
- •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

Time measurement, synchronization and time-resolution |
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6.5 Perfect periodic ACF: minimax binary sequences
Interest in sequences with good periodic ACF is not exhausted by their role as the basic material for designing good aperiodic sequences. Many applications exploit periodic discrete signals (CW-radar, navigation, pilot and synchronization channels of mobile radio etc.), making periodic ACF critically important for system performance. We will address as ‘perfect’ a periodic ACF which has only zero sidelobes, i.e. values between the periodic mainlobes repeating with the period N. Using normalized notation, we write this condition as:
pðmÞ ¼ E |
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aiai m |
¼ |
( |
0; m |
¼ 0modN |
ð6:6Þ |
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1 N 1 |
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1; m |
0modN |
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X |
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6¼ |
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i¼0 |
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where the congruence m ¼ 0modN is read as m is divisible by N (is multiple of N). It is obvious that for the perfect ACF p, max ¼ 0. Figure 6.9 shows the ACF of a discrete baseband signal with rectangular chips, manipulated by the code with perfect periodic ACF. The practical benefits of perfect ACF are obvious from Figure 6.10, where
.
Rp(τ)
τ
∆ |
N∆ |
N∆ |
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Figure 6.9 Perfect periodic ACF |
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a0 |
a0 |
a0 |
(a)
t
∆
a0 |
N∆ |
a0 |
a0 |
(b)
t
τ
(c)
t
Figure 6.10 Resolution of signal replicas; perfect periodic ACF

160 |
Spread Spectrum and CDMA |
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waveforms (a) and (b) show two time-shifted copies of the same bandpass periodic signal whose code ACF meets (6.6). When the superposition of these signals arrives at the input of the filter matched to a one-period segment of the signal, two time-shifted replicas of the signal ACF are observed at the output. If the time delay between signal copies is greater than ACF mainlobe duration 2D (but smaller than (N 2)D), the filter responses to both signals are entirely resolved with no corruption of each other (Figure 6.10c).
Let us investigate non-normalized periodic ACF of a binary sequence composed of elements f 1g:
N 1
X
RpðmÞ ¼ aiai m |
ð6:7Þ |
i¼0 |
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where the conjugation asterisk is not required, since all ai ¼ 1. Summing both parts of (6.7) over the range m ¼ 0, 1, . . . , N 1 produces:
N 1 |
N 1 N 1 |
N 1 |
N 1 |
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X |
X X |
X |
X |
ð6:8Þ |
RpðmÞ ¼ |
aiai m ¼ ai |
ai m ¼ja~0j2 |
||
m¼0 |
m¼0 i¼0 |
i¼0 |
m¼0 |
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with: |
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N 1 |
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a~0 ¼ |
X |
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ai |
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i¼0
being the constant component (imbalance) of code sequence fa0, a1, . . . , aN 1g. Since the constant component of a binary sequence may take on only integer values, the sum in (6.8) is a squared integer. Suppose now that a binary code has perfect periodic ACF.
Then Rp(0) ¼ N 1 |
(ai)2 |
¼N and Rp(m) ¼ 0, m ¼ 1, 2, . . . , N 1, giving: |
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P |
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i¼0 |
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N 1 |
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X |
ð6:9Þ |
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RpðmÞ ¼ N ¼ ja~0j2 |
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m¼0 |
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Assuming m 6¼0modN, let Ne and Nd be the numbers of products aiai m in the sum of (6.7) equalling þ1 and 1, respectively. Then Rp(m) ¼ Ne Nd ¼ 0 means Ne ¼ Nd and N ¼ Ne þ Nd ¼ 2Ne. Thus, according to (6.9) and the last result, length N is an even squared integer, i.e. the necessary condition for obtaining perfect ACF for a binary sequence is N ¼ 4h2, where h is integer. All of these lengths (4, 16, 36, 64, . . . ) were investigated in the early 1960s by Turin, who proved that the only binary code2 with perfect periodic PACF of length N 12 100 is a trivial one of length 4: þ1þ1þ1 1 [28]. Later, the nonexistence of such sequences was proved up to lengths N < 4 1652 ¼ 108 900 [29]. Their existence beyond this range looks quite improbable.
2 We do not consider as new (and it is universally adopted) sequences obtained from an initial one by a cyclic shift, mirror imaging or changing signs of all elements. These transforms do not change periodic ACF (Problem 5.5), and sequences obtainable from each other in this way are treated as trivially different or equivalent.