复数矩阵导数及其在信号处理与通信中的应用

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"《复数矩阵导数及其应用》是由ARE HJØR UNGNES撰写的一本专业书籍,深入介绍了计算复数函数导数的规则,并将其应用于信号处理和通信领域。这本书对于研究和工程实践都是一份极好的手册。书中详细探讨了关于标量、向量和矩阵值函数相对于复数矩阵变量的导数理论,提供了一套解决含有复数矩阵参数的科研问题的数学工具。全书结构清晰,易于理解,通过众多来自信号处理和通信的实际例子展示了这些工具如何用于分析和优化工程系统性能。这是第一本从工程角度出发讲解复数矩阵导数的专著,涵盖了无模式和有模式的矩阵,利用最新的研究实例来阐明概念,并涉及无线通信、控制理论、自适应滤波、资源管理和数字信号处理等多个领域的应用。书中包含了81个章节末尾的练习题以及一份完整的解题指南(可在网络上获取)。作者Are Hjørungnes是某大学的教授,具有深厚的学术背景和实践经验。" 在《复数矩阵导数及其应用》中,作者首先介绍了复数函数导数的基础理论,这是理解复数域中函数变化率的关键。复数矩阵导数不仅扩展了单变量复数导数的概念,还考虑了多变量和矩阵变量的情况,这对于处理复数矩阵参数的优化问题至关重要。书中的内容包括但不限于: 1. 复数矩阵微积分基础:定义和性质,如链式法则、乘积法则以及偏导数等。 2. 复数矩阵函数的导数计算方法:如何计算复数矩阵的导数,以及处理复数矩阵函数的复合运算。 3. 应用实例:如在信号处理中,通过导数分析滤波器性能的优化;在通信领域,利用导数来设计和调整通信系统的参数,以提高传输效率和抗干扰能力。 4. 控制理论和自适应滤波:复数矩阵导数在动态系统分析和控制器设计中的应用,以及在自适应滤波算法中调整滤波器参数的作用。 5. 资源管理和数字信号处理:如何利用复数矩阵导数进行资源分配优化和信号质量改善。 此外,书中通过大量的练习题和解答,旨在帮助读者巩固所学知识,提升解决实际问题的能力。无论是对于学术研究者还是工程技术人员,这本书都提供了丰富的素材和实用的指导,有助于他们深入理解和应用复数矩阵导数的理论。
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Preface page xi Acknowledgments xiii Abbreviations xv Nomenclature xvii 1 Introduction 1 1.1 Introduction to the Book 1 1.2 Motivation for the Book 2 1.3 Brief Literature Summary 3 1.4 Brief Outline 5 2 Background Material 6 2.1 Introduction 6 2.2 Notation and Classification of Complex Variables and Functions 6 2.2.1 Complex-Valued Variables 7 2.2.2 Complex-Valued Functions 7 2.3 Analytic versus Non-Analytic Functions 8 2.4 Matrix-Related Definitions 12 2.5 Useful Manipulation Formulas 20 2.5.1 Moore-Penrose Inverse 23 2.5.2 Trace Operator 24 2.5.3 Kronecker and Hadamard Products 25 2.5.4 Complex Quadratic Forms 29 2.5.5 Results for Finding Generalized Matrix Derivatives 31 2.6 Exercises 38 3 Theory of Complex-Valued Matrix Derivatives 43 3.1 Introduction 43 3.2 Complex Differentials 44 3.2.1 Procedure for Finding Complex Differentials 46 3.2.2 Basic Complex Differential Properties 46 3.2.3 Results Used to Identify First- and Second-Order Derivatives 53 viii Contents 3.3 Derivative with Respect to Complex Matrices 55 3.3.1 Procedure for Finding Complex-Valued Matrix Derivatives 59 3.4 Fundamental Results on Complex-Valued Matrix Derivatives 60 3.4.1 Chain Rule 60 3.4.2 Scalar Real-Valued Functions 61 3.4.3 One Independent Input Matrix Variable 64 3.5 Exercises 65 4 Development of Complex-Valued Derivative Formulas 70 4.1 Introduction 70 4.2 Complex-Valued Derivatives of Scalar Functions 70 4.2.1 Complex-Valued Derivatives of f (z, z∗) 70 4.2.2 Complex-Valued Derivatives of f (z, z∗) 74 4.2.3 Complex-Valued Derivatives of f (Z, Z∗) 76 4.3 Complex-Valued Derivatives of Vector Functions 82 4.3.1 Complex-Valued Derivatives of f (z, z∗) 82 4.3.2 Complex-Valued Derivatives of f (z, z∗) 82 4.3.3 Complex-Valued Derivatives of f (Z, Z∗) 82 4.4 Complex-Valued Derivatives of Matrix Functions 84 4.4.1 Complex-Valued Derivatives of F(z, z∗) 84 4.4.2 Complex-Valued Derivatives of F(z, z∗) 85 4.4.3 Complex-Valued Derivatives of F(Z, Z∗) 86 4.5 Exercises 91 5 Complex Hessian Matrices for Scalar, Vector, and Matrix Functions 95 5.1 Introduction 95 5.2 Alternative Representations of Complex-Valued Matrix Variables 96 5.2.1 Complex-Valued Matrix Variables Z and Z∗ 96 5.2.2 Augmented Complex-Valued Matrix Variables Z 97 5.3 Complex Hessian Matrices of Scalar Functions 99 5.3.1 Complex Hessian Matrices of Scalar Functions Using Z and Z∗ 99 5.3.2 Complex Hessian Matrices of Scalar Functions Using Z 105 5.3.3 Connections between Hessians When Using Two-Matrix Variable Representations 107 5.4 Complex Hessian Matrices of Vector Functions 109 5.5 Complex Hessian Matrices of Matrix Functions 112 5.5.1 Alternative Expression of Hessian Matrix of Matrix Function 117 5.5.2 Chain Rule for Complex Hessian Matrices 117 5.6 Examples of Finding Complex Hessian Matrices 118 5.6.1 Examples of Finding Complex Hessian Matrices of Scalar Functions 118 5.6.2 Examples of Finding Complex Hessian Matrices of Vector Functions 123 Contents ix 5.6.3 Examples of Finding Complex Hessian Matrices of Matrix Functions 126 5.7 Exercises 129 6 Generalized Complex-Valued Matrix Derivatives 133 6.1 Introduction 133 6.2 Derivatives of Mixture of Real- and Complex-Valued Matrix Variables 137 6.2.1 Chain Rule for Mixture of Real- and Complex-Valued Matrix Variables 139 6.2.2 Steepest Ascent and Descent Methods for Mixture of Real- and Complex-Valued Matrix Variables 142 6.3 Definitions from the Theory of Manifolds 144 6.4 Finding Generalized Complex-Valued Matrix Derivatives 147 6.4.1 Manifolds and Parameterization Function 147 6.4.2 Finding the Derivative of H(X, Z, Z∗) 152 6.4.3 Finding the Derivative of G(W,W∗) 153 6.4.4 Specialization to Unpatterned Derivatives 153 6.4.5 Specialization to Real-Valued Derivatives 154 6.4.6 Specialization to Scalar Function of Square Complex-Valued Matrices 154 6.5 Examples of Generalized Complex Matrix Derivatives 157 6.5.1 Generalized Derivative with Respect to Scalar Variables 157 6.5.2 Generalized Derivative with Respect to Vector Variables 160 6.5.3 Generalized Matrix Derivatives with Respect to Diagonal Matrices 163 6.5.4 Generalized Matrix Derivative with Respect to Symmetric Matrices 166 6.5.5 Generalized Matrix Derivative with Respect to Hermitian Matrices 171 6.5.6 Generalized Matrix Derivative with Respect to Skew-Symmetric Matrices 179 6.5.7 Generalized Matrix Derivative with Respect to Skew-Hermitian Matrices 180 6.5.8 Orthogonal Matrices 184 6.5.9 Unitary Matrices 185 6.5.10 Positive Semidefinite Matrices 187 6.6 Exercises 188 7 Applications in Signal Processing and Communications 201 7.1 Introduction 201 7.2 Absolute Value of Fourier Transform Example 201 7.2.1 Special Function and Matrix Definitions 202 7.2.2 Objective Function Formulation 204 x Contents 7.2.3 First-Order Derivatives of the Objective Function 204 7.2.4 Hessians of the Objective Function 206 7.3 Minimization of Off-Diagonal Covariance Matrix Elements 209 7.4 MIMO Precoder Design for Coherent Detection 211 7.4.1 Precoded OSTBC System Model 212 7.4.2 Correlated Ricean MIMO Channel Model 213 7.4.3 Equivalent Single-Input Single-Output Model 213 7.4.4 Exact SER Expressions for Precoded OSTBC 214 7.4.5 Precoder Optimization Problem Statement and Optimization Algorithm 216 7.4.5.1 Optimal Precoder Problem Formulation 216 7.4.5.2 Precoder Optimization Algorithm 217 7.5 Minimum MSE FIR MIMO Transmit and Receive Filters 219 7.5.1 FIR MIMO System Model 220 7.5.2 FIR MIMO Filter Expansions 220 7.5.3 FIR MIMO Transmit and Receive Filter Problems 223 7.5.4 FIR MIMO Receive Filter Optimization 225 7.5.5 FIR MIMO Transmit Filter Optimization 226 7.6 Exercises 228 References 231 Index 237