
- •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

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The conclusion we just arrived at explains why so many efforts have been dedicated to searching for ensembles whose characteristics approach those of the hypothetical ensembles mentioned above when length L grows. Quite a popular criterion of this approximation is the minimax one, orienting the ensemble design towards minimizing maximum value among all unwanted correlations. Define the correlation peak max as the greater of two entities: the maximal autocorrelation sidelobe amax among all sequences and the maximal cross-correlation peak cmax among all pairs of sequences:
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Naturally, for the hypothetical perfect ensemble, max along with 2 is zero, and for any real ensemble max may serve as an adequate measure of its proximity to the perfect one.
Since the maximal value of any variable can never be smaller than its average,
2max 2, which spreads the Welch bounds (7.34) and (7.35) on the correlation peak:
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where, again, the last approximation corresponds to the case K 1. With additional limitations on the PSK alphabet, the bound above may appear rather loose, especially when the number of sequences approaches L. In particular, for sufficiently large ensembles of binary f 1g sequences the Sidelnikov bound holds [67,68]:
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Ensembles having max attaining the limit predicted by the lower bounds are certainly optimal in the correlation peak criterion and are sometimes called minimax. Some of them are discussed in Section 7.5.
7.4 Time-offset signatures for asynchronous CDMA
In many real situations mutual time shifts of asynchronous signatures may vary only within a restricted range. The finiteness of a channel delay spread on the one hand, and system geometry on the other are the most typical factors setting such limitations. To be specific, let us turn to the uplink of a cellular mobile radio. The local clock of an active MS is synchronized with the received BS signal and has a delay 1 versus the BS clock determined by the distance D from BS to MS as 1 ¼ D/c, where c is the speed of light. Since the signal transmitted by a specific MS reaches the BS receiver with the same delay, the total delay of the signal arriving at BS versus the BS clock is 2 ¼ 2 1 ¼ 2D/c. Let Dmax be the maximal distance, the signal from which has intensity perceptible by the BS receiver. Strong path attenuation (see Section 4.6) permits signals arriving from distances markedly exceeding the cell radius Dc to be ignored, which gives a rough estimation Dmax Dc. Then the maximal value of 2 is 2Dc/c and signals from mobiles at distances from BS ranging between zero and Dc arrive at BS within the time window

Spread spectrum signature ensembles |
233 |
[0,2Dc/c]. Besides, multipath replicas of signals are also present, so that a complete extension max of the window spanning the delays of all multipath signals increases by the channel delay spread ds: max ¼ 2Dc/c þ ds, where ds may be maximized over all possible locations of MS. Figure 7.16 helps to show the details of these deliberations. The signal of some specific MS may have an advance as well as a delay compared to some other, and all multipath replicas of any MS signal are potentially usable by a BS receiver (RAKE processing; see Section 3.7). Therefore, the entire range of possible mutual time shifts between any multipath replicas of any signatures proves to be
[ max, max], max ¼ 2Dc/c þ ds.
Certainly, in circumstances like these, one should take care to observe the second and
third conditions (7.43) within the range of only really likely values of m. Let us denote
thecontents of max in the number of chips rounded upwards as mmax: mmax ¼ maxD .
Then the range of m, where (7.43) should be obeyed, is [ mmax, mmax]. Now take a sequence fa1, ig of the period L K(mmax þ 1) and use as K signatures its cyclic replicas offset from each other by mmax þ 1 positions:
ak;i ¼ a1;i ðk 1Þðmmaxþ1Þ; k ¼ 1; 2; . . . ; K; i ¼ . . . ; 1; 0; 1; . . . ;
as is shown in Figure 7.17. Evidently all correlations between signatures thus arranged will be expressed in terms of ACF 11(m) of the initial sequence fa1, ig. Evaluating the CCF of the kth and lth signatures results in:
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Suppose now that the initial sequence fa1, ig has either perfect or good enough periodic ACF 11(m). The former is possible, e.g. for ternary or polyphase sequences (see Section 6.11), while any minimax binary sequence (Sections 6.7 and 6.9) may serve as an example of the latter. The idea is that all sidelobes of 11(m) are negligible. Then with jmj mmax the argument in the square brackets of (7.47) turns into zero modulo L only for the case k ¼ l and m ¼ 0modL, corresponding to the mainlobe of the ACF of the kth signature. For any other combination of k,l,m, the right-hand side of (7.47) gives
Multipath replicas of signal
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Figure 7.16 Variations of time of arrival of MS signal at the BS

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Spread Spectrum and CDMA |
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Figure 7.17 Signatures formed as time-shifted copies of the initial one
a sidelobe of 11(m), whose level was assumed to be negligible. We have thus proved that properly offset copies of the initial sequence with good periodic ACF produce the ensemble where conditions of pseudorandomness (7.43) hold within the full range of possible mutual signature shifts jmj mmax. It follows immediately that this ensemble achieves (when 11(m) is perfect) or approaches very closely the lowest level (7.40) of average unwanted effects due to MAI and multipath propagation or, equivalently, the Welch bounds (7.36) or (7.35). We again emphasize strongly the validity of this statement in the presence of DS data modulation of signatures, since conditions (7.43) are sufficient to minimize unwanted MAI/multipath effects in this case (see the remark following (7.40)).2
Example 7.4.1. Consider the system with chip duration D ¼ 1 ms, number of users K ¼ 60, channel delay spread ds ¼ 20 ms and cell radius Dc ¼ 15 km. In this case max ¼ 2Dc /c þ ds ¼ 120 ms and mmax ¼ 120. The signature ensemble may be arranged starting with the initial sequence fa1, i g whose period L K (mmax þ 1) ¼ 60 121 ¼ 7260. Since fa1, i g should have a good periodic ACF the relevant candidates may be the ternary perfect ACF sequence of length L ¼ 8011, a binary m-sequence (L ¼ 213 1 ¼ 8191) or a Legendre sequence (L ¼ 7283). The 60 signatures are then just 60 cyclic replicas of the fa1, i g offset from each other by 121 chips. Clearly, there is no upper limit on the length of the sequence and it may be advisable to take it longer with an appropriate increase in signature offset to secure some safety margin.
The uplinks of the 2G cdmaOne (IS-95) and 3G cdma2000 standards present very good examples of implementation of this version of asynchronous CDMA [69]. A binary m-sequence of an extremely long length L ¼ 242 1 extended by one symbol is used as the initial one and the user-specific signatures of all mobiles are just its relevant cyclic replicas. Pseudonoise properties of an m-sequence along with signature offsets exceeding possible variations of time of arrival of signal at the BS receiver guarantee a minimal
2 Without DS data modulation, perfection of periodic ACF of the initial sequence secures zero level of both MAI and multipath interference for any m mmax in the described signature construction.