Chapter
1.5 MIMO in wireless networks
1.5.1 MIMO in cellular networks
1.5.2 MIMO in ad hoc networks
1.6 MIMO in wireless standards
1.7 Organization of the book and future challenges
1.8 Bibliographical notes
2 Capacity limits of MIMO systems
2.2 Mutual information and Shannon capacity
2.2.1 Mathematical definition of capacity
2.2.2 Time-varying channels
2.2.3 Multi-user channels
2.3.1 Channel and side information model
2.3.2 Constant MIMO channel capacity
2.3.3 Fading MIMO channel capacity
Capacity with perfect CSIT and perfect CSIR
Capacity with perfect CSIR and CDIT: ZMSW model
Capacity with perfect CSIR and CDIT: CMI, CCI and QCI models
Multiple-input single-output channels
Multiple-input multiple-output channels
Capacity with CDIT and CDIR: ZMSW model
Capacity with CDIT and CDIR: CCI model
Capacity with correlated fading
Frequency-selective fading channels
Training for multiple antenna systems
Application to matrix channels
2.3.4 Open problems in single-user MIMO
2.4.2 MIMO multiple-access channel
Multiple-access channel capacity
2.4.3 MIMO broadcast channel
Broadcast channel capacity
Dirty paper coding achievable rate region
Constant channel capacity
2.4.4 Open problems in multi-user MIMO
2.5.1 Multi-cell MIMO without base-station cooperation
2.5.2 Multi-cell MIMO with base-station cooperation
2.5.3 System level issues
2.6 MIMO for ad hoc networks
2.6.2 The interference channel
2.6.3 Cooperative communication
Diversity–multiplexing trade-offs
2.8 Bibliographical notes
3.1 Transmit channel side information
3.1.2 Methods of obtaining CSIT
3.1.3 A dynamic CSIT model
3.2 Information-theoretic foundation for exploiting CSIT
3.2.1 Value of CSIT in MIMO systems
3.2.2 Optimal signaling with CSIT
3.3 A transmitter structure
3.3.2 Linear precoding structure
3.4 Precoding design criteria
3.4.1 Information and system capacity
3.4.3 Pairwise error probability
PEP per-distance criterion
3.4.4 Detection mean-squared error
3.5 Linear precoder designs
3.5.1 Optimal precoder input-shaping matrix
3.5.2 Precoding on perfect CSIT
3.5.3 Precoding on correlation CSIT
3.5.4 Precoding on mean CSIT
3.5.5 Precoding on both mean and correlation CSIT
3.6 Precoder performance results and discussion
Specific simulation parameters
3.6.1 Performance results
Mean and correlation CSIT
3.7 Applications in practical systems
3.7.1 Channel acquisition methods
Overhead in MIMO CSIT acquisition
3.7.2 Codebook design in closed-loop systems
3.7.3 The role of channel information at the receiver
3.7.4 Precoding in emerging wireless standards
3.8.1 Other types of CSIT
3.8.2 Open problems in exploiting CSIT
3.9 Bibliographical notes
4 Space–time coding for wireless communications: principles and applications
4.2.1 Broadband wireless channel model
4.2.4 Rate–diversity trade-off
4.3 Space–time coding principles
4.3.1 Space–time code design criteria
4.3.2 Space–time trellis codes (STTC)
4.3.3 Space–time block codes (STBC)
4.3.4 A new non-linear maximum-diversity quaternionic code
4.3.5 Diversity-embedded space–time codes
Linear diversity-embedded codes
Non-linear diversity-embedded codes
Channel estimation for quasi-static channels
Integration of equalization and decoding
4.4.2 Applications of diversity-embedded codes
4.4.3 Interactions with network layers
Multiple access: interference cancellation
Integration of physical, link, and transport layers
Network utility maximization (NUM)
4.5 Discussion and future challenges
4.6 Bibliographical notes
Appendix 4.1 Algebraic structure: quadratic forms
5 Fundamentals of receiver design
5.2 Reception of uncoded signals
5.2.2 Decision-feedback receivers
5.3 Factor graphs and iterative processing
5.3.2 Examples of factor graphs
TWLK (Tanner–Wiberg–Loeliger–Koetter) graphs
Factor graph of a dispersive channel
Factor graph of a MIMO channel
5.3.3 The sum–product algorithm
5.3.4 Factor graph with cycles: iterative algorithms
5.3.5 Factor graphs and receiver structures
Decoding over a general channel
Equalizing a dispersive channel
5.4 MIMO receivers for uncoded signals
5.4.2 Linear interfaces with nonlinear processing
5.5 MIMO receivers for coded signals
5.5.1 Iterative sum–product algorithm
5.5.2 Low-complexity approximations
Message approximation: hard and soft decisions
Interference cancelers with linear filtering
EXIT-chart convergence analysis
5.5.4 Quasi-static channel
5.6 Some iterative receivers
5.7 Bibliographical notes
6 Multi-user receiver design
6.2 Multiple-access MIMO systems
6.2.1 Signal and channel models
6.2.2 Canonical receiver structure
6.2.3 Basic MUD algorithms
Space–time matched filter/rake receiver
Decorrelating (zero-forcing) receiver
6.2.4 Digital receiver implementation
6.3 Iterative space–time multi-user detection
6.3.2 Iterative linear space–time multi-user detection
6.3.3 Iterative nonlinear space–time multi-user detection
Cholesky iterative decorrelating decision-feedback ST MUD
Multistage interference canceling ST MUD
6.3.4 EM-based iterative space–time multi-user detection
6.4 Multi-user detection in space–time coded systems
6.4.2 Joint ML multi-user detection and decoding for space–time coded multi-user systems
6.4.3 Partitioned low-complexity receivers for space–time coded multi-user systems
Decorrelator-based partitioned space–time multi-user receiver
Linear MMSE-based partitioned space–time multi-user receiver
Iterative MUD with interference cancellation for space–time coded CDMA
Iterative MUD with interference cancellation and instantaneous MMSE filtering for space–time coded multi-user systems
6.4.4 Single-user soft-input soft-output space–time map decoder
6.5 Adaptive linear space–time multi-user detection
6.5.1 Diversity multi-user detection versus space–time multi-user detection
Linear diversity multi-user detector
Linear space–time multi-user detector
6.5.2 Adaptive linear space–time multi-user detection for flat-fading CDMA
Batch blind linear space–time multi-user detection
Adaptive blind linear space–time multi-user detection
6.5.3 Blind adaptive space–time multi-user detection for asynchronous CDMA in fading multi-path channels
Blind MMSE space–time multi-user detection
Blind sequential Kalman channel estimation
6.7 Bibliographical notes
MIMO Wireless Communications
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