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　　本書重點介紹無線系統(tǒng)接收機設計中涉及到的信號檢測和處理技術。本書主要介紹了盲多用戶檢測、組盲多用戶檢測、非高斯信道中的魯棒多用戶檢測、空-時多用戶檢測、Turbo多用戶檢測、窄帶干擾抑制、MonteCarloBayesian信號處理、衰落信道的信號處理、編碼OFDM系統(tǒng)的信號處理等方面的內容。本書可用做通信與電子、信息與信號處理等專業(yè)的高年級本科生、研究生的教材，也可作為相關人員的參考書。前言本套叢書精選自國外通信與信息科學領域中的經(jīng)典著作，在許多國家的數(shù)百所大學用做教材或教學參考書，在國內也有較大影響。本叢書的出版得到了國內多所重點大學的大力推薦和多位著名教授的論證。本套叢書具有較強的先進性和實用性，能充分滿足當前高等院校教學和教學改革的需要，將進一步推動國內教學與國際接軌。本套叢書的讀者對象是高等院校通信、計算機、電子信息工程、自動化等領域高年級本科生、研究生和教師，也可供工程技術人員閱讀參考，并適合有一定英語基礎的人員自學使用。1．WirelessCommunicationsPrinciplesandPractice《無線通信原理與應用》TheodoreS.Rappaport著2．DigitalCommunications，F(xiàn)ourthEdition《數(shù)字通信》（第四版）JohnG.Proakis著3．SecretsofRFCircuitDesign，ThirdEdition《射頻電路設計》（第3版）JosephJ.Carr著4．ContinuousandDiscreteControlSystems《連續(xù)與離散控制系統(tǒng)》JohnDorsey著5．ElectronicCommunicationSystems，SecondEdition《電子通信系統(tǒng)》（第2版）RoyBlake著6．TheTheoryofInformationandCoding，SecondEdition《信息論與編碼理論》（第2版）RobertJ.McEliece著7．Detection，Estimation，andModulationTheory《檢測、估值與調制理論》HarryL.VanTrees著8．OptoelectronicsandPhotonics：PrinciplesandPractices《光電子學與光子學的原理及應用》S.O.Kasap著9．UnderstandingFiberOptics《光纖通信原理、系統(tǒng)與應用》JeffHecht著1．TheEssentialGuidetoRFandWireless《RF與無線技術精要》CardJ.Weisman著11．Detection，Estimation，andModulationTheory——Radar-SonarSignalProcessingandGaussianSignalsinNoise（PartIII）《檢測、估值與調制理論——雷達－聲納信號處理與噪聲中的高斯信號》（卷III）HarryL.VanTrees著12．WirelessCommunicationSystems：AdvancedTechniquesforSignalReception《無線通信系統(tǒng)——信號檢測與處理技術》XiaodongWang，H.VincentPoor著若需了解圖書詳細信息，可訪問網(wǎng)站www.phei.com.cn或撥打電話1-68216265。

作者簡介

暫缺《無線通信系統(tǒng)：信號檢測與處理技術》作者簡介

圖書目錄

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