
- •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

Merits of spread spectrum |
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and duration reduces the signal power spectrum density, hiding it under the background spectrum of the natural thermal noise.
Example 3.2.1. Consider the system transmitting sporadically and rather infrequently one of 64 messages using orthogonal signals. To provide error probability no worse than 10 3 it needs to use SNR around 7 dB per one bit, or 15 dB per 6-bit message (see Figure 2.9). Thus q2 is 15 dB and transforming the interceptor’s (voltage!) SNR (3.9) into decibels we have:
ðqi ÞdB ¼ 2ðq2ÞdB 20 lg 2 10 lg WT
If the system employs spread spectrum signals with processing gain WT ¼ 1000 the interceptor’s SNR turns to be (qi )dB ¼ 6 dB or qi ¼ 1/2, which is not at all sufficient for reliable detection of the intended system’s presence on the air in one session. If, for instance, the interceptor tolerates a probability of false alarm of pf ¼ 10 3 then according to (3.7) the detection probability pd 5 10 3, i.e. is extremely small and exposes no serious threat to the intended system.
Finishing this section, note that the discussed advantage of spread spectrum is widely utilized today not only by the military or special services. The fact that a spread spectrum signal may be practically unnoticeable for the equipment that monitors the state of the radio air has serious implications for licensing policy. In particular, the range of commercial systems that may actively operate on the air without applying for a licence becomes broader, and in some regions special spectral zones are currently allocated for such licence-free use.
3.3 Signal structure secrecy
Continuing the line of the previous section, let us remember once more that the only reason for an interceptor to resort to such an ineffective detection instrument as an energy receiver is lack of information about the structure of the detected signal, i.e. its modulation law. As a result, the interceptor cannot process the signal in the manner used by the intended receiver (matched filtering). Of course, if the signal structure is not complicated enough and the interceptor is aware that it was chosen from only a few alternatives he may try them all. Appropriate equipment for doing so may be a bank of parallel matched filters or a single filter (several filters) reconfigured to fit the candidate signal structures serially in time, if the signal is known to be received for an adequate duration. Therefore, another aspect of the strategy of the intended system in its conflict with an interceptor consists in making a signal structure practically unbreakable.
A similar task is characteristic of military or commercial systems that do not tend to make the fact of their operation a mystery, e.g. if they function continuously, but are very keen to avoid unauthorized access to services addressed only to classified consumers, or forging of the transmitted information. The satellite-based global navigation system GPS is a convincing example of this kind. It has two positioning channels (see Section 11.2): open (or clear access, abbreviated C/A) and special (or protected, P). The signal transmitted over the second channel allows super-high precision of positioning, and the US government, which runs the system, does not permit unconditional access

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Spread Spectrum and CDMA |
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to this channel. In order to protect it from unauthorized use some special measures are undertaken concerning the signal modulation.
In disciplines dealing with information security, the extent of data protection is measured by a number of competitive equiprobable keys, which an enemy cryptanalyst (eavesdropper) should try to crack the ciphertext, i.e. encrypted data. In application to the signal structure, each of those keys is just a modulation law, which is typically repeated with some period T. Suppose that a signal is built of chips (see the example in Section 2.7.3) on the basis of an M-ary alphabet, i.e. using M different symbols to manipulate chips. If the bandwidth allocated to the system is W then the total signal space has dimension measured as WT (ignoring bandpass doubling; see Sections 2.3–2.5), i.e. a modulation law may be thought of as being constructed of WT chips. It is clear, then, that MWT is the total number of possible modulation laws, i.e. competitive keys, and the system designer concerned with secrecy of the modulation format in the developed system should employ signals with rather large processing gain WT.
Example 3.3.1. The signal of the P-channel (P-code) in GPS is binary (M ¼ 2) with the bandwidth W 10 MHz. Its structure is quite regular and repeated with the period T ¼ 7 days. Being hidden under the thermal noise, this signal cannot be retrieved by symbol-wise reception and only knowledge of its fine structure permits it to be cleared with highest efficiency off the AWGN. To prevent an unauthorized interceptor from accessing the P-code, a secret binary key (W-code) is modulo 2 added to it, masking the structure of the resulting Y-code. A single symbol of the W-code spans 20 symbols of the P-code; therefore, to break this mask by a trial and error method, up to 2WT /20 alternatives should be tested. Since WT ¼ 7 86 400 107 > 1012, the number of tried keys is greater than 2 to the power of ten billion, which is fully beyond any imagination. For this reason the Y-code is believed to be unbreakable and no reports have emerged in nearly 10 years of its history on any successful cryptanalytical attack on it.
We conclude the section with another declaration on the advantages of spread spectrum: this technology is very conducive to cryptographic protection of a signal structure.
3.4 Electromagnetic compatibility
The problem of electromagnetic compatibility (EMC) is one of the most topical in modern wireless engineering. EMC implies friendly co-existence of different systems on the air despite each of them receiving not only its proper signal but also the signals of the other systems. Certainly, it is impossible to root out entirely mutual disturbance when several systems are operating simultaneously within a relatively small area. Any active system, i.e. emitting electromagnetic waves, inevitably affects all neighbouring ones and a system designer should try to minimize this potentially harmful influence.
There are two parties playing the EMC game. The first, which may be called ‘emanating’, tries to minimize the interference created by its emitted power to other nearby, so-called ‘susceptible’, systems [15]. The motivation for this is not only ethical