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无线通信系统：信号检测与处理技术

【作　者】：Xiaodong wang H.Vincent Poor

【又/译名】：Wireless Communication Systems:Advanced Techniques for Signal Reception

【丛编项】：英文版

【装帧项】：精装小16开 / 628

【出版项】：电子工业出版社 / 2004-07-01

【ISBN号】：9787120000608 / 7120000608

【原书定价】：￥59.00　有10家书店打折销售　

【主题词】：通信－无线通信

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【图书简介】
　　本书重点介绍无线系统接收机设计中涉及到的信号检测和处理技术。本书主要介绍了盲多用户检测、组盲多用户检测、非高斯信道中的鲁棒多用户检测、空-时多用户检测、Turbo多用户检测、窄带干扰抑制、MonteCarloBayesian信号处理、衰落信道的信号处理、编码OFDM系统的信号处理等方面的内容。本书可用做通信与电子、信息与信号处理等专业的高年级本科生、研究生的教材，也可作为相关人员的参考书。前言本套丛书精选自国外通信与信息科学领域中的经典著作，在许多国家的数百所大学用做教材或教学参考书，在国内也有较大影响。本丛书的出版得到了国内多所重点大学的大力推荐和多位著名教授的论证。本套丛书具有较强的先进性和实用性，能充分满足当前高等院校教学和教学改革的需要，将进一步推动国内教学与国际接轨。本套丛书的读者对象是高等院校通信、计算机、电子信息工程、自动化等领域高年级本科生、研究生和教师，也可供工程技术人员阅读参考，并适合有一定英语基础的人员自学使用。1．WirelessCommunicationsPrinciplesandPractice《无线通信原理与应用》TheodoreS.Rappaport著2．DigitalCommunications，FourthEdition《数字通信》（第四版）JohnG.Proakis著3．SecretsofRFCircuitDesign，ThirdEdition《射频电路设计》（第3版）JosephJ.Carr著4．ContinuousandDiscreteControlSystems《连续与离散控制系统》JohnDorsey著5．ElectronicCommunicationSystems，SecondEdition《电子通信系统》（第2版）RoyBlake著6．TheTheoryofInformationandCoding，SecondEdition《信息论与编码理论》（第2版）RobertJ.McEliece著7．Detection，Estimation，andModulationTheory《检测、估值与调制理论》HarryL.VanTrees著8．OptoelectronicsandPhotonics：PrinciplesandPractices《光电子学与光子学的原理及应用》S.O.Kasap著9．UnderstandingFiberOptics《光纤通信原理、系统与应用》JeffHecht著1．TheEssentialGuidetoRFandWireless《RF与无线技术精要》CardJ.Weisman著11．Detection，Estimation，andModulationTheory——Radar-SonarSignalProcessingandGaussianSignalsinNoise（PartIII）《检测、估值与调制理论——雷达－声纳信号处理与噪声中的高斯信号》（卷III）HarryL.VanTrees著12．WirelessCommunicationSystems：AdvancedTechniquesforSignalReception《无线通信系统——信号检测与处理技术》XiaodongWang，H.VincentPoor著若需了解图书详细信息，可访问网站www.phei.com.cn或拨打电话1-68216265。-读书网|DuShu.com

【本书目录】
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

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