文件名称：Channel Estimation for Gigabit Multi-user MIMO-OFDM Systems MIMO-OFDM Systems
文件大小：2.37MB
文件格式：PDF
更新时间：2015-06-05 05:04:31
Multi-user MIMO-OFDM Channel Estimation 信道估计
《Channel Estimation for Gigabit Multi-user MIMO-OFDM Systems MIMO-OFDM Systems》为国外一博士论文，内附详细MATLAB代码。文章结构如下： 1 An Introduction to MIMO-OFDM Systems 3 1.1 Predicting the Emerging and Future Wireless Communications Technologies . . . . . . . . . . . . . . . . .. . . . . . . . . . . 4 1.2 Chapter Summary . . . . . . . . . . . . . . . . . . . . . . . . 6 1.3 The MIMO-OFDM System Model . . . . . . . . . . . . . . . . . 9 1.4 The MIMO-OFDM Air Interface . . . . . . . . . . . . . . . . . 13 1.4.1 IQ Constellation Mapping . . . . . . . . . . . . . . . . . 13 1.4.2 Digital multitone/multi-carrier modulation . . . . . . . . 17 1.4.3 Maximum Likelihood Detection . . . . . . . . . . . . . . 19 1.5 The MIMO-OFDM Mapping/De-mapping Function . . . . . . . 23 1.5.1 Space-Frequency Coding . . . . . . . . . . . . . . . . . . 24 1.5.2 Spatial Multiplexing . . . . . . . . . . . . . . . . . . . . 27 1.6 Multi-user MIMO-OFDM . . . . . . . . . . . . . . . . . . . . . 29 1.7 Research Objectives of the Thesis . . . . . . . . . . . . . . .31 2 The Wireless Channel 33 2.1 Multipath Propagation . . . . . . . . . . . . . . . . . . . . 35 2.2 Tapped-Delay-Line System Model . . . . . . . . . . . . . . . . 37 2.2.1 Statistical Model of a Multipath Channel . . . . . . . . . 41 2.2.2 Bandlimited transmission . . . . . . . . . . . . . . . . . 45 2.2.3 Rayleigh Fading Channels . . . . . . . . . . . . . . . . . 48 2.3 Saleh-Valenzuela channel Model . . . . . . . . . . . . . . . . 50 2.4 Correlation of the channel gain parameters of MIMO antennas . 55 2.5 Chapter Summary . . . . . . . . . . . . . . . . . . . . . . . 57 3 Coherent Detection for MIMO-OFDM Systems 61 3.1 OFDM Equalization . . . . . . . . . . . . . . . . . . . . . . 62 3.2 SISO-OFDM Channel Estimation . . . . . . . . . . . . . . . . . 68 3.2.1 One Dimensional Channel Estimation . . . . . . . . . . . 70 3.2.2 Two Dimensional Channel estimation . . . . . . . . . . . 73 3.3 MIMO-OFDM Channel Estimation . . . . . . . . . . . . . . . . 77 3.3.1 Least Squares Solution . . . . . . . . . . . . . . . . . . . 78 3.3.2 Time Domain LS Channel Estimation . . . . . . . . . . . 79 3.3.3 Performance of the Channel Estimator . . . . . . . . . . 84 3.4 Chapter Summary . . . . . . . . . . . . . . . . . . . . . . . 86 4 Reduced Parameter Channel Estimation 89 4.1 CSI Frequency Correlations . . . . . . . . . . . . . . . . . . 90 4.1.1 OFDM Frequency Correlations . . . . . . . . . . . . . . 91 4.1.2 E®ects of Multipath on Frequency correlations . . . . . . 92 4.2 RP-CSI Basis Functions . . . . . . . . . . . . . . . . . . . . 93 4.2.1 Wavelet Basis . . . . . . . . . . . . . . . . . . . . . . . 94 4.2.2 Principal Component Analysis Basis . . . . . . . . . . . 98 4.3 The Proposed Method . . . . . . . . . . . . . . . . . . . . 102 4.3.1 OFDM Symbol based correlations . . . . . . . . . . . . . 102 4.3.2 OFDM sub-symbol based correlations . . . . . . . . . . . 108 4.4 Chapter Summary . . . . . . . . . . . . . . . . . . . . . . 113 5 Reduced Parameter Channel State Information Analysis 116 5.1 The Lower Bound for MSE in Channel Estimate . . . . . . . . . 117 5.2 RP-CSI Simulation Results . . . . . . .. . . . . . . . . . . 121 5.2.1 Simulation Results for L = 4 . . . . . . . . . . . . . . . 121 5.2.2 Simulation Results for L = 8 . . . . . . . . . . . . . . . 124 5.2.3 Simulation Results for L = 16 . . . . . . . . . . . . . . . 126 5.3 OFDM Sub-symbol based MIMO-OFDM channel Estimation . . 128 5.3.1 Orthogonal Training Sequences for channel estimation . . 129 5.3.2 OFDM sub-symbol based MU-MIMO-OFDM channel estimation . 132 5.4 Chapter Summary . . . . . . . . . . . . . . . . . . . . . . . 139 6 Time Varying Channels 142 6.1 Clarke's Model . . . . . . . . . . . . . . . . . . . . . . . 143 6.2 Slepian Basis Expansion . . . . . . . . . . . . . . . . . . . 147 6.3 Kalman ¯lter Tracking . . . . . . . . . . . . . . . . . . . . 155 6.3.1 Deriving the Kalman Filter Process Model . . . . . . . . 158 6.3.2 Deriving the Kalman Filter Measurement Model . . . . . 162 6.4 Conclusions & Future Work . . . . . . . . . . . . . . . . 165 E MATLAB CODE 203 E.1 Saleh-Valenzuela Channel Model . . . . . . . . . . . . . . . 203 E.2 Wiener Filter Implementation . . . . . . . . . . . . . . . . 208 E.3 RP-CSI Estimator . . . . . . . . . . . . . . . . . . . . . . 218 E.4 Wavelet Basis . . . . . . . . . . . . . . . . . . . . . . . 226 E.5 Slepian/ Discrete Prolate Spheroidal Sequences . . . .229 E.6 Orthogonal Training Sequence Training . . . . . . . . . . . . 230 E.7 Kalman Filter . . . . . . . . . . . . . . . . . . . . . . . 233