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Channel Equalization for Wireless C
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To my colleagues at Ericsson
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CONTENTS List of Figures List of Ta
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CONTENTS XI 4.2 4.3 4.4 4.5 4.6 4.1
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CONTENTS XIII 8 Practical Considera
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xvi LIST OF FIGURES 2.2 Matched fil
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XviÜ LIST OF FIGURES 6.18 Scatter
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XX Prologue Alice was nervous. Woul
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XXÜ PREFACE is introduced as neede
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ACRONYMS AC A/D ADC AMLD AMPS ASK A
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ML MLD MLPD MLSD MLSE MRC MSE MSB O
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2 INTRODUCTION Table 1.1 Possible m
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4 INTRODUCTION s o S 1 8 2 S 3 S 0
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6 INTRODUCTION A block diagram of t
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8 INTRODUCTION Figure 1.7 QPSK. tra
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10 INTRODUCTION phases (phase modul
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12 INTRODUCTION rollo« 0.22 0.8 Ί
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14 INTRODUCTION is the "channel" re
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16 INTRODUCTION 1.4 MORE MATH In th
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18 INTRODUCTION For K = 1 (order 0)
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20 INTRODUCTION The K = 4 main bloc
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22 INTRODUCTION A sample of the noi
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24 INTRODUCTION The receiver may ha
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26 INTRODUCTION 1.5.1 Reference sys
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28 INTRODUCTION a) What most likely
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CHAPTER 2 MATCHED FILTERING In this
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MORE DETAILS 33 2.2 MORE DETAILS So
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THE MATH 35 Now compare the output
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THE MATH 37 The correlation in (2.2
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THE MATH 39 correlation function f(
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THE MATH 41 Thus, in the complex pl
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THE MATH 43 to the medium response
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THE MATH 45 We can interpret f(t) a
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- Page 77 and 78: PROBLEMS 55 period) is examined in
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- Page 81 and 82: MORE DETAILS 59 1/c r m —► H i
- Page 83 and 84: MORE DETAILS 61 where «i = m - (d/
- Page 85 and 86: MORE MATH 63 Because there is no IS
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- Page 89 and 90: PROBLEMS 67 b) What is the detected
- Page 91 and 92: CHAPTER 4 LINEAR EQUALIZATION With
- Page 93 and 94: THE IDEA 71 estimate of symbol s 2
- Page 95 and 96: THE IDEA 73 Notice that £2 (MSE) d
- Page 97 and 98: MORE DETAILS 75 The first term is t
- Page 99 and 100: MORE DETAILS 77 To obtain the unity
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- Page 103 and 104: THE MATH 81 To obtain the MMSE solu
- Page 105 and 106: THE MATH 83 where *m n = ν ^ ^ ^ '
- Page 107 and 108: THE MATH 85 power is much larger th
- Page 109 and 110: MORE MATH 87 IQ' 1 OC 111 ω 10 2 1
- Page 111 and 112: MORE MATH 89 values of h k m (f) fr
- Page 113 and 114: MORE MATH 91 where C(d(),d 0 ) . C(
- Page 115 and 116: AN EXAMPLE 93 between z and s. This
- Page 117 and 118: PROBLEMS 95 Another aspect of cocha
- Page 119: PROBLEMS 97 The math 4.11 Consider
- Page 123 and 124: MORE DETAILS 101 2.5 - ÍS 1.5 0.5
- Page 125 and 126: MORE DETAILS 103 Notice that this i
- Page 127 and 128: THE MATH 105 Suppose we are detecti
- Page 129 and 130: THE MATH 107 10" 1 DC LU m 10 2 10"
- Page 131 and 132: MORE MATH 109 The decision variable
- Page 133 and 134: THE LITERATURE 111 both directions
- Page 135: PROBLEMS 113 a) Determine the model
- Page 138 and 139: 116 MAXIMUM LIKELIHOOD SEQUENCE DET
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148 MAXIMUM LIKELIHOOD SEQUENCE DET
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CHAPTER 7 ADVANCED TOPICS In the pr
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THE IDEA 153 Traditionally, we stil
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THE IDEA 155 detected value. Thus,
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MORE DETAILS 157 minimum sequence m
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MORE DETAILS 159 While it seems lik
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THE MATH 161 still take advantage o
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THE MATH 163 Figure 7.1 MAPSD trell
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THE MATH 165 Let's start with the "
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MORE MATH 167 If the BCJR algorithm
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PROBLEMS 169 parity-check (LDPC) co
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PROBLEMS 171 b) What are the three
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174 PRACTICAL CONSIDERATIONS In pra
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176 PRACTICAL CONSIDERATIONS adapti
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178 PRACTICAL CONSIDERATIONS Figure
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180 PRACTICAL CONSIDERATIONS 8.3.2
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182 PRACTICAL CONSIDERATIONS When t
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184 PRACTICAL CONSIDERATIONS from a
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186 PRACTICAL CONSIDERATIONS a) Sup
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188 PRACTICAL CONSIDERATIONS Epilog
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190 SIMULATION NOTES etc., then the
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192 SIMULATION NOTES and noise real
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194 NOTATION Variable Meaning a lc
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REFERENCES [Abd94] [Abe68] [Abe70]
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REFERENCES 199 [Bau02] [Bel79] [Ben
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REFERENCES 201 [Cho04] [Chu96] [Cim
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REFERENCES 203 [For73] G. D. Forney
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REFERENCES 205 [Hoe99] P. Hoeher an
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REFERENCES 207 Signal Processing
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REFERENCES 209 [Mon84] [Mud04] [Nag
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REFERENCES 211 [San96] [Say98] [Say
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REFERENCES 213 [Vac81] G. M. Vachul
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REFERENCES 215 [Xie90b] [Xio90] [Yo