranging systems, in particular in remote space, pilot or synchronization channels for digital data transmission systems (downlink pilot channels of cdmaOne and cdma2000, secondary synchronization channel of UMTS), CW radar and sonar systems etc.
Despite binary f 1g minimax sequences look rather practicable, having maximal periodic sidelobe p, max ¼ 1/N dropping with length, situations are still likely where an acceptable value of p, max requires an infeasibly long length N. For example, for radar, ranging or sonar systems time-resolution of signals in the dynamic range over 80 dB is not an unusual demand. To meet it with optimal binary sequences, lengths exceeding 104 are necessary, which may unreasonably slow down the initial searching procedure (see Section 8.2). Certainly, perfect periodic ACF (6.6) would be the best option for many such scenarios. However, it cannot be realized among binary codes, which are, clearly, the most attractive technologically. In the rest of this chapter we will inspect possible ways of getting perfect periodic ACF when the sequence alphabet is not rigorously limited to just binary symbols f 1g.
6.11.1 Binary non-antipodal sequences
Replacing the antipodal alphabet fþ1, 1g by some binary non-antipodal one, it proves to be possible to turn all periodic sidelobes of any binary minimax sequence meeting (6.12) into zero. The simplest way to derive an appropriate alphabet is by adding a constant c (complex in the general case) to the original fþ1, 1g sequence

178 |
Spread Spectrum and CDMA |
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a0, a1, . . . , aN 1, converting symbols þ1 and 1 to 1 þ c and 1 þ c, respectively. The periodic ACF of the sequence thus obtained is found directly:
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a0, a1, . . . , aN 1. Equation (6.8) shows that for any minimax sequence meeting (6.12)
PN 1 Rp(m) ¼ N þ (N 1)( 1) ¼ 1 ) a~0 ¼ 1. Since changing the signs of all
m¼0
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This equation in two real unknowns (real and imaginary parts of c) has an infinite number of solutions. Let us find those that are potentially most interesting. If a real alphabet is desired Re(c) ¼ c and jcj2 ¼ c2 so (6.29) is a quadratic equation:
c2 N2 c N1 ¼ 0
with roots c1, 2 ¼ 1 Nþ1. New binary non-antipodal symbols 1 þ c and 1 þ c may now
N
be divided by 1 þ c to retain þ1 as one of the symbols in the new alphabet. After this we come to the rule of converting a binary minimax sequence with periodic ACF (6.12) into one with perfect ACF: elements 1 should be changed to:
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Example 6.11.1. The m-sequence or Legendre sequence of length N ¼ 127 is transformed into
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The solution above produces an alphabet with two opposite symbols of unequal magnitude, i.e. results in amplitude modulation (Figure 6.18a). Another possible option
is a PSK non-antipodal alphabet. To come to it take a ‘pure’ imaginary c ¼ jc1. Then |
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Time measurement, synchronization and time-resolution |
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Figure 6.18 Non-antipodal binary alphabets
Example 6.11.2. For N ¼ 127 cos F ¼ 63/64 and F ¼ arccos (63/64) 10 803000. Changing all negative elements of the binary m-sequence or Legendre sequence of length N ¼ 127 toexp (jF) produces a sequence with perfect periodic ACF.
The alphabet transformation just discussed, which has been proposed and reopened repeatedly [38,39], can hardly be recognized as very effective practically. As is seen and confirmed by examples, it prescribes rather exotic values of code complex amplitudes, the setting and holding of which with adequate precision may appear technologically infeasible.
6.11.2 Polyphase codes
Involving non-binary PSK modulation with M > 2 opens the way to numerous polyphase sequences with perfect periodic ACF. There are various rules for their construction, but more or less all of them originate in two of the most popular algorithms. The first, corresponding to Chu (or quadratic residue) codes, is very straightforward and approximates in a discrete form the law of linear frequency modulation (cf. Section 6.2). The Chu code exists for an arbitrary length N and is generated as:
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It is easy to check that ai ¼ aiþN for all i and therefore, N is at least a multiple of the code period. Calculation of periodic ACF will in passing eventually clarify the issue of a
period. For the code of even length non-normalized periodic ACF: |
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Spread Spectrum and CDMA |
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When m ¼ 0 modN the last sum equals N, while the coefficient in front of it turns into 1. For any other m exp (j2 im/N) depends on i, and the sum above is a sum of the roots of unity of some degree, or, equivalently, a geometric series with the common ratio exp (j2 m/N). Summation of the series gives:
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The denominator of the last fraction never turns into zero unless m ¼ 0 modN, and therefore Rp(m) ¼ 0 at all shifts but multiples of N. Hence, the Chu code defined by the first row in (6.30) has period N and perfect periodic ACF. The solution for an odd N is carried out similarly (Problem 6.29).
Despite Chu codes making a rather convincing academic example of PSK sequences with perfect periodic ACF, their practical feasibility is pretty doubtful, since the size of the phase alphabet grows linearly with length and distances between adjacent phases becomes very small. Because of that excessive demands arise towards the precision of forming code symbols, the fineness of representing a phase, susceptibility to environmental conditions, etc.
The same shortcomings are characteristic (to a slightly lower extent, though) of the second popular family of polyphase sequences: Frank codes. They also realize stepapproximation of the linear frequency modulation, but much more roughly, and exist only for lengths that are squares of integers N ¼ h2 ¼ 4, 9, 16, 25, 36, 49, . . . . Their generation rule is:
ai ¼ exp j2 i i ; i ¼ . . . ; 1; 0; 1; . . . ð6:31Þ h h
where, as usual, bxc stands for rounding non-negative x towards zero.
Proof of perfection of periodic correlation properties of Frank codes differs from that
above only in minor details and is left for Problem 6.30. As is seen from a comparison of p
(6.31) and (6.30) the phase step of Frank codes is reduced N times, so the alphabet size grows with N markedly slower.
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þ1, þ1, þ1, þj, 1, j, þ1, 1, þ1, 1, þ1, j, 1, þj. Perfection of its periodic ACF may be tested by a direct computation.