mohatta2015.pdf

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CONTENTS List of Figures List of Tables Preface Acknowledgments Acronyms xv xix xxi xxiii xxv 1 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
Page 2 and 3: Channel Equalization for Wireless C
Page 4 and 5: To my colleagues at Ericsson
Page 8 and 9: CONTENTS XI 4.2 4.3 4.4 4.5 4.6 4.1
Page 10 and 11: CONTENTS XIII 8 Practical Considera
Page 12 and 13: xvi LIST OF FIGURES 2.2 Matched fil
Page 14 and 15: XviÜ LIST OF FIGURES 6.18 Scatter
Page 16 and 17: XX Prologue Alice was nervous. Woul
Page 18 and 19: XXÜ PREFACE is introduced as neede
Page 20 and 21: ACRONYMS AC A/D ADC AMLD AMPS ASK A
Page 22 and 23: ML MLD MLPD MLSD MLSE MRC MSE MSB O
Page 24 and 25: 2 INTRODUCTION Table 1.1 Possible m
Page 26 and 27: 4 INTRODUCTION s o S 1 8 2 S 3 S 0
Page 28 and 29: 6 INTRODUCTION A block diagram of t
Page 30 and 31: 8 INTRODUCTION Figure 1.7 QPSK. tra
Page 32 and 33: 10 INTRODUCTION phases (phase modul
Page 34 and 35: 12 INTRODUCTION rollo« 0.22 0.8 Ί
Page 36 and 37: 14 INTRODUCTION is the "channel" re
Page 38 and 39: 16 INTRODUCTION 1.4 MORE MATH In th
Page 40 and 41: 18 INTRODUCTION For K = 1 (order 0)
Page 42 and 43: 20 INTRODUCTION The K = 4 main bloc
Page 44 and 45: 22 INTRODUCTION A sample of the noi
Page 46 and 47: 24 INTRODUCTION The receiver may ha
Page 48 and 49: 26 INTRODUCTION 1.5.1 Reference sys
Page 50 and 51: 28 INTRODUCTION a) What most likely
Page 53 and 54: CHAPTER 2 MATCHED FILTERING In this
Page 55 and 56: MORE DETAILS 33 2.2 MORE DETAILS So
Page 57 and 58:
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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MORE MATH 47 10" 1 Odeg 90 deg X 18
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MORE MATH 49 With despread-level Ra
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MORE MATH 51 Figure 2.6 OFDM exampl
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MORE MATH 53 2.4.3 MF in colored no
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PROBLEMS 55 period) is examined in
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CHAPTER 3 ZERO-FORCING DECISION FEE
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MORE DETAILS 59 1/c r m —► H i
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MORE DETAILS 61 where «i = m - (d/
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MORE MATH 63 Because there is no IS
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MORE MATH 65 In the later chapter o
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PROBLEMS 67 b) What is the detected
Page 91 and 92:
CHAPTER 4 LINEAR EQUALIZATION With
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THE IDEA 71 estimate of symbol s 2
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THE IDEA 73 Notice that £2 (MSE) d
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MORE DETAILS 75 The first term is t
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MORE DETAILS 77 To obtain the unity
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THE MATH 79 and SINR becomes w T h
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THE MATH 81 To obtain the MMSE solu
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THE MATH 83 where *m n = ν ^ ^ ^ '
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THE MATH 85 power is much larger th
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MORE MATH 87 IQ' 1 OC 111 ω 10 2 1
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MORE MATH 89 values of h k m (f) fr
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MORE MATH 91 where C(d(),d 0 ) . C(
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AN EXAMPLE 93 between z and s. This
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PROBLEMS 95 Another aspect of cocha
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PROBLEMS 97 The math 4.11 Consider
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100 MMSE AND ML DECISION FEEDBACK E
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Page 126 and 127:
Page 128 and 129:
Page 130 and 131:
Page 132 and 133:
Page 134 and 135:
Page 137 and 138:
CHAPTER 6 MAXIMUM LIKELIHOOD SEQUEN
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MORE DETAILS 117 r 2 Ht, r, fcl + f
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MORE DETAILS 119 Figure 6.3 MLSD tr
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THE MATH 121 legs of the journey is
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THE MATH 123 Figure 6.8 MLSD trelli
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THE MATH 125 We would then identify
Page 149 and 150:
THE MATH 127 Expanding the square,
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THE MATH 129 Consider the matched f
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THE MATH 131 6.3.5.1 M-algorithm Wi
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THE MATH 133 results, DFE and MLSD
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THE MATH 135 IQ" 1 oc LU CD 10 2 10
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THE MATH 137 sometimes inaccurate w
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MORE MATH 139 POWER CONTROL effecti
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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
Page 171:
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
Page 195 and 196:
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
Page 226 and 227:
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
Page 232 and 233:
210 REFERENCES [Pic95] [P0088] [Pri
Page 234 and 235:
212 REFERENCES [Sto93] [Sto96] [Sui
Page 236 and 237:
214 REFERENCES [Wan06a] T. Wang, J.
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