
- •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
Operational wireless spread spectrum systems |
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ionospheric error compensation described above, the P-code (DS-modulated by the data bit stream similarly to C/A-code) is transmitted on both frequencies L1 and L2, quadrature multiplexing of C/A and P signals being used on L1 with 3 dB stronger C/ A-signal. In its turn, the L2 intensity is 3 dB lower than that of L1. The structure of the P-code is described in open GPS documents. It is formed as a symbol-wise modulo 2 sum of two very long binary sequences differing in length by 37 chips. The resulting period of the sequence thus formed is around 266 days. The non-overlapping 7-day (6:187104 1012 chips) segments of this sequence are used as P-codes for different satellites. The USA Department of Defense commissioned the designers of GPS to make provision for strict limitation of access to the P-code, reckoning that unauthorized usage of it may be hazardous to national security. Encryption of the P-code is realized by its modulo 2 summation with a masking or key W-code, whose structure is secret. The resulting Y-code possesses excellent cracking resistance (see Example 3.3.1).
11.2.3 Signal processing
The basic operations of a single-frequency (L1) GPS receiver are very conventional for any DS spread spectrum system. After a coarse acquisition of a satellite C/A-signal (see Sections 8.2 and 8.3), aided when possible by a priori knowledge of satellite locations, the code delay-lock loop (Section 8.4) is locked and starts to output a sequence of estimations of a satellite pseudo-range. Typically, modern GPS receivers include a set of channels processing in parallel the C/A-signals of all visible satellites. On finishing the search for the last used satellite, the receiver is ready to produce the user’s coordinates, which is a steady-state process lasting for as long as the user wishes.
The authorized receiver repeats the same operations for the P-codes of both carriers, spending only a little time on searching the signals, since the data frame available from the L1 signal contains a special handover word which facilitates setting the local generator of the P-code to an appropriate initial state.
In many modern GPS receivers these basic operations are supplemented or replaced by a variety of others pursuing improvements of accuracy, speeding up of the initial fixing time, consumer convenience etc. For example, additional accuracy may be gained by measuring pseudo-ranges via integration of the carrier frequency of the received signal. The instantaneous Doppler frequency shift is proportional to the radial speed of the satellite relative to a user. Hence, the integral of the Doppler frequency over some period is proportional to variation of the satellite–user distance over this time interval. Having started from the point with precisely known coordinates, the receiver may further position itself via integrals of instantaneous frequencies of visible satellites, i.e. their current accumulated ranges. Moreover, methods of ambiguity resolution exist, making possible positioning through frequency integrals even without initialization at a known point [117,118].
Another hugely popular operational technique is so-called differential or relative positioning, the idea of which is as follows. Let one GPS receiver be set up at the reference site (base) with precisely known coordinates. Then comparing pre-computed satellite ranges with the measured ones, the base receiver can find systematic errors (biases) inserted by system imperfection. Let another receiver be placed at a remote
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point with unknown coordinates. If the baseline, i.e. the distance between the base and remote receivers, is not very long (e.g. within tens of kilometres) the systematic errors at the base and remote sites are strongly correlated, so the remote receiver may subtract biases estimated by a base receiver from measured ranges, improving their accuracy. Of course, such system modification should contain a communication link providing delivery of base-receiver data to the remote receivers. A vast number of reference sites are now arranged all over the world, transmitting differential corrections via FM stations, broadcasting satellites, radio beacons, cellular radio, Internet etc. [117,118].
11.2.4 Accuracy
The originally planned precision of C/A-code GPS positioning was set up around 100 m in the horizontal and 156 m in the vertical directions, the probability of keeping errors within these limits being 95%. Analogous figures for the P-code fixing were 16 m and 23 m, respectively. However, numerous advanced receiver structures developed by manufacturers have exhibited much better precision even without involving the P-code. This became a matter of anxiety for the US institutions responsible for national security, and in 1990 a selective availability mode was introduced, distorting satellite-transmitted ephemeris and timing and thereby deliberately corrupting positioning accuracy. During the subsequent decade, however, differential navigation, which eliminates these types of errors almost entirely, gained great popularity, so the selective availability mode turned out to be pointless in practice and was terminated in 2000. Nowadays a wide spectrum of offers is characteristic of the GPS equipment market, with proclaimed accuracies ranging from tens of metres to several millimetres and better.
11.2.5 GLONASS and GNSS
The Russian space-based navigation system GLONASS has many common features with GPS. Its space segment consists of 24 satellites located in 3 nearly circular orbits with nominal sidereal period 11 hours 15 minutes and 64.8 inclination to the equator. Again, two frequencies L1 and L2 (respectively in the 1.5 and 1.2 GHz bands) are used to provide ionospheric correction, with C/A-code transmitted on L1 and P-code transmitted on both carriers. Current ephemeris and other relevant data encoded by Hamming code and properly arranged into subframes and frames are superimposed onto ranging codes in a DS manner and transmitted by satellites at the rate 50 bps. A control segment provides continuous monitoring of satellites, computation/prediction of their orbit parameters and uploading them to the satellite onboard memory.
The substantial difference between GLONASS and GPS is that all satellites transmit the same C/A-code, which is a binary m-sequence of length N ¼ 511 with real-time period 1 ms. Distinguishing individual satellite signals is possible due to the small mutual carrier offsets between them, transforming the common C/A code into an ensemble of frequency-offset replicas of the m-sequence, as described in Section 7.5.1. In order to save bandwidth antipodal satellites of the same orbit (which are never seen by a user simultaneously) employ the same frequency offset.