Channel Characterization and Modeling for Vehicular Communications (Wireless Networks)

Preface
Contents
Acronyms
1 Introduction of Vehicular Communications
	1.1 Overview of Vehicular Communications
	1.2 Propagation Characteristics of VehicularCommunication Channels
	1.3 Classification of Vehicular Channel Models
	1.4 Organization of the Monograph
	References
2 A NGSM for SISO V2V Channels
	2.1 Framework of SISO V2V NGSM
		2.1.1 Introduction and Contributions of Proposed SISO V2V NGSM
		2.1.2 Capturing Severe Fading Characteristic and Including LoS Component
	2.2 Modeling of Time-Frequency Non-stationarity
		2.2.1 Modeling of Time Non-stationarity by Markov Chains
		2.2.2 Modeling of Frequency Non-stationarity by Generating Correlated Taps
	2.3 Simulations and Discussions
		2.3.1 Parameter Setting
		2.3.2 Simulation Results
			2.3.2.1 Power Delay Profiles
			2.3.2.2 Tap Correlation Coefficient Matrix
			2.3.2.3 Doppler Power Spectral Density
		2.3.3 Comparisons of Conventional NGSMs and Proposed Model via Simulation
	2.4 Summary
	References
3 A 3D RS-GBSM with Uniform Planar Antenna Array for Massive MIMO V2V Channels
	3.1 Framework of Massive MIMO Vehicular RS-GBSM
		3.1.1 Introduction and Contributions of Proposed RS-GBSM
		3.1.2 Geometrical Representation and Channel Impulse Response of Proposed RS-GBSM
			3.1.2.1 For LoS Component
			3.1.2.2 For Ground Reflection Component
			3.1.2.3 For the Double-Bounced Component Through Two Dynamic Clusters
			3.1.2.4 For Double-Bounced Component Through Dynamic Clusters and Static Clusters
	3.2 Space-Time Non-stationary Modeling with UniformPlanar Antenna
	3.3 Simulations and Discussions
		3.3.1 Statistical Properties
			3.3.1.1 Space-Time Correlation Function
			3.3.1.2 Space Cross-Correlation Function
			3.3.1.3 Time Auto-Correlation Function
			3.3.1.4 Wigner-Ville Spectrum
		3.3.2 Model Simulation
		3.3.3 Model Validation
	3.4 Summary
	References
4 A 3D IS-GBSM for Massive MIMO V2V Channels
	4.1 Framework of Massive MIMO V2V IS-GBSM
		4.1.1 Introduction and Contributions of Proposed IS-GBSM
		4.1.2 Channel Impulse Response of Proposed Cluster-Based IS-GBSM
			4.1.2.1 Complex Channel Gain of LoS Component
			4.1.2.2 Complex Channel Gain of NLoS Component
	4.2 Space-Time Non-stationary Modeling with VehicularTraffic Density
		4.2.1 VTD-Combined Time Cluster Evolution Calculation
		4.2.2 VTD-Combined Array Cluster Evolution Calculation
		4.2.3 Steps of VTD-Combined Time-Array Cluster Evolution Algorithm
	4.3 Simulations and Discussions
		4.3.1 Statistical Properties
			4.3.1.1 Space–Time–Frequency Correlation Function
			4.3.1.2 Doppler Power Spectral Density
		4.3.2 Model Simulation
		4.3.3 Model Validation
	4.4 Summary
	References
5 A 3D IS-GBSM with Continuously Arbitrary Trajectory for mmWave Massive MIMO V2V Channels
	5.1 Framework of mmWave Massive MIMO Vehicular IS-GBSM
		5.1.1 Introduction and Contributions of Proposed IS-GBSM with Continuously Arbitrary Trajectory
		5.1.2 Channel Impulse Response of Proposed Channel Model
			5.1.2.1 Calculation of Transmission with Continuously Arbitrary Trajectory
			5.1.2.2 Complex Channel Gain of LoS Component
			5.1.2.3 Complex Channel Gain of NLoS Component Resulting from Dynamic Clusters
			5.1.2.4 Complex Channel Gain of NLoS Component Resulting from Static Clusters
	5.2 Space–Time–Frequency Non-stationary Modeling with Continuously Arbitrary Trajectory
		5.2.1 Selective Cluster Evolution Based Space–Time–Frequency Non-stationary Modeling Method
			5.2.1.1 Selective Cluster Evolution
			5.2.1.2 Array-Time Evolution of Static Clusters
			5.2.1.3 Array-Time Evolution of Dynamic Clusters
			5.2.1.4 Frequency-Dependent Path Gain
	5.3 Simulations and Discussions
		5.3.1 Statistical Properties
			5.3.1.1 Space–Time–Frequency Correlation Function
			5.3.1.2 Doppler Power Spectral Density
			5.3.1.3 Time Stationary Interval
		5.3.2 Model Simulation
			5.3.2.1 Model Validation
	5.4 Summary
	References
6 A 3D Mixed-Bouncing IS-GBSM with Time-Space Consistency for mmWave Massive MIMO V2V Channels
	6.1 Framework of mmWave Massive MIMO V2V Mixed-Bouncing IS-GBSM
		6.1.1 Introduction and Contributions of Proposed Mixed-Bouncing IS-GBSM with Time-Space Consistency
		6.1.2 Channel Impulse Response of Proposed Mixed-Bouncing IS-GBSM with Time-Space Consistency
			6.1.2.1 Complex Channel Gain of LoS Component
			6.1.2.2 Complex Channel Gain of NLoS Component via Ground Reflection
			6.1.2.3 Complex Channel Gain of NLoS Components via Static Single-Clusters and Twin-Clusters
			6.1.2.4 Complex Channel Gain of NLoS Component via Dynamic Single-Clusters and Twin-Clusters
	6.2 Space–Time–Frequency Non-stationary Modeling with Time-Space Consistency
		6.2.1 Modeling of Space Non-stationarity and Consistency by Observable Semi-spheres Assigned to Antennas
			6.2.1.1 Conditions of Array Observable Static/Dynamic Single-Clusters
			6.2.1.2 Conditions of Array Observable Static/Dynamic Twin-Clusters
		6.2.2 Modeling of Time Non-stationarity and Consistency by Observable Spheres Assigned to Clusters
			6.2.2.1 Conditions of Time Observable Static Single/Twin-Clusters
			6.2.2.2 Conditions of Time Observable Dynamic Single/Twin-Clusters
		6.2.3 Soft Transition Factor
		6.2.4 Frequency-Dependent Factor
	6.3 Simulations and Discussions
		6.3.1 Statistical Properties
			6.3.1.1 Space–Time–Frequency Correlation Function
			6.3.1.2 Power Delay Profile
			6.3.1.3 Time Stationary Interval
			6.3.1.4 Doppler Power Spectral Density
		6.3.2 Model Simulation
		6.3.3 Model Validation by Measurement and RT-Based Results
	6.4 Summary
	References
7 Conclusions and Future Research Directions
	7.1 Conclusions
		7.1.1 Discussions and Summary of Chap.1
		7.1.2 Discussions and Summary of Chap.2
		7.1.3 Discussions and Summary of Chap.3
		7.1.4 Discussions and Summary of Chap.4
		7.1.5 Discussions and Summary of Chap.5
		7.1.6 Discussions and Summary of Chap.6
	7.2 Future Research Directions
		7.2.1 Channel Measurement Perspective
			7.2.1.1 Measurement Platform Establishment
			7.2.1.2 Measurement of Complicated Channel Non-stationarity and Consistency
		7.2.2 Channel Modeling Perspective
			7.2.2.1 Capturing Non-stationarity and Consistency of mmWave-THz Ultra-Massive MIMO V2V Channels
			7.2.2.2 Developing Comprehensive and Efficient Hybrid Modeling Methods
			7.2.2.3 Machine Learning Enabled Channel Non-stationarity and Consistency Capture
		7.2.3 Channel Application Perspective
	References
Index