Titre : |
Channel Equalization for Wireless Communications : From Concepts to Detailed Mathematics |
Type de document : |
texte imprimé |
Auteurs : |
Bottomley, Gregory E., |
Editeur : |
USA : John Wiley |
Année de publication : |
2011 |
Importance : |
219 p |
Présentation : |
Couv.ill. tab. schém. refer. Index |
Format : |
24×16 cm |
ISBN/ISSN/EAN : |
978-0-470-87427-1 |
Langues : |
Anglais (eng) Langues originales : Anglais (eng) |
Index. décimale : |
621 Physique appliquée |
Résumé : |
"he most thorough, up-to-date reference on channel equalization—from basic concepts to complex modeling techniques
In today's instant-access society, a high premium is placed on information that can be stored and communicated effectively. As a result, storage densities and communications rates are being pushed to capacity, causing information symbols to interfere with one another. To help unclog pathways for the clearer conveyance of information, this book offers in-depth discussion of the significant contributions and future adaptability of channel equalization and a set of approaches for solving the problem of intersymbol interference (ISI). Chapter explorations in Channel Equalization include:
Channel equalization topics presented with incremental learning methodology—from the very fundamental concept to more advanced mathematical knowledge
Coverage of technology used in second-, third- and fourth-generation cellular communication systems
A set of homework problems that reinforce concepts discussed in the book
Tutorial explanations of recent developments currently captured in IEEE technical journals
Unlike existing digital communications books that devote cursory attention to channel equalization, this invaluable guide addresses a crucial need by focusing solely on the background, current state, and future direction of this increasingly important technology. A unique mix of basic concepts and complex frameworks for delivering digitized data make Channel Equalization a valuable reference for all practicing wireless communication engineers and students dealing with the pressing demands of the information age."
|
Note de contenu : |
Introduction 1
1.1 The Idea 2
1.2 More Details 4
1.2.1 General dispersive and MIMO scenarios 5
1.2.2 Use of complex numbers 7
1.3 The Math 7
1.3.1 Transmitter 9
1.3.2 Channel 11
1.3.3 Receiver 15
1.4 More Math 16
1.4.1 Transmitter 16
1.4.2 Channel 21
1.4.3 Receiver 23
1.5 An Example 24
1.5.1 Reference system and channel models 26
1.6 The Literature 26
X CONTENTS
Problems 27
Matched Filtering 31
2.1 The Idea 31
2.2 More Details 33
2.2.1 General dispersive scenario 34
2.2.2 MIMO scenario 35
2.3 The Math 35
2.3.1 Maximum-likelihood detection 35
2.3.2 Output SNR and error rate performance 37
2.3.3 TDM 38
2.3.4 Maximum SNR 38
2.3.5 Partial MF 41
2.3.6 Fractionally spaced MF 42
2.3.7 Whitened MF 43
2.3.8 The matched filter bound (MFB) 44
2.3.9 MF in colored noise 44
2.3.10 Performance results 45
2.4 More Math 47
2.4.1 Partial MF 49
2.4.2 The matched filter bound 52
2.4.3 MF in colored noise 53
2.4.4 Group matched filtering 53
2.5 An Example 54
2.6 The Literature 54
Problems 55
Zero-Forcing Decision Feedback Equalization 57
3.1 The Idea 57
3.2 More Details 59
3.3 The Math 62
3.3.1 Performance results 63
3.4 More Math 63
3.4.1 Dispersive scenario and TDM 64
3.4.2 MIMO/cochannel scenario 65
3.5 An Example 66
3.6 The Literature 66
Problems 66
Linear Equalization 69
4.1 The Idea 69
CONTENTS XI
Minimum mean-square error
More Details
Minimum mean-square error solution
Maximum SINR solution
General dispersive scenario
General MIMO scenario
The Math
MMSE solution
ML solution
Output SINR
Other design criteria
Fractionally spaced linear equalization
Performance results
More Math
ZF solution
MMSE solution
ML solution
Other forms for the CDM case
Other forms for the OFDM case
Simpler models
Block and sub-block forms
Group linear equalization
An Example
The Literature
Problems
MMSE and ML Decision Feedback Equalization 99
5.1 The Idea 99
5.2 More Details 101
5.3 The Math 104
5.3.1 MMSE solution 104
5.3.2 ML solution 106
5.3.3 Output SINR 106
5.3.4 Fractionally spaced DFE 106
5.3.5 Performance results 106
5.4 More Math 108
5.4.1 MMSE solution 108
5.4.2 ML solution 109
5.4.3 Simpler models 109
5.4.4 Block and sub-block forms 109
5.4.5 Group decision feedback equalization 110
5.5 An Example 110
XII CONTENTS
5.6 The Literature 110
Problems 112
Maximum Likelihood Sequence Detection 115
6.1 The Idea 115
6.2 More Details 117
6.3 The Math 120
6.3.1 The Viterbi algorithm 120
6.3.2 SISO TDM scenario 125
6.3.3 Given statistics 130
6.3.4 Fractionally spaced MLSD 130
6.3.5 Approximate forms 130
6.3.6 Performance results 131
6.4 More Math 138
6.4.1 Block form 142
6.4.2 Sphere decoding 142
6.4.3 More approximate forms 143
6.5 An Example 144
6.6 The Literature 145
Problems 147
Advanced Topics 151
7.1 The Idea 151
7.1.1 MAP symbol detection 151
7.1.2 Soft information 153
7.1.3 Joint demodulation and decoding 155
7.2 More Details 156
7.2.1 MAP symbol detection 156
7.2.2 Soft information 157
7.2.3 Joint demodulation and decoding 160
7.3 The Math 160
7.3.1 MAP symbol detection 160
7.3.2 Soft information 166
7.3.3 Joint demodulation and decoding 167
7.4 More Math 167
7.5 An Example 167
7.6 The Literature 168
7.6.1 MAP symbol detection 168
7.6.2 Soft information 168
7.6.3 Joint demodulation and decoding 169
Problems 169
CONTENTS XIII
8 Practical Considerations 173
8.1 The Idea 173
8.2 More Details 175
8.2.1 Parameter estimation 175
8.2.2 Equalizer selection 176
8.2.3 Radio aspects 177
8.3 The Math 178
8.3.1 Time-invariant channel and training sequence 179
8.3.2 Time-varying channel and known symbol sequence 180
8.3.3 Time-varying channel and partially known symbol
sequence 181
8.3.4 Per-survivor processing 182
8.4 More practical aspects 182
8.4.1 Acquisition 182
8.4.2 Timing 182
8.4.3 Doppler 183
8.4.4 Channel Delay Estimation 183
8.4.5 Pilot symbol and traffic symbol powers 184
8.4.6 Pilot symbols and multi-antenna transmission 184
8.5 An Example 184
8.6 The Literature 185
Problems 185
Appendix A: Simulation Notes 189
A.l Fading channels 191
A.2 Matched filter and matched filter bound 192
A.3 Simulation calibration 192
Appendix B: Notation 193
References 197
|
Channel Equalization for Wireless Communications : From Concepts to Detailed Mathematics [texte imprimé] / Bottomley, Gregory E., . - USA : John Wiley, 2011 . - 219 p : Couv.ill. tab. schém. refer. Index ; 24×16 cm. ISBN : 978-0-470-87427-1 Langues : Anglais ( eng) Langues originales : Anglais ( eng)
Index. décimale : |
621 Physique appliquée |
Résumé : |
"he most thorough, up-to-date reference on channel equalization—from basic concepts to complex modeling techniques
In today's instant-access society, a high premium is placed on information that can be stored and communicated effectively. As a result, storage densities and communications rates are being pushed to capacity, causing information symbols to interfere with one another. To help unclog pathways for the clearer conveyance of information, this book offers in-depth discussion of the significant contributions and future adaptability of channel equalization and a set of approaches for solving the problem of intersymbol interference (ISI). Chapter explorations in Channel Equalization include:
Channel equalization topics presented with incremental learning methodology—from the very fundamental concept to more advanced mathematical knowledge
Coverage of technology used in second-, third- and fourth-generation cellular communication systems
A set of homework problems that reinforce concepts discussed in the book
Tutorial explanations of recent developments currently captured in IEEE technical journals
Unlike existing digital communications books that devote cursory attention to channel equalization, this invaluable guide addresses a crucial need by focusing solely on the background, current state, and future direction of this increasingly important technology. A unique mix of basic concepts and complex frameworks for delivering digitized data make Channel Equalization a valuable reference for all practicing wireless communication engineers and students dealing with the pressing demands of the information age."
|
Note de contenu : |
Introduction 1
1.1 The Idea 2
1.2 More Details 4
1.2.1 General dispersive and MIMO scenarios 5
1.2.2 Use of complex numbers 7
1.3 The Math 7
1.3.1 Transmitter 9
1.3.2 Channel 11
1.3.3 Receiver 15
1.4 More Math 16
1.4.1 Transmitter 16
1.4.2 Channel 21
1.4.3 Receiver 23
1.5 An Example 24
1.5.1 Reference system and channel models 26
1.6 The Literature 26
X CONTENTS
Problems 27
Matched Filtering 31
2.1 The Idea 31
2.2 More Details 33
2.2.1 General dispersive scenario 34
2.2.2 MIMO scenario 35
2.3 The Math 35
2.3.1 Maximum-likelihood detection 35
2.3.2 Output SNR and error rate performance 37
2.3.3 TDM 38
2.3.4 Maximum SNR 38
2.3.5 Partial MF 41
2.3.6 Fractionally spaced MF 42
2.3.7 Whitened MF 43
2.3.8 The matched filter bound (MFB) 44
2.3.9 MF in colored noise 44
2.3.10 Performance results 45
2.4 More Math 47
2.4.1 Partial MF 49
2.4.2 The matched filter bound 52
2.4.3 MF in colored noise 53
2.4.4 Group matched filtering 53
2.5 An Example 54
2.6 The Literature 54
Problems 55
Zero-Forcing Decision Feedback Equalization 57
3.1 The Idea 57
3.2 More Details 59
3.3 The Math 62
3.3.1 Performance results 63
3.4 More Math 63
3.4.1 Dispersive scenario and TDM 64
3.4.2 MIMO/cochannel scenario 65
3.5 An Example 66
3.6 The Literature 66
Problems 66
Linear Equalization 69
4.1 The Idea 69
CONTENTS XI
Minimum mean-square error
More Details
Minimum mean-square error solution
Maximum SINR solution
General dispersive scenario
General MIMO scenario
The Math
MMSE solution
ML solution
Output SINR
Other design criteria
Fractionally spaced linear equalization
Performance results
More Math
ZF solution
MMSE solution
ML solution
Other forms for the CDM case
Other forms for the OFDM case
Simpler models
Block and sub-block forms
Group linear equalization
An Example
The Literature
Problems
MMSE and ML Decision Feedback Equalization 99
5.1 The Idea 99
5.2 More Details 101
5.3 The Math 104
5.3.1 MMSE solution 104
5.3.2 ML solution 106
5.3.3 Output SINR 106
5.3.4 Fractionally spaced DFE 106
5.3.5 Performance results 106
5.4 More Math 108
5.4.1 MMSE solution 108
5.4.2 ML solution 109
5.4.3 Simpler models 109
5.4.4 Block and sub-block forms 109
5.4.5 Group decision feedback equalization 110
5.5 An Example 110
XII CONTENTS
5.6 The Literature 110
Problems 112
Maximum Likelihood Sequence Detection 115
6.1 The Idea 115
6.2 More Details 117
6.3 The Math 120
6.3.1 The Viterbi algorithm 120
6.3.2 SISO TDM scenario 125
6.3.3 Given statistics 130
6.3.4 Fractionally spaced MLSD 130
6.3.5 Approximate forms 130
6.3.6 Performance results 131
6.4 More Math 138
6.4.1 Block form 142
6.4.2 Sphere decoding 142
6.4.3 More approximate forms 143
6.5 An Example 144
6.6 The Literature 145
Problems 147
Advanced Topics 151
7.1 The Idea 151
7.1.1 MAP symbol detection 151
7.1.2 Soft information 153
7.1.3 Joint demodulation and decoding 155
7.2 More Details 156
7.2.1 MAP symbol detection 156
7.2.2 Soft information 157
7.2.3 Joint demodulation and decoding 160
7.3 The Math 160
7.3.1 MAP symbol detection 160
7.3.2 Soft information 166
7.3.3 Joint demodulation and decoding 167
7.4 More Math 167
7.5 An Example 167
7.6 The Literature 168
7.6.1 MAP symbol detection 168
7.6.2 Soft information 168
7.6.3 Joint demodulation and decoding 169
Problems 169
CONTENTS XIII
8 Practical Considerations 173
8.1 The Idea 173
8.2 More Details 175
8.2.1 Parameter estimation 175
8.2.2 Equalizer selection 176
8.2.3 Radio aspects 177
8.3 The Math 178
8.3.1 Time-invariant channel and training sequence 179
8.3.2 Time-varying channel and known symbol sequence 180
8.3.3 Time-varying channel and partially known symbol
sequence 181
8.3.4 Per-survivor processing 182
8.4 More practical aspects 182
8.4.1 Acquisition 182
8.4.2 Timing 182
8.4.3 Doppler 183
8.4.4 Channel Delay Estimation 183
8.4.5 Pilot symbol and traffic symbol powers 184
8.4.6 Pilot symbols and multi-antenna transmission 184
8.5 An Example 184
8.6 The Literature 185
Problems 185
Appendix A: Simulation Notes 189
A.l Fading channels 191
A.2 Matched filter and matched filter bound 192
A.3 Simulation calibration 192
Appendix B: Notation 193
References 197
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