Propagation Modeling for Wireless Communications

Description

This book introduces the various approaches and tools used for modelling different propagation environments and lays the foundation for developing a unified theoretical framework for future integrated communication networks. In the case of each type of network, the book uses basic concepts of physics, mathematics, geometry and probability theory to study the impact of the dimension and shape of the propagation environment and relative transmit-receive position on the information flow. The book provides an introduction into wireless communication systems and networks and their applications. For both systems and networks, the basic hard (encoder, modulator, etc.) and soft components (information, signal, etc.) are discussed through schematic block diagrams. Next each of the modes of communication, namely radio waves, acoustic waves, magnetic induction, optical waves, biological particles (molecules, aerosols, neural synapse etc.) and quantum field, are discussed. For each communication scenario presented, the impact of different environmental factors on the propagation phenomenon is articulated, followed by different channel modelling (deterministic, analytical, and stochastic) techniques that are used to characterize the propagation environment. Finally future trends in wireless communication networks are examined and envisioned for next generations 6G/7G of communication systems, like space information networks, sea-to-sky internet of vehicles, and internet of bio-nano things. Based on the future trends of integrated networks, the book drives the need for a generalized channel model irrespective of the media and mode of information transfer. The primary audience for the book is post-graduate students, researchers and academics in electronics and communications engineering, electrical engineering and computer science. Cover Half Title Title Page Copyright Page Contents List of Figures List of Tables I. Introduction 1. Introduction 1.1. Introduction 1.2. The Propagation Environment 1.3. Need for Propagation Modeling 1.4. Historical Highlights 1.5. Additional Preliminaries – Measurement of Wireless Channels 1.5.1. Indoor Wide-band SIMO Channel Sounder 1.5.2. Vector Radio Channel Sounder 1.5.3. Wideband 60-GHz Channel Sounder 1.6. Philosophical Perspective Bibliography II. Wave-based Propagation 2. Radiowave Propagation 2.1. Introduction 2.2. Basic Preliminaries and Details 2.3. Propagation Phenomenon 2.3.1. Spatio-time Variation of the Channel 2.3.2. Temporal Variation of the Channel 2.3.3. Large-Scale Path loss 2.3.4. Delay Spread 2.3.5. Frequency Dependence of Channel Statistics 2.3.6. Noise and Co-Channel Interference 2.4. Modeling Approaches 2.4.1. Channel Impulse Response 2.4.1.1. Analytical Modeling 2.4.1.2. Physical Modeling 2.4.2. Discrete-Time Impulse Response 2.4.2.1. Analytical Modeling 2.4.3. Ray Tracing 2.4.3.1. Analytical Modeling 2.4.4. Channel Transfer Matrix 2.4.4.1. Analytical Modeling 2.4.4.2. Physical Modeling 2.5. Modeling Spatio-Temporal Variations 2.5.1. Distribution of Arrival Time Sequence 2.5.1.1. Standard Poisson Model 2.5.1.2. Modified Poisson|The 􀀀 K Model 2.5.1.3. Modified Poisson|Non-exponential interarrivals 2.5.1.4. Neymann-Scott Clustering Model 2.5.1.5. Gilbert's Burst Noise Model 2.5.1.6. Pseudo-Markov Model 2.5.1.7. -Stable Distribution Model 2.5.2. Distribution of Path Amplitudes 2.5.2.1. Rayleigh Distribution 2.5.2.2. Rician Distribution 2.5.2.3. Hoyt Distribution 2.5.2.4. Nakagami-m Distribution 2.5.2.5. Weibull Distribution 2.5.2.6. Lognormal Distribution 2.5.2.7. Suzuki Distribution 2.5.2.8. Beckmann Distribution 2.5.2.9. K-Distribution 2.5.2.10. K-Distribution 2.5.2.11. 􀀀 - Distribution 2.5.2.12. 􀀀 - Distribution 2.5.3. Distribution of Path Phases 2.5.3.1. Random Phase Increment Model 2.5.3.2. Deterministic Phase Increment Model 2.5.4. Interdependence within path variables 2.5.4.1. Correlation within a profile 2.5.4.2. Correlation between spatially separated profiles 2.5.4.3. Correlation between spatial and temporal variations 2.6. Modeling Large-Scale Path loss 2.6.1. Model 1 2.6.2. Model 2 2.6.3. Model 3 2.6.4. Model 4 2.6.5. Model 5 2.6.6. Model 6 2.7. Modeling Power Delay Pro le 2.7.1. Exponential Model 2.7.2. Exponential-Lognormal Model 2.7.3. Cluster Model 2.7.4. Tapped Delay Line Model 2.7.5. ACT technology-based FIR modeling 2.8. Modeling Frequency Dependence of Channel Statistics 2.8.1. Amplitude modeling 2.8.2. Number of multipath components modeling 2.8.3. Arrival time modeling 2.8.4. Modeling of Frequency Correlation 2.8.5. Modeling of RMS Delay Spread 2.9. Hybrid Models 2.9.1. 3-D Stochastic Image-based Model 2.9.1.1. Calculation of the position of the cluster center 2.9.1.2. Calculation of path loss of the cluster center 2.9.1.3. Calculation of AOA of the rays with in the cluster 2.9.1.4. Calculation of TOA of the rays with in the cluster 2.9.1.5. Calculating the receiver effect 2.9.2. Auto-regressive Model 2.9.3. Two Ring Model 2.9.4. Multiple Scatterer Model 2.9.5. Modified Saleh-Valenzuela Model 2.9.6. Cluttering Model 2.9.7. Reduced Finite Di erence Time Domain (RFDTD) Model 2.9.8. Wavelet Packet-based Model 2.9.9. M-Step 4-State Markov Channel Model Bibliography 3. Acoustic Wave Propagation 3.1. Introduction 3.2. Acoustic Propagation Phenomenon 3.2.1. Acoustic Waves for Underground Communications 3.2.2. Acoustic Waves for Underwater Communications 3.2.2.1. The Doubly Spread Channel 3.2.2.2. Absorption Loss 3.2.2.3. Spreading Loss 3.2.2.4. Multipath Propagation 3.2.2.5. Shadow Zones 3.2.2.6. Scattering Loss 3.2.2.7. Surface Scattering 3.2.2.8. Bubbles 3.2.2.9. Impact on Signal Power 3.2.2.10. Ambient Noise 3.3. Modeling Underwater Acoustic Links 3.3.1. Sea Surface 3.3.2. Bathymetry 3.3.3. Beam Modeling 3.4. Channel Emulators 3.4.1. Ray tracing for underwater channels 3.4.1.1. Ray Trajectories 3.4.1.2. Solving the Eikonal and the Transport Equations 3.4.1.3. Transmission Loss 3.4.2. Gaussian Beam Modeling 3.4.2.1. WSSUS Model 3.4.2.2. Discrete-Time Channel Model 3.4.2.3. The SOS Channel Model 3.4.2.4. The Tapped Delay Line Representation 3.4.2.5. Simulink Model Bibliography 4. Magneto-Inductive Propagation 4.1. Introduction 4.2. The Propagation Phenomenon 4.2.1. Magnetic Field Analysis 4.2.1.1. MI Path loss Model in Lossless Medium 4.2.1.2. Path loss in Underwater Scenarios 4.2.1.3. Path loss in Underground Scenarios 4.2.1.4. MI Noise Model 4.2.1.5. Path loss in Long-range Underwater MIC 4.2.2. Equivalent Circuit Analysis 4.2.2.1. System Modeling 4.3. Channel Emulator Example Bibliography 5. Optical Wave Propagation 5.1. Introduction 5.2. Propagation Phenomenon 5.2.1. General Characteristics 5.2.1.1. Channel Impulse Response 5.2.1.2. Zero-frequency channel gain 5.2.1.3. Root mean-squared (RMS) delay spread 5.2.1.4. Frequency Response 5.2.1.5. Optical Path loss 5.3. Channel Modeling 5.3.1. Deterministic Models 5.3.1.1. Recursive Model 5.3.1.2. Iterative Model 5.3.1.3. DUSTIN Algorithm 5.3.1.4. Ceiling Bounce Model (CBM) Algorithm 5.3.1.5. Geometry-Based Deterministic Models (GBDMs) 5.3.2. Stochastic Models 5.3.2.1. Geometry-based Stochastic Model 5.3.2.2. Carruther's Model 5.3.2.3. RS-GBSM Model 5.3.2.4. Monte-Carlo Algorithms (MCA) 5.3.2.5. Modi ed Monte-Carlo Algorithms (MMCA) 5.3.2.6. Hayasaka-Ito Model 5.4. Additional Factors 5.4.1. Absorption 5.4.2. Scattering 5.4.3. Turbulence 5.5. Underwater OpticalWave Communications (UOWC) Bibliography III. Particle-based Propagation 6. Molecular Communication 6.1. Introduction 6.2. Transmitter Model 6.2.1. Point Transmitter 6.2.2. Volume Transmitter 6.2.3. Ion Channel-based Transmitter 6.3. Propagation Phenomenon 6.3.1. Free Diffusion 6.3.2. Advection 6.3.2.1. Force-Induced Drift 6.3.2.2. Bulk Flow 6.3.3. Advection-Di usion 6.3.4. Chemical Reaction 6.3.5. Modeling the Channel 6.3.5.1. Rectangular Duct Channel 6.3.5.2. Circular Duct Channel 6.3.5.3. Advection Channel 6.3.5.4. Laminar Flow 6.3.6. Degradation Channel 6.3.7. Enzymatic Channel 6.4. Receiver Model 6.4.1. Passive Receiver 6.4.2. Fully Absorbing Receiver 6.4.3. Reactive Receiver 6.5. Receive Signal Models 6.5.1. Deterministic Models 6.5.2. Statistical Models 6.5.2.1. Binomial Model 6.5.3. Stochastic Model Bibliography 7. Quantum Field Propagation 7.1. Introduction 7.2. Analogy between Classical and Quantum World 7.3. Basic Concepts 7.3.1. Qubit 7.3.2. Quantum Communications 7.3.3. Quantum Entanglement 7.3.4. Qudit 7.4. Propagation Phenomenon 7.4.1. Free-space Quantum Light 7.4.2. Atmospheric Quantum Channel Bibliography IV. Conclusion 8. The Future 8.1. Technological Trends 8.2. Generalized Model 8.2.1. Need for a Generalized Model 8.2.2. Directions 8.2.3. Outlook Bibliography Index