無線通信系統(tǒng)：信號檢測與處理技術(shù)

PREFACE
1 INTRODUCTION
1.1 Motivation
1.2 Wireless Signaling Environment
1.2.1 Single-User Modulation Techniques
1.2.2 Multiple-Access Techniques
1.2.3 Wireless Channel
1.3 Basic Receiver Signal Processing for Wireless
1.3.1 Matched Filter/RAKE Receiver
1.3.2 Equalization
1.3.3 Multiuser Detection
1.4 Outline of the Book
2 BLIND MULTIUSER DETECTION
2.1 Introduction
2.2 Linear Receivers for Synchronous CDMA
2.2.1 Synchronous CDMA Signal Model
2.2.2 Linear Decorrelating Detector
2.2.3 Linear MMSE Detector
2.3 Blind Multiuser Detection:Direct Methods
2.3.1 LMS Algorithm
2.3.2 RLS Algorithm
2.3.3 QR-RLS Algorithm
2.4 Blind Multiuser Detection:Subspace Methods
2.4.1 Linear Decorrelating Detector
2.4.2 Linear MMSE Detector
2.4.3 Asymptotics of Detector Estimates
2.4.4 Asymptotic Multiuser Efficiency under Mismatch
2.5 Performance of Blind Multiuser Detectors
2.5.1 Performance Measures
2.5.2 Asymptotic Output SINR
2.6 Subspace Tracking Algorithms
2.6.1 PASTd Algorithm
2.6.2 QR-Jacobi Methods
2.6.3 NAHJ Subspace Tracking
2.7 Blind Multiuser Detection in Multipath Channels
2.7.1 Multipath Signal Model
2.7.2 Linear Multiuser Detectors
2.7.3 Blind Channel Estimation
2.7.4 Adaptive Receiver Structures
2.7.5 Blind Multiuser Detection in Correlated Noise
2.8 Appendix
2.8.1 Derivations for Section 2.3.3
2.8.2 Proofs for Section 2.4.4
2.8.3 Proofs for Section 2.5.2
3 GROUP-BLIND MULTIUSER DETECTION
3.1 Introduction
3.2 Linear Group-Blind Multiuser Detection for Synchronous CDMA
3.3 Performance of Group-Blind Multiuser Detectors
3.3.1 Form II Group-Blind Hybrid Detector
3.3.2 Form I Group-Blind Detectors
3.4 Nonlinear Group-Blind Multiuser Detection for Synchronous CDMA
3.4.1 Slowest-descent Search
3.4.2 Nonlinear Group-Blind Multiuser Detection
3.5 Group-Blind Multiuser Detection in Multipath Channels
3.5.1 Linear Group-Blind Detectors
3.5.2 Adaptive Group-Blind Linear Multiuser Detection
3.5.3 Linear Group-Blind Detection in Correlated Noise
3.5.4 Nonlinear Group-Blind Detection
3.6 Appendix
3.6.1 Proofs for Section 3.3.1
3.6.2 Proofs for Section 3.3.2
4 ROBUST MULTIUSER DETECTION IN NON-GAUSSIAN CHANNELS
4.1 Introduction
4.2 Multiuser Detection via Robust Regression
4.2.1 System Model
4.2.2 Least-Squares Regression and Linear Decorrelator
4.2.3 Robust Multiuser Detection via M-Regression
4.3 Asymptotic Performance of Robust Multiuser Detection
4.3.1 Influence Function
4.3.2 Asymptotic Probability of Error
4.4 Implementation of Robust Multiuser Detectors
4.5 Robust Blind Multiuser Detection
4.6 Robust Multiuser Detection Based on Local Likelihood Search
4.6.1 Exhaustive-Search and Decorrelative Detection
4.6.2 Local-Search Detection
4.7 Robust Group-Blind Multiuser Detection
4.8 Extension to Multipath Channels
4.8.1 Robust Blind Multiuser Detection in Multipath Channels
4.8.2 Robust Group-Blind Multiuser Detection in Multipath Channels
4.9 Robust Multiuser Detection in Stable Noise
4.9.1 Symmetric Stable Distribution
4.9.2 Performance of Robust Multiuser Detectors in Stable Noise
4.10 Appendix
4.10.1 Proofs of Proposition 4.1 in Section 4.4
4.10.2 Proofs of Proposition 4.2 in Section 4.5
5 SPACE-TIME MULTIUSER DETECTION
5.1 Introduction
5.2 Adaptive Array Processing in TDMA Systems
5.2.1 Signal Model
5.2.2 Linear MMSE Combining
5.2.3 Subspace-Based Training Algorithm
5.2.4 Extension to Dispersive Channels
5.3 Optimal Space-Time Multiuser Detection
5.3.1 Signal Model
5.3.2 Sufficient Statistic
5.3.3 Maximum-Likelihood Multiuser Sequence Detector
5.4 Linear Space-Time Multiuser Detection
5.4.1 Linear Multiuser Detection via Iterative Interference Cancellation
5.4.2 Single-User Linear Space-Time Detection
5.4.3 Combined Single-User/Multiuser Linear Detection
5.5 Adaptive Space-Time Multiuser Detection in Synchronous CDMA
5.5.1 One Transmit Antenna,Two Receive Antennas
5.5.2 Two Transmit Antennas,One Receive Antenna
5.5.3 Two Transmit and Two Receive Antennas
5.5.4 Blind Adaptive Implementations
5.6 Adaptive Space-Time Multiuser Detection in Multipath CDMA
5.6.1 Signal Model
5.6.2 Blind MMSE Space-Time Multiuser Detection
5.6.3 Blind Adaptive Channel Estimation
6 TURBO MULTIUSER DETECTION
6.1 Introduction to Turbo Processing
6.2 MAP Decoding algorithm for Convolutional Codes
6.3 Turbo Multiuser Detection for Synchronous CDMA
6.3.1 Turbo Multiuser Receiver
6.3.2 Optimal SISO Multiuser Detector
6.3.3 Low-Complexity SISO Multiuser Detector
6.4 Turbo Multiuser Detection with Unknown Interferers
6.4.1 Signal Model
6.4.2 Group-Blind SISO Multiuser Detector
6.4.3 Sliding Window Group-Blind Detector for Asynchronous CDMA
6.5 Turbo Multiuser Detection in CDMA with Multipath Fading
6.5.1 Signal Model and Sufficient Statistics
6.5.2 SISO Multiuser Detector in Multipath Fading Channels
6.6 Turob Multiuser Detection in CDMA with Turbo Coding
6.6.1 Turbo Code and Soft Decoding Algorithm
6.6.2 Turbo Multiuser Receiver in Turbo-Coded CDMA with Multipath Fading
6.7 Turbo Multiuser Detection in Space-Time Block-Coded Systems
6.7.1 Multiuser STBC System
6.7.2 Turbo Multiuser Receiver for STBC System
6.7.3 Projection-Based Turbo Multiuser Detection
6.8 Turbo Multiuser Detection in Space-Time Trellis-Coded Systems
6.8.1 Multiuser STTC System
6.8.2 Turbo Multiuser Receiver for STTC System
6.9 Appendix
6.9.1 Proofs for Section 6.3.3
6.9.2 Derivation of the LLR for the RAKE Receiver in Section 6.6.2
7 NARROWBAND INTERFERENCE SUPPRESSION
7.1 Introduction
7.2 Linear Predictive Techniques
7.2.1 Signal Models
7.2.2 Linear Predictive Methods
7.3 Nonlinear Predictive Techniques
7.3.1 ACM Filter
7.3.2 Adaptive Nonlinear Predictor
7.3.3 Nonlinear Interpolating filters
7.3.4 HMM-Based Methods
7.4 Code-Aided Techniques
7.4.1 NBI Suppression via the Linear MMSE Detector
7.4.2 Tonal Interference
7.4.3 Autoregressive Interference
7.4.4 Digital Interference
7.5 Performance Comparisons of NBI Suppression Techniques
7.5.1 Matched Filter
7.5.2 Linear Predictor and Interpolator
7.5.3 Nonlinear Predictor and Interpolator
7.5.4 Numerical Examples
7.6 Near-Far Resistance to Both NBI and MAI by Linear MMSE Detector
7.6.1 Near-Far Resistance to NBI
7.6.2 Near-Far Resistance to Both NBI and MAI
7.7 Adaptive Linear MMSE NBI Suppression
7.8 Maximum-Likelihood Code-Aided Method
7.9 Appendix:Convergence of the RLS Linear MMSE Detector
7.9.1 Linear MMSE Detector and RLS Blind Adaptation Rule
7.9.2 Convergence of the Mean Weight Vector
7.9.3 Weight Error Correlation Matrix
7.9.4 Convergence of MSE
7.9.5 Steady-State SINR
7.9.6 Comparison with Training-Based RLS Algorithm
8 MONTE CARLO BAYESIAN SIGNAL PROCESSING
8.1 Introduction
8.2 Bayesian Signal Processing
8.2.1 Bayesian Framework
8.2.2 Batch Processing versus Adaptive Processing
8.2.3 Monte Carlo Methods
8.3 Markov Chain Monte Carlo Signal Processing
8.3.1 Metropolis-Hasting Algorithm
8.3.2 Gibbs Sampler
8.4 Bayesian Multiuser Detection via MCMC
8.4.1 System Description
8.4.2 Bayesian Multiuser Detection in Gaussian Noise
8.4.3 Bayesian Multiuser Detection in Impulsive Noise
8.4.4 Bayesian Multiuser Detection in Coded Systems
8.5 Sequential Monte Carlo Signal Processing
8.5.1 Sequential Importance Sampling
8.5.2 SMC for Dynamical Systems
8.5.3 Resampling Procedures
8.5.4 Mixture Kalman Filter
8.6 Blind Adaptive Equalization of MIMO Channels via SMC
8.6.1 System Description
8.6.2 SMC Blind Adaptive Equalizer for MIMO Channels
8.7 Appendix
8.7.1 Derivations for Section 8.4.2
8.7.2 Derivations for Section 8.4.3
8.7.3 Proofs of Proposition 8.1 in Section 8.5.2
8.7.4 Proofs of Proposition 8.2 in Section 8.5.3
9 SIGNAL PROCESSING FOR FADING CHANNELS
9.1 Introduction
9.2 Statistical Modeling of Multipath Fading Channels
9.2.1 Frequency-Nonselective Fading Channels
9.2.2 Frequency-Selective Fading Channels
9.3 Coherent Detection in Fading Channels Based on the EM Algorithm
9.3.1 Expectation-Maximization Algorithm
9.3.2 EM-Based Receiver in Flat-Fading Channels
9.3.3 Linear Multiuser Detection in Flat-Fading Synchronous CDMA Channels
9.3.4 Sequential EM Algorithm
9.4 Decision-feedback Differential Detection in Fading Channels
9.4.1 Decision-Feedback Differential Detection in Flat-Fading Channels
9.4.2 Decision-Feedback Space-Time Differential Decoding
9.5 Adaptive SMC Receivers for Flat-Fading Channels
9.5.1 System Description
9.5.2 Adaptive Receiver in Fading Gaussian Noise Channels:Uncoded Case
9.5.3 Delayed Estimation
9.5.4 Adaptive Receiver in Fading Gaussian Noise Channels:Coded Case
9.5.5 Adaptive Receiver in Fading Impulsive Noise Channels
9.6 Appendix
9.6.1 Proof of Proposition 9.1 in Section 9.5.2
10 ADVANCED SIGNAL PROCESSING FOR CODED OFDM SYSTEMS
10.1 Introduction
10.2 OFDM Communication System
10.3 Blind MCMC Receiver for Coded OFDM with Frequency-Selective Fading and Frequency Offset
10.3.1 System Description
10.3.2 Bayesian MCMC Demodulator
10.4 Pilot-Symbol-Aided Turbo Receiver for Space-Time Block-Coded OFDM Systems
10.4.1 System Descriptions
10.4.2 ML Receiver Based on the EM Algorithm
10.4.3 Pilot-Symbol-Aided Turbo Receiver
10.5 LDPC-Based Space-Time Coded OFDM Systems
10.5.1 Capacity Considerations for STC-OFDM Systems
10.5.2 Low-Density Parity-Check Codes
10.5.3 LDPC-Based STC-OFDM System
10.5.4 Turbo Receiver
10.6 Appendix
10.6.1 Derivations for Section 10.3
ACRONYMS
BIBLIOGRAPHY
INDEX