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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.
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.
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.
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.
Über den Autor
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.
Inhaltsverzeichnis
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 Wiener filter in the z-domain 137
2.2 Linear prediction 140
Forward linear predictor 141
Optimum predictor coefficients 141
Forward prediction error filter 142
Relation between linear prediction and AR models 143
First and second order solutions 144
2.3 The least squares method 145
Data windowing 146
Matrix formulation 146
Correlation matrix 147
Determination of the optimum filter coefficients 147
2.3.1 The principle of orthogonality 148
Minimum cost function 149
The normal equation using the data matrix 149
Geometric interpretation: the projection operator 150
2.3.2 Solutions to the LS problem 151
Singular value decomposition 152
Minimum norm solution 154
2.4 The estimation problem 155
Estimation of a random variable 155
MMSE estimation 155
Extension to multiple observations 157
Linear MMSE estimation of a random variable 158
Linear MMSE estimation of a random vector 158
2.4.1 The Cramér-Rao lower bound 160
Extension to vector parameter 162
2.5 Examples of application 164
2.5.1 Identification of a linear discrete-time system 164
2.5.2 Identification of a continuous-time system 166
2.5.3 Cancellation of an interfering signal 169
2.5.4 Cancellation of a sinusoidal interferer with known frequency 170
2.5.5 Echo cancellation in digital subscriber loops 171
2.5.6 Cancellation of a periodic interferer 172
Bibliography 173
Appendixes 174
2.A The Levinson-Durbin algorithm 175
Lattice filters 176
The Delsarte-Genin algorithm 177
3 Adaptive transversal filters 179
3.1 The MSE design criterion 180
3.1.1 The steepest descent or gradient algorithm 181
Stability 181
Conditions for convergence 183
Adaptation gain 184
Transient behaviour of the MSE 185
3.1.2 The least mean square algorithm 186
Implementation 187
Computational complexity 188
Conditions for convergence 188
3.1.3 Convergence analysis of the LMS algorithm 190
Convergence of the mean 191
Convergence in the mean-square sense: real scalar case 192
Convergence in the mean-square sense: general case 193
Fundamental results 196
Observations 197
Final remarks 199
3.1.4 Other versions of the LMS algorithm 199
Leaky LMS 199
Sign algorithm 200
Normalized LMS 200
Variable adaptation gain 201
3.1.5 Example of application: the predictor 202
3.2 The recursive least squares algorithm 208
Normal equation 209
Derivation 210
Initialization 212
Recursive form of the minimum cost function 212
Convergence 214
Computational complexity 214
Example of application: the predictor 215
3.3 Fast recursive algorithms 215
3.3.1 Comparison of the various algorithms 216
3.4 Examples of application 216
3.4.1 Identification of a linear discrete-time system 217
Finite alphabet case 219
3.4.2 Cancellation of a sinusoidal interferer with known frequency 220
Bibliography 221
4 Transmission channels 223
4.1 Radio channel 223
4.1.1 Propagation and used frequencies in radio transmission 224
Basic propagation mechanisms 224
Frequency ranges 224
4.1.2 Analog front-end architectures 226
Radiation masks 226
Conventional superheterodyne receiver 227
Alternative architectures 227
Direct conversion receiver 228
Single conversion to low-IF 229
Double conversion and wideband IF 229
4.1.3 General channel model 230
High power amplifier 230
Transmission medium 233
Additive noise 234
Phase noise 234
4.1.4 Narrowband radio channel model 235
Equivalent circuit at the receiver 237
Multipath 238
Path loss as a function of distance 240
4.1.5 Fading effects in propagation models 243
Macroscopic fading or shadowing 243
Microscopic fading 245
4.1.6 Doppler shift 245
4.1.7 Wideband channel model 247
Multipath channel parameters 249
Statistical description of fading channels 250
4.1.8 Channel statistics 252
Power delay profile 252
Coherence bandwidth 253
Doppler spectrum 254
Coherence time 255
Doppler spectrum models 256
Power angular spectrum 256
Coherence distance 256
On fading 257
4.1.9 Discrete-time model for fading channels 258
Generation of a process with a preassigned spectrum 259
4.1.10 Discrete-space model of shadowing 261
4.1.11 Multiantenna systems 264
Discrete-time model 266
4.2 Telephone channel 268
Distortion 270
Noise sources 270
Echo 270
Appendixes 272
4.A Discrete-time NB model for mmWave channels 273
Angular domain representation 273
Bibliography 274
5 Vector quantization 277
5.1 Basic concept 277
5.2 Characterization of VQ 278
Parameters determining VQ performance 278
Comparison between VQ and scalar quantization 280
5.3 Optimum quantization 281
Generalized Lloyd algorithm 282
5.4 The Linde, Buzo, and Gray algorithm 284
Choice of the initial codebook 285
Splitting procedure 286
Selection of the training sequence 287
5.4.1 k-means clustering 288
5.5 Variants of VQ 288
Tree search VQ 288
Multistage VQ 289
Product code VQ 291
5.6 VQ of channel state information 292
MISO channel quantization 292
Channel feedback with feedforward information 294
5.7 Principal component analysis 295
5.7.1 PCA and k-means clustering 297
Bibliography 299
6 Digital transmission model and channel capacity 301
6.1 Digital transmission model 301
6.2 Detection 305
6.2.1 Optimum detection 306
ML 307
MAP 307
6.2.2 Soft detection 309
LLRs associated to bits of BMAP 309
Simplified expressions 312
6.2.3 Receiver strategies 314
6.3 Relevant parameters of the digital transmission model 314
Relations among parameters 315
6.4 Error probability 317
6.5 Capacity 320
6.5.1 Discrete-time AWGN channel 321
6.5.2 SISO narrowband AWGN channel 322
6.5.3 SISO dispersive AGN channel 322
6.5.4 MIMO discrete-time NB AWGN channel 325
6.6 Achievable rates of modulations in AWGN channels 326
6.6.1 Rate as a function of the SNR per dimension 327
6.6.2 Coding strategies depending on the signal-to-noise ratio 329
Coding gain 330
6.6.3 Achievable rate of an AWGN channel using PAM 331
Bibliography 333
Appendixes 334
6.A Gray labelling 335
6.B The Gaussian distribution and Marcum functions 336
6.B.1 The Q function 336
6.B.2 Marcum function 338
7 Single-carrier modulation 341
7.1 Signals and systems 341
7.1.1 Baseband digital transmission (PAM) 341
Modulator 342
Transmission channel 343
Receiver 343
Power spectral density 344
7.1.2 Passband digital transmission (QAM) 346
Modulator 346
Power spectral density 347
Three equivalent representations of the modulator 348
Coherent receiver 349
7.1.3 Baseband equivalent model of a QAM system 349
Signal analysis 349
7.1.4 Characterization of system elements 353
Transmitter 353
Transmission channel 354
Receiver 355
7.2 Intersymbol interference 356
Discrete-time equivalent system 356
Nyquist pulses 357
Eye diagram 361
7.3 Performance analysis 365
Signal-to-noise ratio 365
Symbol error probability in the absence of ISI 366
Matched filter receiver 367
7.4 Channel equalization 367
7.4.1 Zero-forcing equalizer 367
7.4.2 Linear equalizer 368
Optimum receiver in the presence of noise and ISI 369
Alternative derivation of the IIR equalizer 370
Signal-to-noise ratio at detector 374
7.4.3 LE with a...
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 Wiener filter in the z-domain 137
2.2 Linear prediction 140
Forward linear predictor 141
Optimum predictor coefficients 141
Forward prediction error filter 142
Relation between linear prediction and AR models 143
First and second order solutions 144
2.3 The least squares method 145
Data windowing 146
Matrix formulation 146
Correlation matrix 147
Determination of the optimum filter coefficients 147
2.3.1 The principle of orthogonality 148
Minimum cost function 149
The normal equation using the data matrix 149
Geometric interpretation: the projection operator 150
2.3.2 Solutions to the LS problem 151
Singular value decomposition 152
Minimum norm solution 154
2.4 The estimation problem 155
Estimation of a random variable 155
MMSE estimation 155
Extension to multiple observations 157
Linear MMSE estimation of a random variable 158
Linear MMSE estimation of a random vector 158
2.4.1 The Cramér-Rao lower bound 160
Extension to vector parameter 162
2.5 Examples of application 164
2.5.1 Identification of a linear discrete-time system 164
2.5.2 Identification of a continuous-time system 166
2.5.3 Cancellation of an interfering signal 169
2.5.4 Cancellation of a sinusoidal interferer with known frequency 170
2.5.5 Echo cancellation in digital subscriber loops 171
2.5.6 Cancellation of a periodic interferer 172
Bibliography 173
Appendixes 174
2.A The Levinson-Durbin algorithm 175
Lattice filters 176
The Delsarte-Genin algorithm 177
3 Adaptive transversal filters 179
3.1 The MSE design criterion 180
3.1.1 The steepest descent or gradient algorithm 181
Stability 181
Conditions for convergence 183
Adaptation gain 184
Transient behaviour of the MSE 185
3.1.2 The least mean square algorithm 186
Implementation 187
Computational complexity 188
Conditions for convergence 188
3.1.3 Convergence analysis of the LMS algorithm 190
Convergence of the mean 191
Convergence in the mean-square sense: real scalar case 192
Convergence in the mean-square sense: general case 193
Fundamental results 196
Observations 197
Final remarks 199
3.1.4 Other versions of the LMS algorithm 199
Leaky LMS 199
Sign algorithm 200
Normalized LMS 200
Variable adaptation gain 201
3.1.5 Example of application: the predictor 202
3.2 The recursive least squares algorithm 208
Normal equation 209
Derivation 210
Initialization 212
Recursive form of the minimum cost function 212
Convergence 214
Computational complexity 214
Example of application: the predictor 215
3.3 Fast recursive algorithms 215
3.3.1 Comparison of the various algorithms 216
3.4 Examples of application 216
3.4.1 Identification of a linear discrete-time system 217
Finite alphabet case 219
3.4.2 Cancellation of a sinusoidal interferer with known frequency 220
Bibliography 221
4 Transmission channels 223
4.1 Radio channel 223
4.1.1 Propagation and used frequencies in radio transmission 224
Basic propagation mechanisms 224
Frequency ranges 224
4.1.2 Analog front-end architectures 226
Radiation masks 226
Conventional superheterodyne receiver 227
Alternative architectures 227
Direct conversion receiver 228
Single conversion to low-IF 229
Double conversion and wideband IF 229
4.1.3 General channel model 230
High power amplifier 230
Transmission medium 233
Additive noise 234
Phase noise 234
4.1.4 Narrowband radio channel model 235
Equivalent circuit at the receiver 237
Multipath 238
Path loss as a function of distance 240
4.1.5 Fading effects in propagation models 243
Macroscopic fading or shadowing 243
Microscopic fading 245
4.1.6 Doppler shift 245
4.1.7 Wideband channel model 247
Multipath channel parameters 249
Statistical description of fading channels 250
4.1.8 Channel statistics 252
Power delay profile 252
Coherence bandwidth 253
Doppler spectrum 254
Coherence time 255
Doppler spectrum models 256
Power angular spectrum 256
Coherence distance 256
On fading 257
4.1.9 Discrete-time model for fading channels 258
Generation of a process with a preassigned spectrum 259
4.1.10 Discrete-space model of shadowing 261
4.1.11 Multiantenna systems 264
Discrete-time model 266
4.2 Telephone channel 268
Distortion 270
Noise sources 270
Echo 270
Appendixes 272
4.A Discrete-time NB model for mmWave channels 273
Angular domain representation 273
Bibliography 274
5 Vector quantization 277
5.1 Basic concept 277
5.2 Characterization of VQ 278
Parameters determining VQ performance 278
Comparison between VQ and scalar quantization 280
5.3 Optimum quantization 281
Generalized Lloyd algorithm 282
5.4 The Linde, Buzo, and Gray algorithm 284
Choice of the initial codebook 285
Splitting procedure 286
Selection of the training sequence 287
5.4.1 k-means clustering 288
5.5 Variants of VQ 288
Tree search VQ 288
Multistage VQ 289
Product code VQ 291
5.6 VQ of channel state information 292
MISO channel quantization 292
Channel feedback with feedforward information 294
5.7 Principal component analysis 295
5.7.1 PCA and k-means clustering 297
Bibliography 299
6 Digital transmission model and channel capacity 301
6.1 Digital transmission model 301
6.2 Detection 305
6.2.1 Optimum detection 306
ML 307
MAP 307
6.2.2 Soft detection 309
LLRs associated to bits of BMAP 309
Simplified expressions 312
6.2.3 Receiver strategies 314
6.3 Relevant parameters of the digital transmission model 314
Relations among parameters 315
6.4 Error probability 317
6.5 Capacity 320
6.5.1 Discrete-time AWGN channel 321
6.5.2 SISO narrowband AWGN channel 322
6.5.3 SISO dispersive AGN channel 322
6.5.4 MIMO discrete-time NB AWGN channel 325
6.6 Achievable rates of modulations in AWGN channels 326
6.6.1 Rate as a function of the SNR per dimension 327
6.6.2 Coding strategies depending on the signal-to-noise ratio 329
Coding gain 330
6.6.3 Achievable rate of an AWGN channel using PAM 331
Bibliography 333
Appendixes 334
6.A Gray labelling 335
6.B The Gaussian distribution and Marcum functions 336
6.B.1 The Q function 336
6.B.2 Marcum function 338
7 Single-carrier modulation 341
7.1 Signals and systems 341
7.1.1 Baseband digital transmission (PAM) 341
Modulator 342
Transmission channel 343
Receiver 343
Power spectral density 344
7.1.2 Passband digital transmission (QAM) 346
Modulator 346
Power spectral density 347
Three equivalent representations of the modulator 348
Coherent receiver 349
7.1.3 Baseband equivalent model of a QAM system 349
Signal analysis 349
7.1.4 Characterization of system elements 353
Transmitter 353
Transmission channel 354
Receiver 355
7.2 Intersymbol interference 356
Discrete-time equivalent system 356
Nyquist pulses 357
Eye diagram 361
7.3 Performance analysis 365
Signal-to-noise ratio 365
Symbol error probability in the absence of ISI 366
Matched filter receiver 367
7.4 Channel equalization 367
7.4.1 Zero-forcing equalizer 367
7.4.2 Linear equalizer 368
Optimum receiver in the presence of noise and ISI 369
Alternative derivation of the IIR equalizer 370
Signal-to-noise ratio at detector 374
7.4.3 LE with a...
Details
| Erscheinungsjahr: | 2021 |
|---|---|
| Fachbereich: | Nachrichtentechnik |
| Genre: | Technik |
| Rubrik: | Naturwissenschaften & Technik |
| Medium: | Buch |
| Seiten: | 960 |
| Inhalt: | 960 S. |
| ISBN-13: | 9781119567967 |
| ISBN-10: | 1119567963 |
| Sprache: | Englisch |
| Einband: | Gebunden |
| Autor: |
Benvenuto, Nevio
Cherubini, Giovanni Tomasin, Stefano |
| Auflage: | 2nd edition |
| Hersteller: | Wiley |
| Maße: | 254 x 178 x 51 mm |
| Von/Mit: | Nevio Benvenuto (u. a.) |
| Erscheinungsdatum: | 01.02.2021 |
| Gewicht: | 1,86 kg |
Über den Autor
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.
Inhaltsverzeichnis
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 Wiener filter in the z-domain 137
2.2 Linear prediction 140
Forward linear predictor 141
Optimum predictor coefficients 141
Forward prediction error filter 142
Relation between linear prediction and AR models 143
First and second order solutions 144
2.3 The least squares method 145
Data windowing 146
Matrix formulation 146
Correlation matrix 147
Determination of the optimum filter coefficients 147
2.3.1 The principle of orthogonality 148
Minimum cost function 149
The normal equation using the data matrix 149
Geometric interpretation: the projection operator 150
2.3.2 Solutions to the LS problem 151
Singular value decomposition 152
Minimum norm solution 154
2.4 The estimation problem 155
Estimation of a random variable 155
MMSE estimation 155
Extension to multiple observations 157
Linear MMSE estimation of a random variable 158
Linear MMSE estimation of a random vector 158
2.4.1 The Cramér-Rao lower bound 160
Extension to vector parameter 162
2.5 Examples of application 164
2.5.1 Identification of a linear discrete-time system 164
2.5.2 Identification of a continuous-time system 166
2.5.3 Cancellation of an interfering signal 169
2.5.4 Cancellation of a sinusoidal interferer with known frequency 170
2.5.5 Echo cancellation in digital subscriber loops 171
2.5.6 Cancellation of a periodic interferer 172
Bibliography 173
Appendixes 174
2.A The Levinson-Durbin algorithm 175
Lattice filters 176
The Delsarte-Genin algorithm 177
3 Adaptive transversal filters 179
3.1 The MSE design criterion 180
3.1.1 The steepest descent or gradient algorithm 181
Stability 181
Conditions for convergence 183
Adaptation gain 184
Transient behaviour of the MSE 185
3.1.2 The least mean square algorithm 186
Implementation 187
Computational complexity 188
Conditions for convergence 188
3.1.3 Convergence analysis of the LMS algorithm 190
Convergence of the mean 191
Convergence in the mean-square sense: real scalar case 192
Convergence in the mean-square sense: general case 193
Fundamental results 196
Observations 197
Final remarks 199
3.1.4 Other versions of the LMS algorithm 199
Leaky LMS 199
Sign algorithm 200
Normalized LMS 200
Variable adaptation gain 201
3.1.5 Example of application: the predictor 202
3.2 The recursive least squares algorithm 208
Normal equation 209
Derivation 210
Initialization 212
Recursive form of the minimum cost function 212
Convergence 214
Computational complexity 214
Example of application: the predictor 215
3.3 Fast recursive algorithms 215
3.3.1 Comparison of the various algorithms 216
3.4 Examples of application 216
3.4.1 Identification of a linear discrete-time system 217
Finite alphabet case 219
3.4.2 Cancellation of a sinusoidal interferer with known frequency 220
Bibliography 221
4 Transmission channels 223
4.1 Radio channel 223
4.1.1 Propagation and used frequencies in radio transmission 224
Basic propagation mechanisms 224
Frequency ranges 224
4.1.2 Analog front-end architectures 226
Radiation masks 226
Conventional superheterodyne receiver 227
Alternative architectures 227
Direct conversion receiver 228
Single conversion to low-IF 229
Double conversion and wideband IF 229
4.1.3 General channel model 230
High power amplifier 230
Transmission medium 233
Additive noise 234
Phase noise 234
4.1.4 Narrowband radio channel model 235
Equivalent circuit at the receiver 237
Multipath 238
Path loss as a function of distance 240
4.1.5 Fading effects in propagation models 243
Macroscopic fading or shadowing 243
Microscopic fading 245
4.1.6 Doppler shift 245
4.1.7 Wideband channel model 247
Multipath channel parameters 249
Statistical description of fading channels 250
4.1.8 Channel statistics 252
Power delay profile 252
Coherence bandwidth 253
Doppler spectrum 254
Coherence time 255
Doppler spectrum models 256
Power angular spectrum 256
Coherence distance 256
On fading 257
4.1.9 Discrete-time model for fading channels 258
Generation of a process with a preassigned spectrum 259
4.1.10 Discrete-space model of shadowing 261
4.1.11 Multiantenna systems 264
Discrete-time model 266
4.2 Telephone channel 268
Distortion 270
Noise sources 270
Echo 270
Appendixes 272
4.A Discrete-time NB model for mmWave channels 273
Angular domain representation 273
Bibliography 274
5 Vector quantization 277
5.1 Basic concept 277
5.2 Characterization of VQ 278
Parameters determining VQ performance 278
Comparison between VQ and scalar quantization 280
5.3 Optimum quantization 281
Generalized Lloyd algorithm 282
5.4 The Linde, Buzo, and Gray algorithm 284
Choice of the initial codebook 285
Splitting procedure 286
Selection of the training sequence 287
5.4.1 k-means clustering 288
5.5 Variants of VQ 288
Tree search VQ 288
Multistage VQ 289
Product code VQ 291
5.6 VQ of channel state information 292
MISO channel quantization 292
Channel feedback with feedforward information 294
5.7 Principal component analysis 295
5.7.1 PCA and k-means clustering 297
Bibliography 299
6 Digital transmission model and channel capacity 301
6.1 Digital transmission model 301
6.2 Detection 305
6.2.1 Optimum detection 306
ML 307
MAP 307
6.2.2 Soft detection 309
LLRs associated to bits of BMAP 309
Simplified expressions 312
6.2.3 Receiver strategies 314
6.3 Relevant parameters of the digital transmission model 314
Relations among parameters 315
6.4 Error probability 317
6.5 Capacity 320
6.5.1 Discrete-time AWGN channel 321
6.5.2 SISO narrowband AWGN channel 322
6.5.3 SISO dispersive AGN channel 322
6.5.4 MIMO discrete-time NB AWGN channel 325
6.6 Achievable rates of modulations in AWGN channels 326
6.6.1 Rate as a function of the SNR per dimension 327
6.6.2 Coding strategies depending on the signal-to-noise ratio 329
Coding gain 330
6.6.3 Achievable rate of an AWGN channel using PAM 331
Bibliography 333
Appendixes 334
6.A Gray labelling 335
6.B The Gaussian distribution and Marcum functions 336
6.B.1 The Q function 336
6.B.2 Marcum function 338
7 Single-carrier modulation 341
7.1 Signals and systems 341
7.1.1 Baseband digital transmission (PAM) 341
Modulator 342
Transmission channel 343
Receiver 343
Power spectral density 344
7.1.2 Passband digital transmission (QAM) 346
Modulator 346
Power spectral density 347
Three equivalent representations of the modulator 348
Coherent receiver 349
7.1.3 Baseband equivalent model of a QAM system 349
Signal analysis 349
7.1.4 Characterization of system elements 353
Transmitter 353
Transmission channel 354
Receiver 355
7.2 Intersymbol interference 356
Discrete-time equivalent system 356
Nyquist pulses 357
Eye diagram 361
7.3 Performance analysis 365
Signal-to-noise ratio 365
Symbol error probability in the absence of ISI 366
Matched filter receiver 367
7.4 Channel equalization 367
7.4.1 Zero-forcing equalizer 367
7.4.2 Linear equalizer 368
Optimum receiver in the presence of noise and ISI 369
Alternative derivation of the IIR equalizer 370
Signal-to-noise ratio at detector 374
7.4.3 LE with a...
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 Wiener filter in the z-domain 137
2.2 Linear prediction 140
Forward linear predictor 141
Optimum predictor coefficients 141
Forward prediction error filter 142
Relation between linear prediction and AR models 143
First and second order solutions 144
2.3 The least squares method 145
Data windowing 146
Matrix formulation 146
Correlation matrix 147
Determination of the optimum filter coefficients 147
2.3.1 The principle of orthogonality 148
Minimum cost function 149
The normal equation using the data matrix 149
Geometric interpretation: the projection operator 150
2.3.2 Solutions to the LS problem 151
Singular value decomposition 152
Minimum norm solution 154
2.4 The estimation problem 155
Estimation of a random variable 155
MMSE estimation 155
Extension to multiple observations 157
Linear MMSE estimation of a random variable 158
Linear MMSE estimation of a random vector 158
2.4.1 The Cramér-Rao lower bound 160
Extension to vector parameter 162
2.5 Examples of application 164
2.5.1 Identification of a linear discrete-time system 164
2.5.2 Identification of a continuous-time system 166
2.5.3 Cancellation of an interfering signal 169
2.5.4 Cancellation of a sinusoidal interferer with known frequency 170
2.5.5 Echo cancellation in digital subscriber loops 171
2.5.6 Cancellation of a periodic interferer 172
Bibliography 173
Appendixes 174
2.A The Levinson-Durbin algorithm 175
Lattice filters 176
The Delsarte-Genin algorithm 177
3 Adaptive transversal filters 179
3.1 The MSE design criterion 180
3.1.1 The steepest descent or gradient algorithm 181
Stability 181
Conditions for convergence 183
Adaptation gain 184
Transient behaviour of the MSE 185
3.1.2 The least mean square algorithm 186
Implementation 187
Computational complexity 188
Conditions for convergence 188
3.1.3 Convergence analysis of the LMS algorithm 190
Convergence of the mean 191
Convergence in the mean-square sense: real scalar case 192
Convergence in the mean-square sense: general case 193
Fundamental results 196
Observations 197
Final remarks 199
3.1.4 Other versions of the LMS algorithm 199
Leaky LMS 199
Sign algorithm 200
Normalized LMS 200
Variable adaptation gain 201
3.1.5 Example of application: the predictor 202
3.2 The recursive least squares algorithm 208
Normal equation 209
Derivation 210
Initialization 212
Recursive form of the minimum cost function 212
Convergence 214
Computational complexity 214
Example of application: the predictor 215
3.3 Fast recursive algorithms 215
3.3.1 Comparison of the various algorithms 216
3.4 Examples of application 216
3.4.1 Identification of a linear discrete-time system 217
Finite alphabet case 219
3.4.2 Cancellation of a sinusoidal interferer with known frequency 220
Bibliography 221
4 Transmission channels 223
4.1 Radio channel 223
4.1.1 Propagation and used frequencies in radio transmission 224
Basic propagation mechanisms 224
Frequency ranges 224
4.1.2 Analog front-end architectures 226
Radiation masks 226
Conventional superheterodyne receiver 227
Alternative architectures 227
Direct conversion receiver 228
Single conversion to low-IF 229
Double conversion and wideband IF 229
4.1.3 General channel model 230
High power amplifier 230
Transmission medium 233
Additive noise 234
Phase noise 234
4.1.4 Narrowband radio channel model 235
Equivalent circuit at the receiver 237
Multipath 238
Path loss as a function of distance 240
4.1.5 Fading effects in propagation models 243
Macroscopic fading or shadowing 243
Microscopic fading 245
4.1.6 Doppler shift 245
4.1.7 Wideband channel model 247
Multipath channel parameters 249
Statistical description of fading channels 250
4.1.8 Channel statistics 252
Power delay profile 252
Coherence bandwidth 253
Doppler spectrum 254
Coherence time 255
Doppler spectrum models 256
Power angular spectrum 256
Coherence distance 256
On fading 257
4.1.9 Discrete-time model for fading channels 258
Generation of a process with a preassigned spectrum 259
4.1.10 Discrete-space model of shadowing 261
4.1.11 Multiantenna systems 264
Discrete-time model 266
4.2 Telephone channel 268
Distortion 270
Noise sources 270
Echo 270
Appendixes 272
4.A Discrete-time NB model for mmWave channels 273
Angular domain representation 273
Bibliography 274
5 Vector quantization 277
5.1 Basic concept 277
5.2 Characterization of VQ 278
Parameters determining VQ performance 278
Comparison between VQ and scalar quantization 280
5.3 Optimum quantization 281
Generalized Lloyd algorithm 282
5.4 The Linde, Buzo, and Gray algorithm 284
Choice of the initial codebook 285
Splitting procedure 286
Selection of the training sequence 287
5.4.1 k-means clustering 288
5.5 Variants of VQ 288
Tree search VQ 288
Multistage VQ 289
Product code VQ 291
5.6 VQ of channel state information 292
MISO channel quantization 292
Channel feedback with feedforward information 294
5.7 Principal component analysis 295
5.7.1 PCA and k-means clustering 297
Bibliography 299
6 Digital transmission model and channel capacity 301
6.1 Digital transmission model 301
6.2 Detection 305
6.2.1 Optimum detection 306
ML 307
MAP 307
6.2.2 Soft detection 309
LLRs associated to bits of BMAP 309
Simplified expressions 312
6.2.3 Receiver strategies 314
6.3 Relevant parameters of the digital transmission model 314
Relations among parameters 315
6.4 Error probability 317
6.5 Capacity 320
6.5.1 Discrete-time AWGN channel 321
6.5.2 SISO narrowband AWGN channel 322
6.5.3 SISO dispersive AGN channel 322
6.5.4 MIMO discrete-time NB AWGN channel 325
6.6 Achievable rates of modulations in AWGN channels 326
6.6.1 Rate as a function of the SNR per dimension 327
6.6.2 Coding strategies depending on the signal-to-noise ratio 329
Coding gain 330
6.6.3 Achievable rate of an AWGN channel using PAM 331
Bibliography 333
Appendixes 334
6.A Gray labelling 335
6.B The Gaussian distribution and Marcum functions 336
6.B.1 The Q function 336
6.B.2 Marcum function 338
7 Single-carrier modulation 341
7.1 Signals and systems 341
7.1.1 Baseband digital transmission (PAM) 341
Modulator 342
Transmission channel 343
Receiver 343
Power spectral density 344
7.1.2 Passband digital transmission (QAM) 346
Modulator 346
Power spectral density 347
Three equivalent representations of the modulator 348
Coherent receiver 349
7.1.3 Baseband equivalent model of a QAM system 349
Signal analysis 349
7.1.4 Characterization of system elements 353
Transmitter 353
Transmission channel 354
Receiver 355
7.2 Intersymbol interference 356
Discrete-time equivalent system 356
Nyquist pulses 357
Eye diagram 361
7.3 Performance analysis 365
Signal-to-noise ratio 365
Symbol error probability in the absence of ISI 366
Matched filter receiver 367
7.4 Channel equalization 367
7.4.1 Zero-forcing equalizer 367
7.4.2 Linear equalizer 368
Optimum receiver in the presence of noise and ISI 369
Alternative derivation of the IIR equalizer 370
Signal-to-noise ratio at detector 374
7.4.3 LE with a...
Details
| Erscheinungsjahr: | 2021 |
|---|---|
| Fachbereich: | Nachrichtentechnik |
| Genre: | Technik |
| Rubrik: | Naturwissenschaften & Technik |
| Medium: | Buch |
| Seiten: | 960 |
| Inhalt: | 960 S. |
| ISBN-13: | 9781119567967 |
| ISBN-10: | 1119567963 |
| Sprache: | Englisch |
| Einband: | Gebunden |
| Autor: |
Benvenuto, Nevio
Cherubini, Giovanni Tomasin, Stefano |
| Auflage: | 2nd edition |
| Hersteller: | Wiley |
| Maße: | 254 x 178 x 51 mm |
| Von/Mit: | Nevio Benvenuto (u. a.) |
| Erscheinungsdatum: | 01.02.2021 |
| Gewicht: | 1,86 kg |
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