Wiley 2011 新书 《Channel Equalization for Wireless Communications》

Lecky

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
Problems 27
2 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
3 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
4.1.1 Minimum mean-square error
4.2  More Details
4.2.1 Minimum mean-square error solution
4.2.2  Maximum SINR solution
4.2.3 General dispersive scenario
4.2.4 General MIMO scenario
4.3 The Math
4.3.1 MMSE solution
4.3.2 ML solution
4.3.3 Output SINR
4.3.4 Other design criteria
4.3.5 Fractionally spaced linear equalization
4.3.6 Performance results
4.4 More Math
4.4.1 ZF solution
4.4.2 MMSE solution
4.4.3 ML solution
4.4.4 Other forms for the CDM case
4.4.5 Other forms for the OFDM case
4.4.6 Simpler models
4.4.7 Block and sub-block forms
4.4.8 Group linear equalization
4.5 An Example
4.6 The Literature
Problems
5 MMSE and ML Decision Feedback Equalization 99
5.1 The Idea 99
5.2 More Details 101
5.3 The Math 104
5.3.1 MMSE solution 104
5.3.2 ML solution 106
5.3.3 Output SINR 106
5.3.4 Fractionally spaced DFE 106
5.3.5 Performance results 106
5.4 More Math 108
5.4.1 MMSE solution 108
5.4.2 ML solution 109
5.4.3 Simpler models 109
5.4.4 Block and sub-block forms 109
5.4.5 Group decision feedback equalization 110
5.5 An Example 110
5.6 The Literature 110
Problems 112
6 Maximum Likelihood Sequence Detection 115
6.1 The Idea 115
6.2 More Details 117
6.3 The Math 120
6.3.1 The Viterbi algorithm 120
6.3.2 SISO TDM scenario 125
6.3.3 Given statistics 130
6.3.4 Fractionally spaced MLSD 130
6.3.5 Approximate forms 130
6.3.6 Performance results 131
6.4 More Math 138
6.4.1 Block form 142
6.4.2 Sphere decoding 142
6.4.3 More approximate forms 143
6.5 An Example 144
6.6 The Literature 145
Problems 147
7 Advanced Topics 151
7.1 The Idea 151
7.1.1 MAP symbol detection 151
7.1.2 Soft information 153
7.1.3 Joint demodulation and decoding 155
7.2 More Details 156
7.2.1 MAP symbol detection 156
7.2.2 Soft information 157
7.2.3 Joint demodulation and decoding 160
7.3 The Math 160
7.3.1 MAP symbol detection 160
7.3.2 Soft information 166
7.3.3 Joint demodulation and decoding 167
7.4 More Math 167
7.5 An Example 167
7.6 The Literature 168
7.6.1 MAP symbol detection 168
7.6.2 Soft information 168
7.6.3 Joint demodulation and decoding 169
Problems 169
8 Practical Considerations 173
8.1 The Idea 173
8.2 More Details 175
8.2.1 Parameter estimation 175
8.2.2 Equalizer selection 176
8.2.3 Radio aspects 177
8.3 The Math 178
8.3.1 Time-invariant channel and training sequence 179
8.3.2 Time-varying channel and known symbol sequence 180
8.3.3 Time-varying channel and partially known symbol
sequence 181
8.3.4 Per-survivor processing 182
8.4 More practical aspects 182
8.4.1 Acquisition 182
8.4.2 Timing 182
8.4.3 Doppler 183
8.4.4 Channel Delay Estimation 183
8.4.5 Pilot symbol and traffic symbol powers 184
8.4.6 Pilot symbols and multi-antenna transmission 184
8.5 An Example 184
8.6 The Literature 185
Problems 185

hnwulei

thanks！！！！！！！！！！！！！！！！！！！！！！！

auspiciouschen

太牛...

qianhe

很好的书,谢谢分享.

heinzmann

非常好的书。

apteye

谢谢分享.

sart

非常感谢楼主分享，好书，学习了。

matrix_er

新书，不错

uubirdloong

多谢楼主，正在做关于均衡的东西。参考一下。

yousee

该书的组织和视角是比较现代的，对老大难问题“信道”给出了一定的总结:
前半部分LMMSE(Adaptive) fractional DFE 和 MLSD 是相对传统的时域均衡技术，已经在众多的系统DVB-C, Wifi, GSM 得到了广泛应用，遗憾得是细节严重不足，只给个图说2句话就没了。
后半部分则开始转向频域处理，比如OFDM，MIMO，只给个介绍开个头就啥也没有了。对MIMO球形译码检测，频域均衡 的 介绍再补充一下就更好了。