無線與移動通信中的信號處理新技術(shù)(第1冊) (平裝)

內(nèi)容概要

《無線與移動通信中的信號處理新技術(shù)》叢書，介紹了近年來無線與移動通信中使用的信號處理（SP）工具的最新的重要進(jìn)展，以及世界范圍內(nèi)該領(lǐng)域的領(lǐng)先者的貢獻(xiàn)。本書是兩本書中的第1冊。本叢書的內(nèi)容涵蓋了范圍廣泛的技術(shù)和方法論，包括噪聲與干擾消除、調(diào)制解調(diào)器設(shè)計(jì)、移動互聯(lián)網(wǎng)業(yè)務(wù)、下一代音頻/視頻廣播、蜂窩移動電話和無線多媒體網(wǎng)絡(luò)等。    本書（第1冊）重點(diǎn)闡述單用戶點(diǎn)對點(diǎn)鏈路的信道識別與均衡的關(guān)鍵技術(shù)。由于信息承載信號是在衰落介質(zhì)中傳播的，所以現(xiàn)代的均衡器必須充分考慮移動無線信道的可變性，減小符號間干擾和同（共）信道干擾，并抑制在單個或多個傳感器的接收機(jī)中的噪聲。本書介紹了最近提出的帶寬節(jié)?。ò耄┟に惴ㄅc性能分析，以及線性預(yù)編碼技術(shù)，這些技術(shù)利用發(fā)射冗余使基于訓(xùn)練序列的系統(tǒng)獲得明顯的改善。本書內(nèi)容包括：    盲識別與反卷積的子空間方法    有色信號驅(qū)動的信道的盲識別與均衡    最優(yōu)子空間方法；多信道均衡的線性預(yù)測算法    FIR多信道估計(jì)的半盲方法    盲判決反饋均衡等    本書還介紹了世界范圍內(nèi)各種期刊中的研究成果，全面匯集了用于優(yōu)化單用戶點(diǎn)對點(diǎn)鏈路的先進(jìn)信號處理技術(shù)。本書對于通信工程、研究人員、管理人員、通信系統(tǒng)設(shè)計(jì)人員和參與最新通信系統(tǒng)設(shè)計(jì)或構(gòu)造的同行將是極其有價值的。

書籍目錄

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

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