Nevio Benvenuto, Giovanni Cherubini, Stefano Tomasin

Algorithms for Communications Systems and their Applications (eBook, ePUB)

• Format: ePub

Produktbeschreibung

The definitive guide to problem-solving in the design of communications systems

In Algorithms for Communications Systems and their Applications, 2nd Edition, authors Benvenuto, Cherubini, and Tomasin have delivered the ultimate and practical guide to applying algorithms in communications systems. Written for researchers and professionals in the areas of digital communications, signal processing, and computer engineering, Algorithms for Communications Systems presents algorithmic and computational procedures within communications systems that overcome a wide range of problems facing system designers.

New material in this fully updated edition includes:

• MIMO systems (Space-time block coding/Spatial multiplexing /Beamforming and interference management/Channel Estimation)
• OFDM and SC-FDMA (Synchronization/Resource allocation (bit and power loading)/Filtered OFDM)
• Improved radio channel model (Doppler and shadowing/mmWave)
• Polar codes (including practical decoding methods)
• 5G systems (New Radio architecture/initial access for mmWave/physical channels)

The book retains the essential coding and signal processing theoretical and operative elements expected from a classic text, further adopting the new radio of 5G systems as a case study to create the definitive guide to modern communications systems.

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Produktdetails

• Verlag: John Wiley & Sons
• Erscheinungstermin: 12. Januar 2021
• Englisch
• ISBN-13: 9781119567981
• Artikelnr.: 60938651

Autorenporträt

Nevio Benvenuto, Professor, DEI-Telecommunications Group, University of Padua, Italy. Nevio received his Ph.D. in engineering from the University of Massachusetts, Amherst, in 1983.

Giovanni Cherubini, IBM Research Zurich, Switzerland. Giovanni Cherubini received M.S. and Ph.D. degrees from the University of California, San Diego, in 1984 and 1986, respectively, all in Electrical Engineering.

Stefano Tomasin, Associate Professor, Department of Information Engineering, University of Padova, Italy. Stefano received the Ph.D. degree in Telecommunications Engineering from the University of Padova, Italy, in 2003.

Inhaltsangabe

Preface 3

Acknowledgments 3

1 Elements of signal theory 7

1.1 Continuous-time linear systems 7

1.2 Discrete-time linear systems 10

Discrete Fourier transform 13

The DFT operator 14

Circular and linear convolution via DFT 15

Convolution by the overlap-save method 17

IIR and FIR filters 19

1.3 Signal bandwidth 22

The sampling theorem 24

Heaviside conditions for the absence of signal distortion 26

1.4 Passband signals and systems 26

Complex representation 26

Relation between a signal and its complex representation 28

Baseband equivalent of a transformation 36

Envelope and instantaneous phase and frequency 37

1.5 Second-order analysis of random processes 38

1.5.1 Correlation 39

Properties of the autocorrelation function 40

1.5.2 Power spectral density 40

Spectral lines in the PSD 40

Cross power spectral density 42

Properties of the PSD 42

PSD through filtering 43

1.5.3 PSD of discrete-time random processes 43

Spectral lines in the PSD 44

PSD through filtering 45

Minimum-phase spectral factorization 46

1.5.4 PSD of passband processes 47

PSD of in-phase and quadrature components 47

Cyclostationary processes 50

1.6 The autocorrelation matrix 56

Properties 56

Eigenvalues 56

Other properties 57

Eigenvalue analysis for Hermitian matrices 58

1.7 Examples of random processes 60

1.8 Matched filter 66

White noise case 68

1.9 Ergodic random processes 69

1.9.1 Mean value estimators 71

Rectangular window 74

Exponential filter 74

General window 75

1.9.2 Correlation estimators 75

Unbiased estimate 76

Biased estimate 76

1.9.3 Power spectral density estimators 77

Periodogram or instantaneous spectrum 77

Welch periodogram 78

Blackman and Tukey correlogram 79

Windowing and window closing 79

1.10 Parametric models of random processes 82

ARMA 82

MA 84

AR 84

Spectral factorization of AR models 87

Whitening filter 87

Relation between ARMA, MA, and AR models 87

1.10.1 Autocorrelation of AR processes 89

1.10.2 Spectral estimation of an AR process 91

Some useful relations 92

AR model of sinusoidal processes 94

1.11 Guide to the bibliography 95

Bibliography 95

Appendixes 97

1.A Multirate systems 98

1.A.1 Fundamentals 98

1.A.2 Decimation 100

1.A.3 Interpolation 102

1.A.4 Decimator filter 104

1.A.5 Interpolator filter 105

1.A.6 Rate conversion 108

1.A.7 Time interpolation 109

Linear interpolation 110

Quadratic interpolation 112

1.A.8 The noble identities 112

1.A.9 The polyphase representation 113

Efficient implementations 114

1.B Generation of a complex Gaussian noise 121

1.C Pseudo-noise sequences 122

Maximal-length 122

CAZAC 124

Gold 125

2 The Wiener filter 129

2.1 The Wiener filter 129

Matrix formulation 130

Optimum filter design 132

The principle of orthogonality 134

Expression of the minimum mean-square error 135

Characterization of the cost function surface 136

The Wi