无线与移动通信中的信号处理新技术(第1册) (平装)

出版时间：2002-11  出版社：第1版 (2002年1月1日)  作者：贾纳科斯  页数：434  

内容概要

《无线与移动通信中的信号处理新技术》丛书，介绍了近年来无线与移动通信中使用的信号处理（SP）工具的最新的重要进展，以及世界范围内该领域的领先者的贡献。本书是两本书中的第1册。本丛书的内容涵盖了范围广泛的技术和方法论，包括噪声与干扰消除、调制解调器设计、移动互联网业务、下一代音频/视频广播、蜂窝移动电话和无线多媒体网络等。    本书（第1册）重点阐述单用户点对点链路的信道识别与均衡的关键技术。由于信息承载信号是在衰落介质中传播的，所以现代的均衡器必须充分考虑移动无线信道的可变性，减小符号间干扰和同（共）信道干扰，并抑制在单个或多个传感器的接收机中的噪声。本书介绍了最近提出的带宽节省（半）盲算法与性能分析，以及线性预编码技术，这些技术利用发射冗余使基于训练序列的系统获得明显的改善。本书内容包括：    盲识别与反卷积的子空间方法    有色信号驱动的信道的盲识别与均衡    最优子空间方法；多信道均衡的线性预测算法    FIR多信道估计的半盲方法    盲判决反馈均衡等    本书还介绍了世界范围内各种期刊中的研究成果，全面汇集了用于优化单用户点对点链路的先进信号处理技术。本书对于通信工程、研究人员、管理人员、通信系统设计人员和参与最新通信系统设计或构造的同行将是极其有价值的。

书籍目录

PREFACE  1  CHANNEL ESTIMATION AND EQUALIZATION USING HIGHER-ORDER STATISTICS   1.1  Introduction   1.2  Single-User Systems :Baud Rate Sampling     1.2.1  Cumulant Matching     1.2.2  Inverse Filter Criteria     1.2.3  Equation Error Formulations    1.2.4  Simulation Examples  1.3  Single-user Systems :Fractional Sampling    1.3.1  Cumulant Matching    1.3.2  Simulation Example  1.4  Multi-user Systems     1.4.1  Inverse Filter Criteria     1.4.2  Cumulant Matching    1.4.3  Simulation Examples  1.5  Concluding Remarks   Bibliography 2  PERFORMANCE BOUNDS FOR BLIND CHANNEL ESTIMATION  2.1  Introduction  2.2  Problem Statement and Preliminaries    2.2.1  The Blind Channel Identification Problem    2.2.2  Ambiguity Elimination    2.2.3  The Unconstrained FIM    2.2.4  Achievability of the CRB  2.3  CRB for Constrained Estimates  2.4  CRB for Estimates of Invariants  2.5  CRB for Projection Errors  2.6  Numerical Examples  2.7  Concluding Remarks  Appendix 2.A Proof of Proposition 2  Bibliography3  SUBSPACE METHOD FOR BLIND IDENTIFICATION AND DECONVOLUTION  3.1  Introduction  3.2  Subspace Identification of SIMO Channels    3.2.1  Practical Considerations     3.2.2  Simplifications in the Two-Channel Case  3.3  Subspace Identification of MIMO Channels    3.3.1  Rational Spaces and Polynomial Bases    3.3.2  The Structure of the Left Nullspace of a Sylvester Matrix    3.3.3  The Subspace Method     3.3.4  Advanced Results   3.4  Applications to the Blind Channel Estimation of CDMA Systems     3.4.1  Model Structure      3.4.2  The Structured Subspace Method: The Uplink Case      3.4.3  The Structured Subspace Method: The Downlink Case    3.5  Undermodeled Channel Identification     3.5.1  Example: Identifying a Significant Part of a Channel     3.5.2  Determining the Effective Impulse Response Length   Appendix 3.A    3.A.1  Proof of Theorem 1  3.A.2  Proof of Proposition 3   3.A.3  Proof of Theorem 4   3.A.4  Proof of Proposition 5    Bibliography  4  BLIND IDENTIFICATION AND EQUALIZATION OF CHANNELS DRIVEN BY COLORED SIGNALS   4.1  Introduction   4.2  FIR MIMO Channel     4.2.1  Original Model     4.2.2  Slide-Window Formulation    4.2.3  Noise Variance and Number of Input Signals    4.3  Identifiability Using SOS     4.3.1  Identifiability Conditions     4.3.2  Some Facts of Polynomial Matrices    4.3.3  Proof of the Conditions    4.3.4  When the Input is White   4.4  Blind Identification via Decorrelation    4.4.1  The Principle of the BID    4.4.2  Constructing the Decorrelators    4.4.3  Removing the GCD of Polynomials    4.4.4  Identification of the SIMO Channels  4.5  Final Remarks  Bibliography5  OPTIMUM SUBSPACE METHODS  5.1  Introduction  5.2  Data Model and Notations     5.2.1  Scalar Valued Communication Systems    5.2.2  Multi Channel Communication Systems    5.2.3  A Stacked System Model     5.2.4  Correlation Matrices    5.2.5  Statistical Assumptions  5.3  Subspace Ideas and Notations    5.3.1  Basic Notations   5.4  Parameterizations    5.4.1  A Noise Subspace Parameterization    5.4.2  Selection Matrices  5.5  Estimation Procedure    5.5.1  The Signal Subspace Parameterization    5.5.2  The Noise Subspace Parameterization   5.6  Statistical Analysis    5.6.1  The Residual Covariance Matrices    5.6.2  The Parameter Covariance Matrices  5.7  Relation to Direction Estimation  5.8  Further Results for the Noise Subspace Parameterization    5.8.1  The Results    5.8.2  The Approach   5.9  Simulation Examples  5.10  Conclusions  Appendix 5.A  Bibliography6  LINEAR PREDICTIVE ALGORITHMS FOR BLIND MULTICHANNEL IDENTIFICATION  6.1  Introduction  6.2  Channel Identification Based on Second Order Statistics: Problem Formulation  6.3  Linear Prediction Algorithm for Channel Identification   6.4  Outer-Product Decomposition Algorithm  6.5  Multi-step Linear Prediction  6.6  Channel Estimation by Linear Smoothing (Not Predicting)  6.7  Channel Estimation by Constrained Output Energy Minimization  6.8  Discussion    6.8.1  Channel Conditions     6.8.2  Data Conditions    6.8.3  Noise Effect  6.9  Simulation Results  6.10  Summary  Bibliography7  SEMI-BLIND METHODS FOR FIR MULTICHANNEL ESTIMATION    7.1  Introduction    7.1.1  Training Sequence Based Methods and Blind Methods    7.1.2  Semi-Blind Principle  7.2  Problem Formulation   7.3  Classification lf Semi-Blind Methods  7.4  Identifiability Conditions for Semi-Blind Channel Estimation    7.4.1  Identifiability Definition    7.4.2  TS Based Channel Identifiability    7.4.3  Identifiability in the Deterministic Model      7.4.4  Identifiability in the Gaussian Model  7.5  Performance Measure: Cramer-Rao Bounds  7.6  Performance Optimization Issues  7.7  Optimal Semi-Blind Methods   7.8  Blind DML     7.8.1  Denoised IQML (DIQML)    7.8.2  Pseudo Quadratic ML (PQML)  7.9  Three Suboptimal DML Based Semi-Blind Criteria      7.9.1  Split of the Data    7.9.2  Least Squares-DML     7.9.3  Alternating Quadratic DML (AQ-DML)    7.9.4  Weighted-Least-Squares-PQML (WLS-PQML)    7.9.5  Wimulations   7.10  Semi-Blind Criteria as a Combination of a Blind and a TS Based Criteria     7.10.1  Semi-Blind SRM Example     7.10.2  Subspace Fitting Example  7.11  Performance of Semi-Blind Quadratic Criteria      7.11.1  MU and MK infinite      7.11.2  MU infinite, MK finite    7.11.3  Optimally Weighted Quadratic Criteria  7.12  Gaussian Methods   7.13  Conclusion  Bibliography 8  A GEOMETRICAL APPROACH TO BLIND SIGNAL ESTIMATION    8.1  Introduction  8.2  Design Criteria for Blind Estimators    8.2.1  The Constant Modulus Receiver    8.2.2  The Shalvi-Weinstein Receiver  8.3  The Signal Space Property and Equivalent Cost Functions    8.3.1  The Signal Space Property of CM Receivers      8.3.2  The Signal Space Property of SW Receivers      8.3.3  Equivalent Cost Functions   8.4  Geometrical Analysis of SW Receivers: Global Characterization    8.4.1  The Noiseless Case    8.4.2  The Noisy Case    8.4.3  Domains of Attraction of SW Receivers  8.5  Geometrical Analysis of SW Receivers: Local Characterizations    8.5.1  Local Characterization    8.5.2  MSE of CM Receivers  8.6  Conclusion and Bibliography Notes    8.6.1  Bibliography Notes  Appendix 8.A Proof of Theorem 5   Bibliography  9  LINEAR PRECODING FOR ESTIMATION AND EQUALIZATION OF FREQUENCY-SELECTIVE CHANNELS  9.1  System Model  9.2  Unifying Filterbank Precoders  9.3  FIR-ZF Equalizers  9.4  Jointly Optimal Precoder and Decoder Design    9.4.1  Zero-order Model    9.4.2  MMSE/ZF Coding    9.4.3  MMSE Solution wit Constrained Average Power      9.4.4  Constrained Power Maximum Information Rate Design    9.4.5  Comparison Between Optimal Designs    9.4.6  Asymptotic Performance    9.4.7  Numerical Examples  9.5  Blind Symbol Recovery    9.5.1  Blind Channel Estimation    9.5.2  Comparison with Other Blind Techniques  324    9.5.3  Statistical Efficiency  9.6  Conclusion  Bibliography10.  BLIND CHANNEL IDENTIFIABILITY WITH AN ARBITRARY LINEAR PRECODR  10.1  Introduction  10.2  Basic Theory of Polynomial Equations    10.2.1  Definition of Generic    10.2.2  General Properties of Polynomial Maps    10.2.3  Generic and Non-Generic Points    10.2.4  Invertibility Criteria  10.3  Inherent Scale Ambiguity  10.4  Weak Identifiability and the CRB  10.5  Arbitrary Linear Precoders  10.6  Zero Prefix Precoders  10.7  Geometric Interpretation of Precoding    10.7.1  Linear Precoders    10.7.2  Zero Prefix Precoders  10.8  Filter Banks    10.8.1  Algebraic Analysis of Filter Banks    10.8.2  Spectral Analysis of Filter Banks  10.9  Ambiguity Resistant Precoders  10.10 Symbolic Methods  10.11 Conclusio  Bibliography11  CURRENT APPROACHES TO BLIND DECISION FEEDBACK EQUALIZATION  11.1  Introduction  11.2  Notation  11.3  Data Model  11.4  Wiener Filtering    11.4.1  Unconstrained Length MMSE Receivers    11.4.2  Constrained Length MMSE Receivers    11.4.3  Example: Constrained Versus Unconstrained Length Wiener Receivers  11.5  Blind Tracking Algorithms    11.5.1  DD-DFE    11.5.2  CMA-DFE    11.5.3  Algorithmic and Structural Modifications      11.5.4  Summary of Blind Tracking Algorithms  11.6  DFE Initialization Strategies    11.6.1  Generic Strategy     11.6.2  Multistage Equalization    11.6.3  CMA-IIR Initialization    11.6.4  Local Stability of Adaptive IIR Equalizers      11.6.5  Summary of Blind Initialization Strategies    11.7  Conclusion    Appendix 11.A  Spectral Factorization    Appendix 11.B  CL-MMSE-DFE   Appendix 11.C  DD-DFE Local Convergence   Appendix 11.D  Adaptive IIR Algorithm Updates   Appendix 11.E  CMA-AR Local Stability   Bibliography