Nonlinear Channel Models And Their Simulations

Contents
Summary of Contents
Preface
About the Author
Chapter 1 Introduction
	1.1 Satellite Channel Modeling Research
		1.1.1 Channel single-state model
		1.1.2 Channel multi-state model
		1.1.3 Ka-band satellite channel statistical characteristics
		1.1.4 Research on satellite channel simulation research
	1.2 Research on Satellite Channel Equalization
	1.3 Main Contents of the Book
		1.3.1 Research on nonlinear channel modeling methods
		1.3.2 Research on nonlinear channel equalization algorithm
	References
Chapter 2 The Theoretical Basis for the Establishment of the Satellite Channel Model
	2.1 Basic Components of a Satellite Communication System
	2.2 Basic Parameters of the Satellite Communication Link
		2.2.1 Elevation from earth station to satellite
		2.2.2 Azimuth of earth station
		2.2.3 Link distance between satellite and ground
		2.2.4 Working frequency
		2.2.5 Key parameters in the communication link
		2.2.6 Power flux density
	2.3 Layered Propagation Characteristics of the Satellite Channel
		2.3.1 Outer space
		2.3.2 Dissipation layer, thermal layer, and intermediate layer
		2.3.3 Stratosphere and troposphere
			2.3.3.1 Meteorological loss
				2.3.3.1.1 Atmospheric absorption loss
				2.3.3.1.2 Rain attenuation
				2.3.3.1.3 Cloud and fog attenuation
				2.3.3.1.4 Tropospheric scintillation
				2.3.3.1.5 Depolarization effect
			2.3.3.2 Non-meteorological loss
				2.3.3.2.1 Multipath effect
				2.3.3.2.3 Doppler effect
	2.4 Classic Satellite Channel Model
		2.4.1 Common probability distribution functions
			2.4.1.1 Gaussian distribution
			2.4.1.2 Rice/Rayleigh distribution
			2.4.1.3 Lognormal distribution
			2.4.1.4 Nakagami distribution
		2.4.2 Classic satellite channel modeling
			2.4.2.1 C. Loo model
			2.4.2.2 Suzuki model
			2.4.2.3 Corazza model
			2.4.2.4 Lutz model
	2.5 Statistical Characteristics of Satellite Channels
		2.5.1 First-order statistical properties
			2.5.1.1 Probability density function of the envelope
			2.5.1.2 Probability density function of phase
		2.5.2 Second-order statistical property
			2.5.2.1 Fading rate
			2.5.2.2 Level crossing rate
			2.5.2.3 Average fading duration
		2.5.3 Doppler power spectrum
			2.5.3.1 Classic power spectrum
			2.5.3.2 Gaussian power spectrum
	2.6 Satellite Channel Model Simulation Method
		2.6.1 Generation method of colored Gaussian noise
		2.6.2 Calculation method of Doppler coefficient and Doppler frequency
			2.6.2.1 Equidistance method
			2.6.2.2 Equal area method
			2.6.2.3 MSE method
			2.6.2.4 Improved Doppler coefficient and frequency calculation method
		2.6.3 Doppler phase calculation method
		2.6.4 Simulation implementation method of the classical channel model
			2.6.4.1 Simulation implementation method of Rayleigh channel model
			2.6.4.2 Simulation implementation method of Rice channel model
			2.6.4.3 Simulation implementation method of lognormal channel model
			2.6.4.4 Simulation implementation method of Suzuki channel model
	References
Chapter 3 Multi-State Markov Chain Model for Satellite Channels
	3.1 Satellite Channel Two-state Markov Chain Model
		3.1.1 Satellite channel two-state Markov chain model in ground environment
			3.1.1.1 “Ideal state” channel statistical characteristics
			3.1.1.2 “Non-ideal state” channel statistical characteristics
			3.1.1.3 Two-state switching
		3.1.2 Simulation verification
		3.1.3 Channel model parameter fitting
		3.1.4 Channel model simulation
	3.2 Satellite Channel Three-state Markov Chain Model
		3.2.1 Channel model in atmospheric environment
		3.2.2 Channel model in ground environment
		3.2.3 Satellite channel three-state Markovchain model
		3.2.3 Satellite channel three-state Markov chain model
		3.2.4 Satellite channel three-state Markov chain model statistical characteristics
		3.2.5 Satellite channel three-state Markov chain model simulation method
			3.2.5.1 Markov chain state transition implementation
			3.2.5.2 Implementation method of satellite channel Markov chain model
			3.2.5.3 Simulation verification
			3.2.5.4 Simulink implementation of satellite channel three-state Markov chain model
				3.2.5.4.1 Simulation module of probability distribution function
				3.2.5.4.2 Satellite channel three-state Markov chain model
				3.2.5.4.3 Simulation verification
	3.3 Satellite Channel Five-state Markov Chain Model
		3.3.1 Five-state Markov chain model
			3.3.1.1 Transfer model
			3.3.1.2 Shadowing fading model
			3.3.1.3 State division
		3.3.2 Simulation tests
	3.4 Interrupt Probability of Six-state Markov Chain Model for Satellite Channel
		3.4.1 Analysis of satellite channel six-state Markov chain model
			3.4.1.1 Several distributions
				3.4.1.1.1 Rice distribution
				3.4.1.1.2 Rayleigh–lognormal distribution
			3.4.1.2 Maximum ratio combined diversity reception
				3.4.1.2.1 Rician channel
				3.4.1.2.2 Rayleigh–lognormal channel
			3.4.1.3 Outage probability
		3.4.2 Algorithm simulation
	3.5 Satellite Channel Model Based on Principal Component Analysis and Fuzzy Clustering
		3.5.1 Analysis of key influencing factors in satellite channel modeling
		3.5.2 Analysis of satellite channel state number
		3.5.3 Multi-state Markov chain model for satellite channels
		3.5.4 Simulation verification
	References
Chapter 4 Nonlinear Satellite Channel Model Based on Different Backgrounds
	4.1 Nonlinear Satellite Channel Model
		4.1.1 TWTA model
		4.1.2 Group delay model
	4.2 Nonlinear Satellite Channel Model and Equalization System under Gaussian Noise Background
		4.2.1 Wiener and Hammerstein models for nonlinear satellite channel
			4.2.1.1 Wiener and Hammerstein models
			4.2.1.2 Wiener–Hammerstein equalizer for nonlinear satellite channels
		4.2.2 Simulation tests
	4.3 Nonlinear Satellite Channel Model and Equalization System under Alpha-Stable Distributed Noise Background
		4.3.1 Alpha-stable distribution model
		4.3.2 ANFIS model for nonlinear satellite channels
		4.3.3 Simulation tests
	4.4 Nonlinear Satellite Channel Modeling Algorithm Based on TWTA and Group Delay
		4.4.1 Design of linear group delay filter
		4.4.2 Combined effects of TWTA nonlinearity and group delay
		4.4.3 Nonlinear channel model based on channel prior information
			4.4.3.1 Prior information of nonlinear satellite channels
			4.4.3.2 Modeling process
			4.4.3.3 Simulation
	References
Chapter 5 Nonlinear Channel Blind Equalization Algorithm Based on Multiwavelet Double Transform
	5.1 Volterra Blind Equalization System for Nonlinear Satellite Channel
		5.1.1 Influence of nonlinearity of TWTA on modulation signals
		5.1.2 Blind equalization algorithm based on nonlinear filter
			5.1.2.1 Decision feedback filter
			5.1.2.2 Volterra filter
		5.1.3 Volterra blind equalization algorithm
		5.1.4 Nonlinear blind equalization algorithm based on balanced orthogonal multiwavelet double transform
			5.1.4.1 Multiwavelet representation of the equalizer
			5.1.4.2 Balanced orthogonal multiwavelet Wiener equalization algorithm
			5.1.4.3 Balanced orthogonal multiwavelet double transform decision feedback filter
			5.1.4.4 Computational complexity
		5.1.5 Algorithm simulation
	5.2 Nonlinear Blind Equalization Algorithm Based on Multiwavelet Neural Network
		5.2.1 Neural network model
			5.2.1.1 Neuron model
			5.2.1.2 Neural network model
		5.2.2 Nonlinear blind equalization algorithm based on multiwavelet neural network
			5.2.2.1 Neural network blind equalization system model
			5.2.2.2 Nonlinear blind equalization algorithm based on multiwavelet neural network
			5.2.2.3 Computational complexity
		5.2.3 Algorithm simulation
	5.3 Nonlinear Blind Equalization Algorithm Based on Support Vector Machine and Neural Network
		5.3.1 Support vector machine foundation
			5.3.1.1 Optimal classification surface
			5.3.1.2 Generalized optimal classification surface
			5.3.1.3 Kernel function
				5.3.1.3.1 q-order polynomial function
				5.3.1.3.2 Radial basis function
				5.3.1.3.3 Sigmoid function
		5.3.2 Regression principle of support vector machine
			5.3.2.1 Linear support vector machine regression
			5.3.2.2 Regression principle of nonlinear SVM
		5.3.3 Multiwavelet neural network blind equalization algorithm based on spatial diversity SVM
			5.3.3.1 SVM multi-wavelet neural network blind equalization algorithm
			5.3.3.2 Nonlinear blind equalization algorithm based on spatial diversity SVM and multiwavelet neural network
			5.3.3.3 Computational complexity
		5.3.4 Algorithm simulation
	5.4 Blind Equalization Algorithm Based on Chaos Algorithm
		5.4.1 Basis of the chaos algorithm
			5.4.1.1 Chaos theory
				5.4.1.1.1 Sensitive dependency of initial value
				5.4.1.1.2 Elongation and folding characteristic
				5.4.1.1.3 Fractal and self-similarity
				5.4.1.1.4 Boundedness and inner randomness
			5.4.1.2 Chaos algorithm
		5.4.2 Chaotic optimization process
		5.4.3 Multiwavelet double neural network nonlinear blind equalization algorithm based on chaos optimization
		5.4.4 Computational complexity
		5.4.5 Algorithm simulation
	5.5 Equalization Algorithm Based on Volterra Filtering Echo State Network and PCA
		5.5.1 Echo state network
		5.5.2 Average state entropy: echo state network
		5.5.3 Principle of channel equalization
		5.5.4 Algorithm simulation
			5.5.4.1 Method
			5.5.4.2 First channel
			5.5.4.3 Second channel
			5.5.4.4 Third channel
			5.5.4.5 Fourth channel
			5.5.4.6 Fifth channel
	References
Chapter 6 Nonlinear Volterra Channel Blind Equalization Algorithm
	6.1 Nonlinear Channel Adaptive Equalization Algorithm
		6.1.1 Nonlinear channel adaptive equalization model
		6.1.2 Nonlinear channel adaptive equalization algorithm
			6.1.2.1 Frequency domain Volterra series equalization algorithm
			6.1.2.2 Equalization algorithm based on compression mapping
	6.2 Improved Volterra Equalizer for Nonlinear Channel
		6.2.1 Improved nonlinear channel Volterra equalizer
		6.2.2 Algorithm simulations
		6.2.3 Computational complexity
	6.3 Nonlinear Channel Turbo Blind Equalization Algorithm Based on Linear MMSE
		6.3.1 System specification
		6.3.2 Nonlinear channel Volterra–Turbo equalization algorithm based on linear MMSE
			6.3.2.1 Exact MMSE-based equalization algorithm
			6.3.2.2 Time-invariant MMSE coefficient
				6.3.2.2.1 MMSE approximation algorithm without prior information
				6.3.2.2.2 MMSE approximation algorithm for Low Complexity
				6.3.2.2.3 Soft demapper
			6.3.2.3 Algorithm simulation
		6.3.3 Iterative blind equalization algorithm based on linear MMSE
			6.3.3.1 Iterative blind equalization system model
			6.3.3.2 SISO equalizer
			6.3.3.3 SISO decoder
			6.3.3.4 Algorithm simulation
	6.4 Linear Frequency Domain Turbo Equalization Algorithm Based on Nonlinear Volterra Channel
		6.4.1 Available symbols in the loop model
		6.4.2 Frequency domain nonlinear Volterra channel model
		6.4.3 Linear frequency domain Volterra–MMSE equalizer
			6.4.3.1 Turbo MMSE FDE
			6.4.3.2 Soft demapper
		6.4.4 Simulation verification
	6.5 Nonlinear Channel Equalization Steady-State Algorithm Based on Maximum Correlation Entropy Volterra Filter
		6.5.1 Algorithm theory
		6.5.2 Volterra–CMCC algorithm
		6.5.3 Steady-state performance
		6.5.4 Simulation verification
			6.5.4.1 Verification of EMSE
			6.5.4.2 Application to nonlinear channel equalization
	6.6 Complex Neural Network Polynomial Volterra Channel Blind Equalization Algorithm Based on Fuzzy Neural Network Controller
		6.6.1 Fuzzy neural network algorithm
			6.6.1.1 Topological structure of fuzzy neural networks
			6.6.1.2 Fuzzy neural network control structure
			6.6.1.3 Fuzzy neural network control process
		6.6.2 Complex neural polynomial network algorithm
			6.6.2.1 Complex neural polynomial network structure
			6.6.2.2 Complex neural polynomial network algorithm
		6.6.3 Fuzzy neural network controlled complex neural polynomial Volterra channel blind equalization algorithm
			6.6.3.1 64APSK signal
			6.6.3.2 System block diagram and algorithm description
		6.6.4 Simulation verification
	References
Chapter 7 Satellite and Molecular MIMO Channel Markov Chain Model Based on Machine Learning
	7.1 Single Input Single Output (SISO) and Multiple Input and Multiple Output (MIMO) Channel Enhanced Two-State Markov Chain Model
		7.1.1 Two improved enhanced two-state Markov chain models
			7.1.1.1 Experimental datasets
			7.1.1.2 SISO channel two-state semi-Markov input parameters
			7.1.1.3 Confidence intervals
			7.1.1.4 Doppler spectrum
			7.1.1.5 MIMO extension
		7.1.2 Testing analysis
	7.2 LMS-MIMO Channel Empirical–Stochastic Markov Model
		7.2.1 LMS-MIMO channel model
		7.2.2 Measurement setup
		7.2.3 Model generation
		7.2.4 LMS-MIMO channel model validation of small-scale fading
			7.2.4.1 First-order statistics
			7.2.4.2 Second-order statistics
			7.2.4.3 Eigen analysis
	7.3 Molecular MIMO Channel Model Based on Machine Learning
		7.3.1 System model
		7.3.2 Molecular MIMO channel model
			7.3.2.1 Channel model and fitting
			7.3.2.2 Training ANN
			7.3.2.3 Using ANN output for theoretical BER evaluation
		7.3.3 Results and analysis
			7.3.3.1 Received signal analysis
			7.3.3.2 RMSE analysis
			7.3.3.3 Theoretical BER analysis
	References
Index