[免费]关于无线通讯Space-time processing and equalization的博士论文

Challenger

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这是摘要：
In this thesis several aspects of space-time processing and equalization for wireless
communications are treated. We discuss several di erent methods of improving
estimates of space-time channels, such as temporal parametrization, spatial parametrization, reduced rank channel estimation, bootstrap channel estimation,
and joint estimation of an FIR channel and an AR noise model. In wireless communication the signal is often subject to intersymbol interference as well as interference from other users. We here discuss space-time decision feedback equalizers and space-time maximum likelihood sequence estimators, which can alleviate the impact of these factors. In case the wireless channel does not experience a large amount of coupled delay and angle spread, sucient performance may be obtained by an equalizer with a less complex structure. We therefore discuss various reduced complexity equalizers and symbol sequence estimators. We also discuss re-estimating the channel and/or re-tuning the equalizer with a bootstrap method using estimated symbols. With this method we can improve the performance of the channel estimation, the qualization, and the interferer suppression. This method can also be used to suppress asynchronous interferers. When equalizers and symbol detection algorithms are designed based on estimated channels we need to consider how errors in the estimated channels, or errors due to time variations, a ect the performance of the equalizer or symbol detector. We show that equalizers tuned based on ordinary least squares estimated channels exhibit a degree of self-robusti cation, which automatically compensates for potential errors in the channel estimates.
这是目录：
Contents
1 Space-Time Processing in Wireless Communication 1
1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 Outline of Space-Time Processing Schemes . . . . . . . . . . 3
1.3 Architecture Based Classi cation . . . . . . . . . . . . . . . . 4
1.3.1 Link Structure . . . . . . . . . . . . . . . . . . . . . . 4
1.3.2 Channel Reuse . . . . . . . . . . . . . . . . . . . . . . 6
1.3.3 Multiple Access . . . . . . . . . . . . . . . . . . . . . . 10
1.4 Algorithm Based Classi cation . . . . . . . . . . . . . . . . . 10
1.4.1 Channel Estimation Algorithms . . . . . . . . . . . . . 11
1.4.2 TDMA Receive Algorithms . . . . . . . . . . . . . . . 15
1.4.3 CDMA Receive Algorithms . . . . . . . . . . . . . . . 19
1.4.4 Transmit Algorithms . . . . . . . . . . . . . . . . . . . 20
1.5 In
uence of the Channel on Space-Time Processing . . . . . . 21
1.5.1 Doppler Spread . . . . . . . . . . . . . . . . . . . . . . 22
1.5.2 Delay Spread . . . . . . . . . . . . . . . . . . . . . . . 22
1.5.3 Angle Spread . . . . . . . . . . . . . . . . . . . . . . . 23
1.5.4 Di erent Realizations of a Multi-Channel Receiver . . 24
1.6 Some Notes on Special Notation . . . . . . . . . . . . . . . . 25
xi
xii Contents
2 Channel Estimation 29
2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
2.2 Directly Parametrized FIR Channel Estimation . . . . . . . . 36
2.3 Temporal Parametrization . . . . . . . . . . . . . . . . . . . . 38
2.3.1 Channel Modeling . . . . . . . . . . . . . . . . . . . . 39
2.3.2 Channel Estimation . . . . . . . . . . . . . . . . . . . 43
2.3.3 Example . . . . . . . . . . . . . . . . . . . . . . . . . . 46
2.3.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . 54
2.4 Temporal Parametrization of Multi-User Channels . . . . . . 55
2.4.1 Channel Estimation . . . . . . . . . . . . . . . . . . . 56
2.4.2 Examples . . . . . . . . . . . . . . . . . . . . . . . . . 59
2.4.3 Summary . . . . . . . . . . . . . . . . . . . . . . . . . 60
2.5 Spatial Parametrization . . . . . . . . . . . . . . . . . . . . . 61
2.5.1 Least Squares Channel Estimation . . . . . . . . . . . 62
2.5.2 Coherent Decoupled Maximum Likelihood Channel
Estimation . . . . . . . . . . . . . . . . . . . . . . . . 63
2.5.3 Simulation Study . . . . . . . . . . . . . . . . . . . . . 67
2.5.4 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . 74
2.6 Reduced Rank Channel Estimation . . . . . . . . . . . . . . . 75
2.6.1 Channel Model . . . . . . . . . . . . . . . . . . . . . . 76
2.6.2 Maximum-Likelihood Reduced-Rank Channel
Estimation . . . . . . . . . . . . . . . . . . . . . . . . 78
2.6.3 Signal Subspace Projection . . . . . . . . . . . . . . . 79
2.6.4 Estimation of the Spatial Noise-plus-Interference Covariance
Matrix . . . . . . . . . . . . . . . . . . . . . . 83
2.6.5 Simulations . . . . . . . . . . . . . . . . . . . . . . . . 83
2.6.6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . 85
2.7 Bootstrap Channel Estimation . . . . . . . . . . . . . . . . . 86
Contents xiii
2.8 Estimation of Noise plus Interference MA Spectrum . . . . . 89
2.9 Joint FIR Channel and AR Noise Model Estimation . . . . . 91
2.9.1 Joint FIR Channel and AR Noise Model Estimation . 91
2.9.2 Motivation for Joint FIR Channel and AR Noise Model
Estimation . . . . . . . . . . . . . . . . . . . . . . . . 94
2.9.3 Reduced Complexity AR Noise Modeling . . . . . . . 96
2.A Appendix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98
2.A.1 Linearization of the Modulation in GSM . . . . . . . . 98
2.A.2 Modulation in GSM . . . . . . . . . . . . . . . . . . . 98
2.A.3 Linearization without Receiver Filter . . . . . . . . . . 99
2.A.4 Linearization with a Receiver Filter . . . . . . . . . . 100
2.B Least Squares for FIR Channel and AR Noise Estimation . . 103
3 Space-Time Decision Feedback Equalization 105
3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105
3.2 Optimal Space-Time Decision Feedback Equalizers . . . . . . 111
3.2.1 Optimal Space-Time DFE for ARMA Channels with
ARMA Noise . . . . . . . . . . . . . . . . . . . . . . . 111
3.2.2 Optimal Space-Time DFE for FIR Channels with AR
Noise . . . . . . . . . . . . . . . . . . . . . . . . . . . 115
3.2.3 Optimal Fixed Order Space-Time FIR-DFE for a FIR
Channel with Colored Noise . . . . . . . . . . . . . . . 118
3.2.4 Multidimensional Matched Filter DFE . . . . . . . . . 124
3.3 Some Tuning Options . . . . . . . . . . . . . . . . . . . . . . 126
3.3.1 Directly Tuned Decision Feedback Equalizer (D-DFE) 127
3.3.2 Indirectly Tuned DFE (I-DFE) . . . . . . . . . . . . . 128
3.3.3 Indirectly Tuned DFE with Spatial-Only Interference
Cancellation (IS-DFE) . . . . . . . . . . . . . . . . . . 130
xiv Contents
3.3.4 Indirectly Tuned DFE with an AR Noise Model (ARDFE)
. . . . . . . . . . . . . . . . . . . . . . . . . . . 131
3.3.5 Indirectly Tuned MMF-DFE with Spatial-Only Interference
Cancellation (IS-MMF-DFE) . . . . . . . . . . 132
3.3.6 Indirectly Tuned MMF-DFE with an AR Noise Model
(AR-MMF-DFE) . . . . . . . . . . . . . . . . . . . . . 134
3.4 Simulations . . . . . . . . . . . . . . . . . . . . . . . . . . . . 136
3.5 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 140
3.A Appendix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 142
3.A.1 Derivation of the Optimal Space-Time DFE. . . . . . 142
3.A.2 Derivation of the Optimal Space-Time DFE for AR
Noise. . . . . . . . . . . . . . . . . . . . . . . . . . . . 150
3.A.3 Fixed Order FIR-DFE Wiener Equations in Channel
Parameters . . . . . . . . . . . . . . . . . . . . . . . . 158
4 Space-Time ML Sequence Estimation 161
4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 161
4.2 Di erent Implementations of the Multi-Channel MLSE . . . . 163
4.2.1 Channel Description . . . . . . . . . . . . . . . . . . . 163
4.2.2 Log-Likelihood Metric and Noise Whitening Approach 163
4.2.3 Multi-Dimensional Matched Filter Approach . . . . . 165
4.3 Computational Complexity . . . . . . . . . . . . . . . . . . . 169
4.4 Tuning the Multi-Dimensional Matched Filter . . . . . . . . . 170
4.4.1 Direct MMSE Tuning . . . . . . . . . . . . . . . . . . 170
4.4.2 Indirect MMF Tuning . . . . . . . . . . . . . . . . . . 173
4.4.3 Indirect MMSE Tuning . . . . . . . . . . . . . . . . . 174
4.5 Simulations . . . . . . . . . . . . . . . . . . . . . . . . . . . . 175
4.6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 176
4.A Appendix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 180
Contents xv
4.A.1 Deriving the MF Metric from the LL Metric . . . . . . 180
5 Reduced Complexity Space-Time Equalization 185
5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 185
5.2 Spatial Beamforming . . . . . . . . . . . . . . . . . . . . . . . 188
5.2.1 SMI Beamforming . . . . . . . . . . . . . . . . . . . . 188
5.2.2 SMI Beamforming with Variable Reference Signal . . 190
5.3 Reduced Rank Equalization . . . . . . . . . . . . . . . . . . . 197
5.3.1 Reduced Rank Channel Approximation . . . . . . . . 198
5.3.2 Reduced Rank MLSE . . . . . . . . . . . . . . . . . . 206
5.3.3 Reduced Rank DFE . . . . . . . . . . . . . . . . . . . 210
5.3.4 Complexity . . . . . . . . . . . . . . . . . . . . . . . . 217
5.3.5 Experiments on Measured Data . . . . . . . . . . . . . 218
5.3.6 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . 220
5.4 Reduced Rank Tuning . . . . . . . . . . . . . . . . . . . . . . 220
5.4.1 Simulations . . . . . . . . . . . . . . . . . . . . . . . . 227
5.A Appendix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 229
5.A.1 Solving for c given v . . . . . . . . . . . . . . . . . . . 229
6 Bootstrap Equalization and Interferer Suppression 231
6.1 Bootstrap Co-Channel Interference Cancellation . . . . . . . 232
6.1.1 Experiments on Measured Data . . . . . . . . . . . . . 233
6.1.2 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . 237
6.2 Suppression of Asynchronous Interferers . . . . . . . . . . . . 239
6.2.1 Conservative Initial Detection . . . . . . . . . . . . . . 239
6.2.2 Bootstrap Equalization Utilizing Adjacent Frames . . 252
7 Robust Equalization 257
xvi Contents
7.1 Robust Scalar Decision Feedback Equalization . . . . . . . . . 259
7.1.1 Model and Filter Structure . . . . . . . . . . . . . . . 260
7.1.2 Filter Design Equations . . . . . . . . . . . . . . . . . 262
7.1.3 The Class of Equalizers . . . . . . . . . . . . . . . . . 267
7.2 Robustness Against Decision Errors . . . . . . . . . . . . . . 268
7.3 Robustness Against Fading . . . . . . . . . . . . . . . . . . . 270
7.3.1 Model and Filter Structure . . . . . . . . . . . . . . . 270
7.3.2 Filter Design Equations . . . . . . . . . . . . . . . . . 271
7.3.3 Example . . . . . . . . . . . . . . . . . . . . . . . . . . 274
7.4 Robustness of Space-Time Equalizers . . . . . . . . . . . . . . 279
7.4.1 Channel Model . . . . . . . . . . . . . . . . . . . . . . 282
7.4.2 Channel Estimation Errors . . . . . . . . . . . . . . . 282
7.4.3 Spatial Robustness of the Space-Time FIR-DFE . . . 284
7.4.4 Spatial Robustness of the MMF-MLSE and MMF-DFE286
7.A Appendix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 288
7.A.1 Robust MMSE Filtering . . . . . . . . . . . . . . . . . 288
7.A.2 Derivation of a Spatially Robust DFE . . . . . . . . . 296
7.A.3 Expectation of Channel Error Spectrum . . . . . . . . 302

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