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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
- Page 61 and 62: THE MATH 39 correlation function f(
- Page 63 and 64: THE MATH 41 Thus, in the complex pl
- Page 65 and 66: THE MATH 43 to the medium response
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- Page 69 and 70: MORE MATH 47 10" 1 Odeg 90 deg X 18
- Page 71 and 72: MORE MATH 49 With despread-level Ra
- Page 73 and 74: MORE MATH 51 Figure 2.6 OFDM exampl
- Page 75 and 76: MORE MATH 53 2.4.3 MF in colored no
- Page 77 and 78: PROBLEMS 55 period) is examined in
- Page 79 and 80: CHAPTER 3 ZERO-FORCING DECISION FEE
- 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
- Page 87 and 88: MORE MATH 65 In the later chapter o
- 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
- Page 101 and 102: THE MATH 79 and SINR becomes w T h
- 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: MORE MATH 89 values of h k m (f) fr
- 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 122 and 123: 100 MMSE AND ML DECISION FEEDBACK E
- Page 124 and 125: 102 MMSE AND ML DECISION FEEDBACK E
- Page 126 and 127: 104 MMSE AND ML DECISION FEEDBACK E
- Page 128 and 129: 106 MMSE AND ML DECISION FEEDBACK E
- Page 130 and 131: 108 MMSE AND ML DECISION FEEDBACK E
- Page 132 and 133: 110 MMSE AND ML DECISION FEEDBACK E
- Page 134 and 135: 112 MMSE AND ML DECISION FEEDBACK E
- Page 137 and 138: CHAPTER 6 MAXIMUM LIKELIHOOD SEQUEN
- Page 139 and 140: MORE DETAILS 117 r 2 Ht, r, fcl + f
- Page 141 and 142: MORE DETAILS 119 Figure 6.3 MLSD tr
- Page 143 and 144: THE MATH 121 legs of the journey is
- Page 145 and 146: THE MATH 123 Figure 6.8 MLSD trelli
- Page 147 and 148: THE MATH 125 We would then identify
- Page 149 and 150: THE MATH 127 Expanding the square,
- Page 151 and 152: THE MATH 129 Consider the matched f
- Page 153 and 154: THE MATH 131 6.3.5.1 M-algorithm Wi
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- Page 159 and 160: THE MATH 137 sometimes inaccurate w
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MORE MATH 141 15 POWER CONTROL 10
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MORE MATH 143 6.4.3 More approximat
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THE LITERATURE 145 EDGE is an evolu
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PROBLEMS 147 the channel to minimum
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PROBLEMS 149 c) Suppose we know «o
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152 ADVANCED TOPICS detecting the w
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154 ADVANCED TOPICS Table 7.2 Examp
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156 ADVANCED TOPICS Let's look at t
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158 ADVANCED TOPICS Thus, for MAPSD
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160 ADVANCED TOPICS Table 7.6 Examp
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162 ADVANCED TOPICS If we assume no
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164 ADVANCED TOPICS Now we can focu
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166 ADVANCED TOPICS Notice we used
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168 ADVANCED TOPICS to achieve spat
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170 ADVANCED TOPICS c) What are the
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CHAPTER 8 PRACTICAL CONSIDERATIONS
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MORE DETAILS 175 8.2 MORE DETAILS I
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MORE DETAILS 177 One extreme is to
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THE MATH 179 8.3.1 Time-invariant c
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THE MATH 181 With recursive channel
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MORE PRACTICAL ASPECTS 183 timing).
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THE LITERATURE 185 8.6 THE LITERATU
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PROBLEMS 187 a) Draw the new receiv
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APPENDIX A SIMULATION NOTES Perform
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FADING CHANNELS 191 where JA = A 2
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APPENDIX B NOTATION Channel Equaliz
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NOTATION 195 Variable Meaning p(t)
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198 REFERENCES [Ari98] [Ari99] [Ari
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200 REFERENCES [Bra91] W. R. Braun
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202 REFERENCES [Div98] [Dou95] [Due
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204 REFERENCES [Gon68] R. A. Gonsal
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206 REFERENCES [Kaa94] [Kai60] V.-P
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208 REFERENCES [ManOl] [Mar73] [Mar
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210 REFERENCES [Pic95] [P0088] [Pri
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212 REFERENCES [Sto93] [Sto96] [Sui
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214 REFERENCES [Wan06a] T. Wang, J.