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E-raamat: Low Complexity MIMO Receivers

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  • Formaat: PDF+DRM
  • Ilmumisaeg: 13-Mar-2014
  • Kirjastus: Springer International Publishing AG
  • Keel: eng
  • ISBN-13: 9783319049847
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Low Complexity MIMO ReceiversSuurem pilt
  • Formaat: PDF+DRM
  • Ilmumisaeg: 13-Mar-2014
  • Kirjastus: Springer International Publishing AG
  • Keel: eng
  • ISBN-13: 9783319049847
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Multiple-input multiple-output (MIMO) systems can increase the spectral efficiency in wireless communications. However, the interference becomes the major drawback that leads to high computational complexity at both transmitter and receiver. In particular, the complexity of MIMO receivers can be prohibitively high. As an efficient mathematical tool to devise low complexity approaches that mitigate the interference in MIMO systems, lattice reduction (LR) has been widely studied and employed over the last decade. The co-authors of this book are world's leading experts on MIMO receivers, and here they share the key findings of their research over years. They detail a range of key techniques for receiver design as multiple transmitted and received signals are available. The authors first introduce the principle of signal detection and the LR in mathematical aspects. They then move on to discuss the use of LR in low complexity MIMO receiver design with respect to different aspects, including uncoded MIMO detection, MIMO iterative receivers, receivers in multiuser scenarios, and multicell MIMO systems.
1 Introduction
1(4)
2 Signal Processing at Receivers: Detection Theory
5(24)
2.1 Principles of Hypothesis Testing
5(1)
2.2 Maximum a Posteriori Probability Hypothesis Test
6(5)
2.3 Baysian Hypothesis Test
11(1)
2.4 Maximum Likelihood Hypothesis Test
12(1)
2.5 Likelihood Ratio-Based Hypothesis Test
13(2)
2.6 Neyman--Pearson Lemma
15(2)
2.7 Detection of Symmetric Signals
17(5)
2.7.1 Error Probability
18(1)
2.7.2 Bound Analysis
19(3)
2.8 Binary Signal Detection
22(4)
2.9 Detection of M-ary Signals
26(2)
2.10 Concluding Remarks
28(1)
3 MIMO Detection: Vector Space Signal Detection
29(22)
3.1 Signals in Vector Space
29(2)
3.2 Vector Space Signal Detection
31(2)
3.3 Random Signal Detection
33(8)
3.3.1 Likelihood Ratio Based Random Signal Detection
33(1)
3.3.2 Signal Detection with Random Amplitude and Phase
34(3)
3.3.3 Random Gaussian Vector Signal Detection
37(2)
3.3.4 Pairwise Error Probability
39(2)
3.4 MIMO Detection
41(8)
3.4.1 ML Detection
43(1)
3.4.2 Linear Detection
44(2)
3.4.3 Performance Analysis
46(3)
3.5 Concluding Remarks
49(2)
4 Successive Interference Cancellation-Based MIMO Detection
51(40)
4.1 SIC Detection
51(11)
4.1.1 QR Factorization
51(1)
4.1.2 ZF-SIC
52(3)
4.1.3 MMSE-SIC
55(1)
4.1.4 Ordering
56(2)
4.1.5 Performance Analysis
58(4)
4.2 List-Based Detection
62(11)
4.2.1 Detection Algorithms
63(2)
4.2.2 Ordering
65(3)
4.2.3 Sub-detectors
68(3)
4.2.4 Performance Analysis
71(2)
4.3 SIC-Based MAP Detection
73(17)
4.3.1 Partial MAP Detection
75(5)
4.3.2 Partial MAP-Based List Detection
80(8)
4.3.3 Performance Analysis
88(2)
4.4 Concluding Remarks
90(1)
5 Lattice Reduction-Based MIMO Detection
91(52)
5.1 Lattice Reduction-Based Detection
91(32)
5.1.1 MIMO Systems with Lattice
91(2)
5.1.2 Lattice Reduction-Based MIMO Detection
93(6)
5.1.3 Two-Basis Lattice Reduction
99(5)
5.1.4 Two-Basis Gaussian Lattice Reduction
104(5)
5.1.5 LLL and CLLL Algorithms
109(6)
5.1.6 Performance Analysis
115(8)
5.2 Lattice Reduction-Based SIC-List Detection
123(14)
5.2.1 Detection Algorithm
124(2)
5.2.2 Lattice Reduction-Based Subdetection
126(1)
5.2.3 List Generation in the LR Domain
127(2)
5.2.4 Impact of List Length
129(3)
5.2.5 Column Reordering Criteria
132(3)
5.2.6 Performance Analysis
135(2)
5.3 Lattice Reduction-Based Partial MAP-List Detection
137(4)
5.3.1 Detection Algorithm
138(1)
5.3.2 Performance Analysis
139(2)
5.4 Concluding Remarks
141(2)
6 MIMO Iterative Receivers
143(32)
6.1 Convolutional Codes
143(7)
6.1.1 Convolutional Encoders
144(2)
6.1.2 Decoding Approaches for Convolutional Codes
146(4)
6.2 Turbo Principle and BICM-ID
150(7)
6.2.1 Structure and Operation of BICM-ID
150(2)
6.2.2 Performance Analysis of BICM-ID Using EXIT Chart
152(5)
6.3 MIMO Iterative Receivers with Optimal MIMO Detection
157(4)
6.3.1 BICM for MIMO Channels
158(1)
6.3.2 Structure of Iterative Receivers
158(3)
6.4 MIMO Iterative Receivers with Suboptimal MIMO Detection
161(13)
6.4.1 List-Sphere Decoding
161(5)
6.4.2 Monte Carlo Markov Chain Sampling
166(4)
6.4.3 MMSE-SC
170(1)
6.4.4 Numerical Results
171(3)
6.4.5 BER Performance
174(1)
6.5 Concluding Remarks
174(1)
7 Bit-Wise MIMO-BICM-ID Using Lattice Reduction
175(20)
7.1 LR-Based IDD Using Bit-Wise Filtering
175(8)
7.1.1 LR-Based Detection
176(2)
7.1.2 LR-Based Bit-Wise MMSE Filtering
178(3)
7.1.3 List Generation Using Integer Perturbation
181(2)
7.1.4 List Generation Using Quantization
183(1)
7.2 Channel Decomposition for Large Systems
183(3)
7.3 Complexity Analysis
186(2)
7.3.1 Detection Complexity
186(1)
7.3.2 Reduction Complexity
187(1)
7.3.3 Decomposition Complexity
188(1)
7.4 Numerical Results
188(5)
7.4.1 Comparison of Bit-LR 1 and Bit-LR 2
189(1)
7.4.2 Complexity Comparison
190(1)
7.4.3 Convergence Analysis
191(1)
7.4.4 BER Performance
192(1)
7.5 Concluding Remarks
193(2)
8 Randomized Sampling-Based MIMO Iterative Receivers
195(20)
8.1 System Model
195(2)
8.2 LR-Based SIC Detection
197(1)
8.3 LR-Based IDD Using Randomized Sampling
198(7)
8.3.1 Gaussian Approximation in the LR Domain
198(1)
8.3.2 Randomized List Generation
199(5)
8.3.3 Complex-Valued List Generation
204(1)
8.4 Complexity Analysis
205(2)
8.5 Numerical Results
207(6)
8.6 Concluding Remarks
213(2)
9 Iterative Channel Estimation and Detection
215(18)
9.1 EM Algorithm
216(2)
9.2 Iterative Channel Estimation and Detection
218(2)
9.2.1 System Model
218(1)
9.2.2 EM-Based Semi-blind Channel Estimation
219(1)
9.23 EM-Based ICED Algorithm
220(1)
9.3 LR-Based ICED
220(5)
9.3.1 LR-Based Detection within ICED
220(2)
9.3.2 Complexity-Efficient LR-ICED
222(3)
9.4 LR-ICED Over Slow Fading Channels
225(3)
9.5 Numerical Results
228(2)
9.6 Concluding Remarks
230(3)
10 Multiuser and Multicell MIMO Systems: The Use of Lattice Reduction
233(52)
10.1 Single User Selection
234(12)
10.1.1 Maximum Mutual Information
235(1)
10.1.2 User Selection for ML Detection
236(1)
10.1.3 User Selection for Linear Detection
237(1)
10.1.4 User Selection for LR-Based Detection
238(3)
10.1.5 Performance Analysis
241(5)
10.2 Multiple-User Selection
246(22)
10.2.1 Combinatorial User Selection
248(3)
10.2.2 Greedy User Selection
251(7)
10.2.3 Performance Analysis
258(10)
10.3 Lattice-Based Interference Alignment for Multiuser MIMO Systems
268(6)
10.3.1 System Model
269(1)
10.3.2 Lattice-Based Interference Alignment
269(2)
10.3.3 Joint Signal Detection and Precoder Design with IA-L
271(2)
10.3.4 Numerical Results
273(1)
10.4 Multichannel Sharing and Joint Detection in Downlink Multicell OFDMA Systems
274(8)
10.4.1 System Models
275(2)
10.4.2 Joint Detection LR-Based Detectors
277(1)
10.4.3 LR-Based Joint Detection Over Subspace
278(3)
10.4.4 Numerical Results
281(1)
10.5 Concluding Remarks
282(3)
About the Authors 285(2)
References 287(6)
Index 293
Dr. Lin Bai is an Associate Professor at School of Electronic and Information Engineering, Beihang University (University of Aeronautics and Astronautics, BUAA), Beijing, China.

Prof. Jinho Choi researches and teaches at Gwangju Institute of Science and Technology in South Korea.

Prof. Quan Yu is a Research Fellow at the Institute of China Electronic System Engineering Corporation. He is an Academician of Chinese Academy of Engineering (CAE).
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