XUEFENG YIN, Tongji University, China XIANG CHENG, Peking University, China

• Preface xiList of Acronyms and Symbols xiii1 Introduction 11.1 Book Objective 11.2 The Historical Context 21.2.1 Importance of Channel Characterization 21.2.2 Single-input, Single-output Channel Models 21.2.3 Spatial Channel Models (SCMs) 51.2.4 Channel Models for 5G 61.2.5 Other Kinds of Channel Model 71.3 Book Outline 8Bibliography 92 Characterization of Propagation Channels 152.1 Three Phenomena in Wireless Channels 152.2 Path Loss and Shadowing 162.3 Multipath Fading 182.4 Stochastic Characterization of Multipath Fading 222.4.1 Received Envelope and Phase Distribution 232.4.2 Envelope Level Cross Rate and Average Fade Duration 242.4.3 Correlation Functions 242.5 Duality of Multipath Fading 262.6 WSSUS Assumption of Multipath Fading 282.7 A Review of Propagation Channel Modeling 312.7.1 Classification of MIMO Channel Models 322.7.2 Classification of V2V Channel Models 35Bibliography 383 Generic Channel Models 413.1 Channel Spread Function 433.2 Specular-path Model 463.3 Dispersive-path Model 513.4 Time-evolution Model 543.5 Power Spectral Density Model 573.6 Model for Keyhole Channel 68Bibliography 734 Geometry-based Stochastic Channel Modeling 774.1 General Modeling Procedure 774.2 Regular-shaped Geometry-based Stochastic Models 794.2.1 RS-GBSMs for Conventional Cellular Communication Systems 794.2.2 RS-GBSMs for V2V Communication Systems 824.3 Irregular-shaped Geometry-based Stochastic Models 834.4 Simulation Models 844.4.1 Filter Simulation Models 864.4.2 Sum-of-sinusoids Simulation Models 884.5 Simulation Models for Non-isotropic Scattering Narrowband SISO V2V Rayleigh Fading Channels 904.5.1 A Two-ring SISO V2V Reference Model 914.5.2 SoS Simulation Models 92Bibliography 1035 Channel Measurements 1065.1 Channel-sounding Equipment/System 1075.2 Post-processing of Measurement Data 1095.3 Impact of Phase Noise and Possible Solutions 1105.3.1 Mitigating Phase Noise: the Sliding Window 1115.3.2 Mitigating Phase Noise: Whitening and the SAGE algorithm 1135.4 Directional Radiation Patterns 1175.5 Switching-mode Selection 1245.5.1 Switching-mode for Channel Sounding 1255.5.2 Estimation of Doppler Frequency 1265.5.3 Ambiguity in Parameter Estimation 1295.5.4 Case Study: TDM-SIMO Channel Sounding with a Uniform Linear Array 1305.5.5 Switching-mode Optimization 1325.5.6 Simulation Studies 134Bibliography 1426 Deterministic Channel-parameter Estimation 1456.1 Bartlett Beamformer 1466.2 The MUSIC Algorithm 1486.3 The ESPRIT and Propagator Methods 1506.3.1 Esprit 1506.3.2 The Propagator Algorithm 1526.4 Maximum-likelihood Method 1526.5 The SAGE Algorithm 1536.5.1 Signal model 1556.5.2 The SAGE Algorithm Derived 1596.5.3 Initialization of Parameter Estimates for Executing the SAGE Algorithm 1676.6 A Brief Introduction to the RiMAX Algorithm 1726.7 Evidence-framework-based Algorithms 1726.7.1 Multi-level Evidence Framework 1736.7.2 Example I: Exponential Decay used in Three-level EF 1746.7.3 Example II: Delay Spread in a Two-level EF 1766.8 Extended Kalman-filter-based Tracking Algorithm 1786.8.1 Overview 1786.8.2 The Structure of an EKF 1796.8.3 Model Mismatch due to the Linear Approximation 1836.8.4 Tracking Performance and the Initial Phase 1846.9 Particle-filter-based Tracking Algorithm 1886.9.1 State-space Model 1896.9.2 Observation Model 1906.9.3 The Proposed Low-complexity Particle Filter 1906.9.4 Experimental Investigation 193Bibliography 1977 Statistical Channel-parameter Estimation 2017.1 A Brief Review of Dispersive Parameter Estimators 2017.2 Dispersive Component Estimation Algorithms 2037.2.1 Effective Signal Model 2047.2.2 Specular–Scatterer Model Estimation 2057.2.3 First-order GAM Model Estimation 2077.2.4 Nominal Azimuth Estimators 2087.2.5 Azimuth Spread Estimator 2127.2.6 Simulation Studies 2137.3 PSD-based Dispersive Component Estimation 2187.4 Bidirection-delay-Doppler Frequency PSD Estimation 2197.4.1 Channel Power Spectrum Estimator 2197.4.2 Measurement Data Evaluation 225Bibliography 2328 Measurement-based Statistical Channel Modeling 2368.1 General Modeling Procedures 2378.1.1 Channel Measurement 2378.1.2 Channel Parameter Estimation 2388.1.3 Stochastic Channel Modeling 2408.2 Clustering Algorithm based on Specular-path Models 2418.2.1 Stochastic Cluster-based Channel Modeling 2428.2.2 Clustering Algorithms based on Multipath Component Distance 2438.3 Data Segment-length Selection 2458.4 Relay and CoMP Channel Modeling 2498.4.1 Introduction 2498.4.2 SSF Cross-correlation and Modeling Methodology 2508.4.3 Measurements for Relay-channel Characterization 2528.4.4 Model Extraction 253Bibliography 2579 In Practice: Channel Modeling for Modern Communication Systems 2609.1 Scenarios for V2V and Cooperative Communications 2609.1.1 V2V Communication Scenarios 2609.1.2 Cooperative Communication Scenarios 2639.2 Channel Characteristics 2649.2.1 Channel Characteristics of V2V Communication Systems 2649.2.2 Channel Characteristics of Cooperative Communication Systems 2649.3 Scattering Theoretical Channel Models for Conventional Cellular MIMO Systems 2659.3.1 A Wideband Multiple-ring-based MIMO Channel Reference Model 2669.3.2 Generic Space–Time–Frequency CF 2709.3.3 MIMO Simulation Models 2729.3.4 Numerical Results and Analysis 2759.3.5 Summary 2789.4 Scattering Theoretical Channel Models for V2V Systems 2799.4.1 Modeling and Simulation of MIMO V2V Channels: Narrowband 2799.4.2 Modeling and Simulation of MIMO V2V Channels: Wideband 3049.5 Scattering Theoretical Channel Models for Cooperative MIMO Systems 3299.5.1 A Unified Cooperative MIMO Channel-model Framework 3309.5.2 A New MIMO GBSM for Cooperative Relay Systems 3339.5.3 Multi-link Spatial Correlation Functions 3399.5.4 Numerical Results and Analysis 3429.5.5 Summary 348Bibliography 349Appendix A 353A.1 Influence of Neglecting Doppler Shift within the Sensing Periods 353A.2 Simplification of the Noise Component in an Objective Function 357A.3 Derivations of Equations (7.6)–(7.8) 359A.4 Derivation of Eqs (4.20a) and (4.20b) 361A.5 Derivation of the CF ˆρĥĥ(τ) 363A.6 Probability Density Functions 364A.7 Computation of the Gerschgorin Radii 365A.8 Derivations for Chapter 9 367A.8.1 Derivation of Eq. (9.11) 367A.8.2 Derivation of Eq. (9.16) 368A.8.3 Derivation of Eqs (9.43)–(9.48) 369A.8.4 Derivation of Eq. (9.53) 370A.8.5 Derivation of Eq. (9.60) 370A.8.6 Comparison of the Doppler PSDs with different CFs, Eqs (9.49) and (9.50) 371A.8.7 Derivation of Eq. (9.75b) 373A.8.8 Derivation of the Condition max{ RT ,RR} < min{ al − al−1 } that Guarantees the TDL Structure of our Model 374A.8.9 The Reduced Expressions of Spatial Correlation 374Bibliography 378Index 379

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XUEFENG YIN, Tongji University, China XIANG CHENG, Peking University, China

• Preface xiList of Acronyms and Symbols xiii1 Introduction 11.1 Book Objective 11.2 The Historical Context 21.2.1 Importance of Channel Characterization 21.2.2 Single-input, Single-output Channel Models 21.2.3 Spatial Channel Models (SCMs) 51.2.4 Channel Models for 5G 61.2.5 Other Kinds of Channel Model 71.3 Book Outline 8Bibliography 92 Characterization of Propagation Channels 152.1 Three Phenomena in Wireless Channels 152.2 Path Loss and Shadowing 162.3 Multipath Fading 182.4 Stochastic Characterization of Multipath Fading 222.4.1 Received Envelope and Phase Distribution 232.4.2 Envelope Level Cross Rate and Average Fade Duration 242.4.3 Correlation Functions 242.5 Duality of Multipath Fading 262.6 WSSUS Assumption of Multipath Fading 282.7 A Review of Propagation Channel Modeling 312.7.1 Classification of MIMO Channel Models 322.7.2 Classification of V2V Channel Models 35Bibliography 383 Generic Channel Models 413.1 Channel Spread Function 433.2 Specular-path Model 463.3 Dispersive-path Model 513.4 Time-evolution Model 543.5 Power Spectral Density Model 573.6 Model for Keyhole Channel 68Bibliography 734 Geometry-based Stochastic Channel Modeling 774.1 General Modeling Procedure 774.2 Regular-shaped Geometry-based Stochastic Models 794.2.1 RS-GBSMs for Conventional Cellular Communication Systems 794.2.2 RS-GBSMs for V2V Communication Systems 824.3 Irregular-shaped Geometry-based Stochastic Models 834.4 Simulation Models 844.4.1 Filter Simulation Models 864.4.2 Sum-of-sinusoids Simulation Models 884.5 Simulation Models for Non-isotropic Scattering Narrowband SISO V2V Rayleigh Fading Channels 904.5.1 A Two-ring SISO V2V Reference Model 914.5.2 SoS Simulation Models 92Bibliography 1035 Channel Measurements 1065.1 Channel-sounding Equipment/System 1075.2 Post-processing of Measurement Data 1095.3 Impact of Phase Noise and Possible Solutions 1105.3.1 Mitigating Phase Noise: the Sliding Window 1115.3.2 Mitigating Phase Noise: Whitening and the SAGE algorithm 1135.4 Directional Radiation Patterns 1175.5 Switching-mode Selection 1245.5.1 Switching-mode for Channel Sounding 1255.5.2 Estimation of Doppler Frequency 1265.5.3 Ambiguity in Parameter Estimation 1295.5.4 Case Study: TDM-SIMO Channel Sounding with a Uniform Linear Array 1305.5.5 Switching-mode Optimization 1325.5.6 Simulation Studies 134Bibliography 1426 Deterministic Channel-parameter Estimation 1456.1 Bartlett Beamformer 1466.2 The MUSIC Algorithm 1486.3 The ESPRIT and Propagator Methods 1506.3.1 Esprit 1506.3.2 The Propagator Algorithm 1526.4 Maximum-likelihood Method 1526.5 The SAGE Algorithm 1536.5.1 Signal model 1556.5.2 The SAGE Algorithm Derived 1596.5.3 Initialization of Parameter Estimates for Executing the SAGE Algorithm 1676.6 A Brief Introduction to the RiMAX Algorithm 1726.7 Evidence-framework-based Algorithms 1726.7.1 Multi-level Evidence Framework 1736.7.2 Example I: Exponential Decay used in Three-level EF 1746.7.3 Example II: Delay Spread in a Two-level EF 1766.8 Extended Kalman-filter-based Tracking Algorithm 1786.8.1 Overview 1786.8.2 The Structure of an EKF 1796.8.3 Model Mismatch due to the Linear Approximation 1836.8.4 Tracking Performance and the Initial Phase 1846.9 Particle-filter-based Tracking Algorithm 1886.9.1 State-space Model 1896.9.2 Observation Model 1906.9.3 The Proposed Low-complexity Particle Filter 1906.9.4 Experimental Investigation 193Bibliography 1977 Statistical Channel-parameter Estimation 2017.1 A Brief Review of Dispersive Parameter Estimators 2017.2 Dispersive Component Estimation Algorithms 2037.2.1 Effective Signal Model 2047.2.2 Specular–Scatterer Model Estimation 2057.2.3 First-order GAM Model Estimation 2077.2.4 Nominal Azimuth Estimators 2087.2.5 Azimuth Spread Estimator 2127.2.6 Simulation Studies 2137.3 PSD-based Dispersive Component Estimation 2187.4 Bidirection-delay-Doppler Frequency PSD Estimation 2197.4.1 Channel Power Spectrum Estimator 2197.4.2 Measurement Data Evaluation 225Bibliography 2328 Measurement-based Statistical Channel Modeling 2368.1 General Modeling Procedures 2378.1.1 Channel Measurement 2378.1.2 Channel Parameter Estimation 2388.1.3 Stochastic Channel Modeling 2408.2 Clustering Algorithm based on Specular-path Models 2418.2.1 Stochastic Cluster-based Channel Modeling 2428.2.2 Clustering Algorithms based on Multipath Component Distance 2438.3 Data Segment-length Selection 2458.4 Relay and CoMP Channel Modeling 2498.4.1 Introduction 2498.4.2 SSF Cross-correlation and Modeling Methodology 2508.4.3 Measurements for Relay-channel Characterization 2528.4.4 Model Extraction 253Bibliography 2579 In Practice: Channel Modeling for Modern Communication Systems 2609.1 Scenarios for V2V and Cooperative Communications 2609.1.1 V2V Communication Scenarios 2609.1.2 Cooperative Communication Scenarios 2639.2 Channel Characteristics 2649.2.1 Channel Characteristics of V2V Communication Systems 2649.2.2 Channel Characteristics of Cooperative Communication Systems 2649.3 Scattering Theoretical Channel Models for Conventional Cellular MIMO Systems 2659.3.1 A Wideband Multiple-ring-based MIMO Channel Reference Model 2669.3.2 Generic Space–Time–Frequency CF 2709.3.3 MIMO Simulation Models 2729.3.4 Numerical Results and Analysis 2759.3.5 Summary 2789.4 Scattering Theoretical Channel Models for V2V Systems 2799.4.1 Modeling and Simulation of MIMO V2V Channels: Narrowband 2799.4.2 Modeling and Simulation of MIMO V2V Channels: Wideband 3049.5 Scattering Theoretical Channel Models for Cooperative MIMO Systems 3299.5.1 A Unified Cooperative MIMO Channel-model Framework 3309.5.2 A New MIMO GBSM for Cooperative Relay Systems 3339.5.3 Multi-link Spatial Correlation Functions 3399.5.4 Numerical Results and Analysis 3429.5.5 Summary 348Bibliography 349Appendix A 353A.1 Influence of Neglecting Doppler Shift within the Sensing Periods 353A.2 Simplification of the Noise Component in an Objective Function 357A.3 Derivations of Equations (7.6)–(7.8) 359A.4 Derivation of Eqs (4.20a) and (4.20b) 361A.5 Derivation of the CF ˆρĥĥ(τ) 363A.6 Probability Density Functions 364A.7 Computation of the Gerschgorin Radii 365A.8 Derivations for Chapter 9 367A.8.1 Derivation of Eq. (9.11) 367A.8.2 Derivation of Eq. (9.16) 368A.8.3 Derivation of Eqs (9.43)–(9.48) 369A.8.4 Derivation of Eq. (9.53) 370A.8.5 Derivation of Eq. (9.60) 370A.8.6 Comparison of the Doppler PSDs with different CFs, Eqs (9.49) and (9.50) 371A.8.7 Derivation of Eq. (9.75b) 373A.8.8 Derivation of the Condition max{ RT ,RR} < min{ al − al−1 } that Guarantees the TDL Structure of our Model 374A.8.9 The Reduced Expressions of Spatial Correlation 374Bibliography 378Index 379

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XUEFENG YIN, Tongji University, China XIANG CHENG, Peking University, China

• Preface xiList of Acronyms and Symbols xiii1 Introduction 11.1 Book Objective 11.2 The Historical Context 21.2.1 Importance of Channel Characterization 21.2.2 Single-input, Single-output Channel Models 21.2.3 Spatial Channel Models (SCMs) 51.2.4 Channel Models for 5G 61.2.5 Other Kinds of Channel Model 71.3 Book Outline 8Bibliography 92 Characterization of Propagation Channels 152.1 Three Phenomena in Wireless Channels 152.2 Path Loss and Shadowing 162.3 Multipath Fading 182.4 Stochastic Characterization of Multipath Fading 222.4.1 Received Envelope and Phase Distribution 232.4.2 Envelope Level Cross Rate and Average Fade Duration 242.4.3 Correlation Functions 242.5 Duality of Multipath Fading 262.6 WSSUS Assumption of Multipath Fading 282.7 A Review of Propagation Channel Modeling 312.7.1 Classification of MIMO Channel Models 322.7.2 Classification of V2V Channel Models 35Bibliography 383 Generic Channel Models 413.1 Channel Spread Function 433.2 Specular-path Model 463.3 Dispersive-path Model 513.4 Time-evolution Model 543.5 Power Spectral Density Model 573.6 Model for Keyhole Channel 68Bibliography 734 Geometry-based Stochastic Channel Modeling 774.1 General Modeling Procedure 774.2 Regular-shaped Geometry-based Stochastic Models 794.2.1 RS-GBSMs for Conventional Cellular Communication Systems 794.2.2 RS-GBSMs for V2V Communication Systems 824.3 Irregular-shaped Geometry-based Stochastic Models 834.4 Simulation Models 844.4.1 Filter Simulation Models 864.4.2 Sum-of-sinusoids Simulation Models 884.5 Simulation Models for Non-isotropic Scattering Narrowband SISO V2V Rayleigh Fading Channels 904.5.1 A Two-ring SISO V2V Reference Model 914.5.2 SoS Simulation Models 92Bibliography 1035 Channel Measurements 1065.1 Channel-sounding Equipment/System 1075.2 Post-processing of Measurement Data 1095.3 Impact of Phase Noise and Possible Solutions 1105.3.1 Mitigating Phase Noise: the Sliding Window 1115.3.2 Mitigating Phase Noise: Whitening and the SAGE algorithm 1135.4 Directional Radiation Patterns 1175.5 Switching-mode Selection 1245.5.1 Switching-mode for Channel Sounding 1255.5.2 Estimation of Doppler Frequency 1265.5.3 Ambiguity in Parameter Estimation 1295.5.4 Case Study: TDM-SIMO Channel Sounding with a Uniform Linear Array 1305.5.5 Switching-mode Optimization 1325.5.6 Simulation Studies 134Bibliography 1426 Deterministic Channel-parameter Estimation 1456.1 Bartlett Beamformer 1466.2 The MUSIC Algorithm 1486.3 The ESPRIT and Propagator Methods 1506.3.1 Esprit 1506.3.2 The Propagator Algorithm 1526.4 Maximum-likelihood Method 1526.5 The SAGE Algorithm 1536.5.1 Signal model 1556.5.2 The SAGE Algorithm Derived 1596.5.3 Initialization of Parameter Estimates for Executing the SAGE Algorithm 1676.6 A Brief Introduction to the RiMAX Algorithm 1726.7 Evidence-framework-based Algorithms 1726.7.1 Multi-level Evidence Framework 1736.7.2 Example I: Exponential Decay used in Three-level EF 1746.7.3 Example II: Delay Spread in a Two-level EF 1766.8 Extended Kalman-filter-based Tracking Algorithm 1786.8.1 Overview 1786.8.2 The Structure of an EKF 1796.8.3 Model Mismatch due to the Linear Approximation 1836.8.4 Tracking Performance and the Initial Phase 1846.9 Particle-filter-based Tracking Algorithm 1886.9.1 State-space Model 1896.9.2 Observation Model 1906.9.3 The Proposed Low-complexity Particle Filter 1906.9.4 Experimental Investigation 193Bibliography 1977 Statistical Channel-parameter Estimation 2017.1 A Brief Review of Dispersive Parameter Estimators 2017.2 Dispersive Component Estimation Algorithms 2037.2.1 Effective Signal Model 2047.2.2 Specular–Scatterer Model Estimation 2057.2.3 First-order GAM Model Estimation 2077.2.4 Nominal Azimuth Estimators 2087.2.5 Azimuth Spread Estimator 2127.2.6 Simulation Studies 2137.3 PSD-based Dispersive Component Estimation 2187.4 Bidirection-delay-Doppler Frequency PSD Estimation 2197.4.1 Channel Power Spectrum Estimator 2197.4.2 Measurement Data Evaluation 225Bibliography 2328 Measurement-based Statistical Channel Modeling 2368.1 General Modeling Procedures 2378.1.1 Channel Measurement 2378.1.2 Channel Parameter Estimation 2388.1.3 Stochastic Channel Modeling 2408.2 Clustering Algorithm based on Specular-path Models 2418.2.1 Stochastic Cluster-based Channel Modeling 2428.2.2 Clustering Algorithms based on Multipath Component Distance 2438.3 Data Segment-length Selection 2458.4 Relay and CoMP Channel Modeling 2498.4.1 Introduction 2498.4.2 SSF Cross-correlation and Modeling Methodology 2508.4.3 Measurements for Relay-channel Characterization 2528.4.4 Model Extraction 253Bibliography 2579 In Practice: Channel Modeling for Modern Communication Systems 2609.1 Scenarios for V2V and Cooperative Communications 2609.1.1 V2V Communication Scenarios 2609.1.2 Cooperative Communication Scenarios 2639.2 Channel Characteristics 2649.2.1 Channel Characteristics of V2V Communication Systems 2649.2.2 Channel Characteristics of Cooperative Communication Systems 2649.3 Scattering Theoretical Channel Models for Conventional Cellular MIMO Systems 2659.3.1 A Wideband Multiple-ring-based MIMO Channel Reference Model 2669.3.2 Generic Space–Time–Frequency CF 2709.3.3 MIMO Simulation Models 2729.3.4 Numerical Results and Analysis 2759.3.5 Summary 2789.4 Scattering Theoretical Channel Models for V2V Systems 2799.4.1 Modeling and Simulation of MIMO V2V Channels: Narrowband 2799.4.2 Modeling and Simulation of MIMO V2V Channels: Wideband 3049.5 Scattering Theoretical Channel Models for Cooperative MIMO Systems 3299.5.1 A Unified Cooperative MIMO Channel-model Framework 3309.5.2 A New MIMO GBSM for Cooperative Relay Systems 3339.5.3 Multi-link Spatial Correlation Functions 3399.5.4 Numerical Results and Analysis 3429.5.5 Summary 348Bibliography 349Appendix A 353A.1 Influence of Neglecting Doppler Shift within the Sensing Periods 353A.2 Simplification of the Noise Component in an Objective Function 357A.3 Derivations of Equations (7.6)–(7.8) 359A.4 Derivation of Eqs (4.20a) and (4.20b) 361A.5 Derivation of the CF ˆρĥĥ(τ) 363A.6 Probability Density Functions 364A.7 Computation of the Gerschgorin Radii 365A.8 Derivations for Chapter 9 367A.8.1 Derivation of Eq. (9.11) 367A.8.2 Derivation of Eq. (9.16) 368A.8.3 Derivation of Eqs (9.43)–(9.48) 369A.8.4 Derivation of Eq. (9.53) 370A.8.5 Derivation of Eq. (9.60) 370A.8.6 Comparison of the Doppler PSDs with different CFs, Eqs (9.49) and (9.50) 371A.8.7 Derivation of Eq. (9.75b) 373A.8.8 Derivation of the Condition max{ RT ,RR} < min{ al − al−1 } that Guarantees the TDL Structure of our Model 374A.8.9 The Reduced Expressions of Spatial Correlation 374Bibliography 378Index 379

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