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E-raamat: Nonlinear Channel Models And Their Simulations

(Nanjing Univ Of Information Science & Technology, China & Wuxi Univ, China)
  • Formaat: 448 pages
  • Ilmumisaeg: 27-Jun-2022
  • Kirjastus: World Scientific Publishing Co Pte Ltd
  • Keel: eng
  • ISBN-13: 9789811249464
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Nonlinear Channel Models And Their SimulationsSuurem pilt
  • Formaat: 448 pages
  • Ilmumisaeg: 27-Jun-2022
  • Kirjastus: World Scientific Publishing Co Pte Ltd
  • Keel: eng
  • ISBN-13: 9789811249464
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This comprehensive compendium highlights the research results of nonlinear channel modeling and simulation. Nonlinear channels include nonlinear satellite channels, nonlinear Volterra channels, molecular MIMO channels, etc. This volume involves wavelet theory, neural network, echo state network, machine learning, support vector machine, chaos calculation, principal component analysis, Markov chain model, correlation entropy, fuzzy theory and other theories for nonlinear channel modeling and equalization. The useful reference text enriches the theoretical system of nonlinear channel modeling and improving the means of establishing nonlinear channel model. It is suitable for engineering technicians, researchers and graduate students in information and communication engineering, and control science and engineering, intelligent science and technology.

Preface vii
About the Author xi
Chapter 1 Introduction 1(18)
1.1 Satellite Channel Modeling Research
3(6)
1.1.1 Channel single-state model
4(2)
1.1.2 Channel multi-state model
6(1)
1.1.3 Ka-band satellite channel statistical characteristics
7(1)
1.1.4 Research on satellite channel simulation research
8(1)
1.2 Research on Satellite Channel Equalization
9(3)
1.3 Main Contents of the Book
12(1)
1.3.1 Research on nonlinear channel modeling methods
12(1)
1.3.2 Research on nonlinear channel equalization algorithm
12(1)
References
13(6)
Chapter 2 The Theoretical Basis for the Establishment of the Satellite Channel Model 19(76)
2.1 Basic Components of a Satellite Communication System
20(2)
2.2 Basic Parameters of the Satellite Communication Link
22(5)
2.2.1 Elevation from earth station to satellite
22(1)
2.2.2 Azimuth of earth station
23(1)
2.2.3 Link distance between satellite and ground
23(1)
2.2.4 Working frequency
24(1)
2.2.5 Key parameters in the communication link
25(1)
2.2.6 Power flux density
25(2)
2.3 Layered Propagation Characteristics of the Satellite Channel
27(24)
2.3.1 Outer space
29(1)
2.3.2 Dissipation layer, thermal layer, and intermediate layer
30(2)
2.3.3 Stratosphere and troposphere
32(19)
2.3.3.1 Meteorological loss
32(14)
2.3.3.1.1 Atmospheric absorption loss
32(4)
2.3.3.1.2 Rain attenuation
36(4)
2.3.3.1.3 Cloud and fog attenuation
40(2)
2.3.3.1.4 Tropospheric scintillation
42(3)
2.3.3.1.5 Depolarization effect
45(1)
2.3.3.2 Non-meteorological loss
46(5)
2.3.3.2.1 Multipath effect
46(2)
2.3.3.2.2 Camouflage shadowing effect
48(2)
2.3.3.2.3 Doppler effect
50(1)
2.4 Classic Satellite Channel Model
51(20)
2.4.1 Common probability distribution functions
51(9)
2.4.1.1 Gaussian distribution
51(1)
2.4.1.2 Rice/Rayleigh distribution
52(4)
2.4.1.3 Lognormal distribution
56(3)
2.4.1.4 Nakagami distribution
59(1)
2.4.2 Classic satellite channel modeling
60(11)
2.4.2.1 C. Loo model
61(2)
2.4.2.2 Suzuki model
63(2)
2.4.2.3 Corazza model
65(3)
2.4.2.4 Lutz model
68(3)
2.5 Statistical Characteristics of Satellite Channels
71(8)
2.5.1 First-order statistical properties
72(1)
2.5.1.1 Probability density function of the envelope
72(1)
2.5.1.2 Probability density function of phase
72(1)
2.5.2 Second-order statistical property
73(3)
2.5.2.1 Fading rate
73(1)
2.5.2.2 Level crossing rate
74(2)
2.5.2.3 Average fading duration
76(1)
2.5.3 Doppler power spectrum
76(3)
2.5.3.1 Classic power spectrum
77(2)
2.5.3.2 Gaussian power spectrum
79(1)
2.6 Satellite Channel Model Simulation Method
79(11)
2.6.1 Generation method of colored Gaussian noise
80(1)
2.6.2 Calculation method of Doppler coefficient and Doppler frequency
81(4)
2.6.2.1 Equidistance method
81(1)
2.6.2.2 Equal area method
82(1)
2.6.2.3 MSE method
83(1)
2.6.2.4 Improved Doppler coefficient and frequency calculation method
84(1)
2.6.3 Doppler phase calculation method
85(1)
2.6.4 Simulation implementation method of the classical channel model
86(10)
2.6.4.1 Simulation implementation method of Rayleigh channel model
86(1)
2.6.4.2 Simulation implementation method of Rice channel model
87(1)
2.6.4.3 Simulation implementation method of lognormal channel model
88(1)
2.6.4.4 Simulation implementation method of Suzuki channel model
89(1)
References
90(5)
Chapter 3 Multi-State Markov Chain Model for Satellite Channels 95(84)
3.1 Satellite Channel Two-state Markov Chain Model
96(14)
3.1.1 Satellite channel two-state Markov chain model in ground environment
97(4)
3.1.1.1 "Ideal state" channel statistical characteristics
98(1)
3.1.1.2 "Non-ideal state" channel statistical characteristics
99(1)
3.1.1.3 Two-state switching
100(1)
3.1.2 Simulation verification
101(1)
3.1.3 Channel model parameter fitting
102(4)
3.1.4 Channel model simulation
106(4)
3.2 Satellite Channel Three-state Markov Chain Model
110(34)
3.2.1 Channel model in atmospheric environment
111(1)
3.2.2 Channel model in ground environment
112(3)
3.2.3 Satellite channel three-state Markov chain model
115(4)
3.2.4 Satellite channel three-state Markov chain model statistical characteristics
119(1)
3.2.5 Satellite channel three-state Markov chain model simulation method
120(24)
3.2.5.1 Markov chain state transition implementation
121(1)
3.2.5.2 Implementation method of satellite channel Markov chain model
122(1)
3.2.5.3 Simulation verification
123(7)
3.2.5.4 Simulink implementation of satellite channel three-state Markov chain model
130(14)
3.2.5.4.1 Simulation module of probability distribution function
131(4)
3.2.5.4.2 Satellite channel three-state Markov chain model
135(5)
3.2.5.4.3 Simulation verification
140(4)
3.3 Satellite Channel Five-state Markov Chain Model
144(6)
3.3.1 Five-state Markov chain model
144(5)
3.3.1.1 Transfer model
144(2)
3.3.1.2 Shadowing fading model
146(1)
3.3.1.3 State division
147(2)
3.3.2 Simulation tests
149(1)
3.4 Interrupt Probability of Six-state Markov Chain Model for Satellite Channel
150(9)
3.4.1 Analysis of satellite channel six-state Markov chain model
152(4)
3.4.1.1 Several distributions
153(1)
3.4.1.1.1 Rice distribution
153(1)
3.4.1.1.2 Rayleigh-lognormal distribution
154(1)
3.4.1.2 Maximum ratio combined diversity reception
154(1)
3.4.1.2.1 Rician channel
155(1)
3.4.1.2.2 Rayleigh-lognormal channel
155(1)
3.4.1.3 Outage probability
155(1)
3.4.2 Algorithm simulation
156(3)
3.5 Satellite Channel Model Based on Principal Component Analysis and Fuzzy Clustering
159(14)
3.5.1 Analysis of key influencing factors in satellite channel modeling
160(3)
3.5.2 Analysis of satellite channel state number
163(4)
3.5.3 Multi-state Markov chain model for satellite channels
167(1)
3.5.4 Simulation verification
168(5)
References
173(6)
Chapter 4 Nonlinear Satellite Channel Model Based on Different Backgrounds 179(42)
4.1 Nonlinear Satellite Channel Model
180(3)
4.1.1 TWTA model
180(2)
4.1.2 Group delay model
182(1)
4.2 Nonlinear Satellite Channel Model and Equalization System under Gaussian Noise Background
183(11)
4.2.1 Wiener and Hammerstein models for nonlinear satellite channel
185(6)
4.2.1.1 Wiener and Hammerstein models
185(1)
4.2.1.2 Wiener-Hammerstein equalizer for nonlinear satellite channels
186(5)
4.2.2 Simulation tests
191(3)
4.3 Nonlinear Satellite Channel Model and Equalization System under Alpha-Stable Distributed Noise Background
194(8)
4.3.1 Alpha-stable distribution model
194(3)
4.3.2 ANFIS model for nonlinear satellite channels
197(3)
4.3.3 Simulation tests
200(2)
4.4 Nonlinear Satellite Channel Modeling Algorithm Based on TWTA and Group Delay
202(16)
4.4.1 Design of linear group delay filter
204(1)
4.4.2 Combined effects of TWTA nonlinearity and group delay
205(2)
4.4.3 Nonlinear channel model based on channel prior information
207(16)
4.4.3.1 Prior information of nonlinear satellite channels
208(1)
4.4.3.2 Modeling process
209(7)
4.4.3.3 Simulation
216(2)
References
218(3)
Chapter 5 Nonlinear Channel Blind Equalization Algorithm Based on Multiwavelet Double Transform 221(80)
5.1 Volterra Blind Equalization System for Nonlinear Satellite Channel
223(18)
5.1.1 Influence of nonlinearity of TWTA on modulation signals
224(2)
5.1.2 Blind equalization algorithm based on nonlinear filter
226(3)
5.1.2.1 Decision feedback filter
226(1)
5.1.2.2 Volterra filter
227(2)
5.1.3 Volterra blind equalization algorithm
229(2)
5.1.4 Nonlinear blind equalization algorithm based on balanced orthogonal multiwavelet double transform
231(8)
5.1.4.1 Multiwavelet representation of the equalizer
231(4)
5.1.4.2 Balanced orthogonal multiwavelet Wiener equalization algorithm
235(2)
5.1.4.3 Balanced orthogonal multiwavelet double transform decision feedback filter
237(1)
5.1.4.4 Computational complexity
238(1)
5.1.5 Algorithm simulation
239(2)
5.2 Nonlinear Blind Equalization Algorithm Based on Multiwavelet Neural Network
241(9)
5.2.1 Neural network model
241(3)
5.2.1.1 Neuron model
241(2)
5.2.1.2 Neural network model
243(1)
5.2.2 Nonlinear blind equalization algorithm based on multiwavelet neural network
244(5)
5.2.2.1 Neural network blind equalization system model
244(2)
5.2.2.2 Nonlinear blind equalization algorithm based on multiwavelet neural network
246(2)
5.2.2.3 Computational complexity
248(1)
5.2.3 Algorithm simulation
249(1)
5.3 Nonlinear Blind Equalization Algorithm Based on Support Vector Machine and Neural Network
250(17)
5.3.1 Support vector machine foundation
250(6)
5.3.1.1 Optimal classification surface
251(2)
5.3.1.2 Generalized optimal classification surface
253(1)
5.3.1.3 Kernel function
254(2)
5.3.1.3.1 q-order polynomial function
254(1)
5.3.1.3.2 Radial basis function
255(1)
5.3.1.3.3 Sigmoid function
255(1)
5.3.2 Regression principle of support vector machine
256(4)
5.3.2.1 Linear support vector machine regression
257(1)
5.3.2.2 Regression principle of nonlinear SVM
258(2)
5.3.3 Multiwavelet neural network blind equalization algorithm based on spatial diversity SVM
260(6)
5.3.3.1 SVM multi-wavelet neural network blind equalization algorithm
260(3)
5.3.3.2 Nonlinear blind equalization algorithm based on spatial diversity SVM and multiwavelet neural network
263(3)
5.3.3.3 Computational complexity
266(1)
5.3.4 Algorithm simulation
266(1)
5.4 Blind Equalization Algorithm Based on Chaos Algorithm
267(12)
5.4.1 Basis of the chaos algorithm
269(2)
5.4.1.1 Chaos theory
269(1)
5.4.1.1.1 Sensitive dependency of initial value
269(1)
5.4.1.1.2 Elongation and folding characteristic
269(1)
5.4.1.1.3 Fractal and self-similarity
270(1)
5.4.1.1.4 Boundedness and inner randomness
270(1)
5.4.1.2 Chaos algorithm
270(1)
5.4.2 Chaotic optimization process
271(2)
5.4.3 Multiwavelet double neural network nonlinear blind equalization algorithm based on chaos optimization
273(4)
5.4.4 Computational complexity
277(1)
5.4.5 Algorithm simulation
277(2)
5.5 Equalization Algorithm Based on Volterra Filtering Echo State Network and PCA
279(16)
5.5.1 Echo state network
280(3)
5.5.2 Average state entropy: echo state network
283(2)
5.5.3 Principle of channel equalization
285(2)
5.5.4 Algorithm simulation
287(15)
5.5.4.1 Method
287(1)
5.5.4.2 First channel
288(2)
5.5.4.3 Second channel
290(1)
5.5.4.4 Third channel
291(2)
5.5.4.5 Fourth channel
293(1)
5.5.4.6 Fifth channel
293(2)
References
295(6)
Chapter 6 Nonlinear Volterra Channel Blind Equalization Algorithm 301(76)
6.1 Nonlinear Channel Adaptive Equalization Algorithm
302(8)
6.1.1 Nonlinear channel adaptive equalization model
302(4)
6.1.2 Nonlinear channel adaptive equalization algorithm
306(4)
6.1.2.1 Frequency domain Volterra series equalization algorithm
307(1)
6.1.2.2 Equalization algorithm based on compression mapping
308(2)
6.2 Improved Volterra Equalizer for Nonlinear Channel
310(9)
6.2.1 Improved nonlinear channel Volterra equalizer
310(6)
6.2.2 Algorithm simulations
316(1)
6.2.3 Computational complexity
316(3)
6.3 Nonlinear Channel Turbo Blind Equalization Algorithm Based on Linear MMSE
319(15)
6.3.1 System specification
319(2)
6.3.2 Nonlinear channel Volterra-Turbo equalization algorithm based on linear MMSE
321(8)
6.3.2.1 Exact MMSE-based equalization algorithm
322(3)
6.3.2.2 Time-invariant MMSE coefficient
325(2)
6.3.2.2.1 MMSE approximation algorithm without prior information
325(1)
6.3.2.2.2 MMSE approximation algorithm for Low Complexity .
326(1)
6.3.2.2.3 Soft demapper
326(1)
6.3.2.3 Algorithm simulation
327(2)
6.3.3 Iterative blind equalization algorithm based on linear MMSE
329(5)
6.3.3.1 Iterative blind equalization system model
329(1)
6.3.3.2 SISO equalizer
330(2)
6.3.3.3 SISO decoder
332(1)
6.3.3.4 Algorithm simulation
333(1)
6.4 Linear Frequency Domain Turbo Equalization Algorithm Based on Nonlinear Volterra Channel
334(12)
6.4.1 Available symbols in the loop model
335(1)
6.4.2 Frequency domain nonlinear Volterra channel model
336(4)
6.4.3 Linear frequency domain Volterra-MMSE equalizer
340(5)
6.4.3.1 Turbo MMSE FDE
340(4)
6.4.3.2 Soft demapper
344(1)
6.4.4 Simulation verification
345(1)
6.5 Nonlinear Channel Equalization Steady-State Algorithm Based on Maximum Correlation Entropy Volterra Filter
346(12)
6.5.1 Algorithm theory
347(1)
6.5.2 Volterra-CMCC algorithm
348(4)
6.5.3 Steady-state performance
352(4)
6.5.4 Simulation verification
356(2)
6.5.4.1 Verification of EMSE
356(1)
6.5.4.2 Application to nonlinear channel equalization
357(1)
6.6 Complex Neural Network Polynomial Volterra Channel Blind Equalization Algorithm Based on Fuzzy Neural Network Controller
358(14)
6.6.1 Fuzzy neural network algorithm
358(4)
6.6.1.1 Topological structure of fuzzy neural networks
359(1)
6.6.1.2 Fuzzy neural network control structure
359(1)
6.6.1.3 Fuzzy neural network control process
360(2)
6.6.2 Complex neural polynomial network algorithm
362(2)
6.6.2.1 Complex neural polynomial network structure
362(1)
6.6.2.2 Complex neural polynomial network algorithm
363(1)
6.6.3 Fuzzy neural network controlled complex neural polynomial Volterra channel blind equalization algorithm
364(5)
6.6.3.1 64APSK signal
364(2)
6.6.3.2 System block diagram and algorithm description
366(3)
6.6.4 Simulation verification
369(3)
References
372(5)
Chapter 7 Satellite and Molecular MIMO Channel Markov Chain Model Based on Machine Learning 377(46)
7.1 Single Input Single Output (SISO) and Multiple Input and Multiple Output (MIMO) Channel Enhanced Two-State Markov Chain Model
377(12)
7.1.1 Two improved enhanced two-state Markov chain models
378(8)
7.1.1.1 Experimental datasets
378(1)
7.1.1.2 SISO channel two-state semi-Markov input parameters
379(1)
7.1.1.3 Confidence intervals
380(1)
7.1.1.4 Doppler spectrum
381(3)
7.1.1.5 MIMO extension
384(2)
7.1.2 Testing analysis
386(3)
7.2 LMS-MIMO Channel Empirical-Stochastic Markov Model
389(19)
7.2.1 LMS-MIMO channel model
390(3)
7.2.2 Measurement setup
393(2)
7.2.3 Model generation
395(8)
7.2.4 LMS-MIMO channel model validation of small-scale fading
403(5)
7.2.4.1 First-order statistics
403(2)
7.2.4.2 Second-order statistics
405(1)
7.2.4.3 Eigen analysis
405(3)
7.3 Molecular MIMO Channel Model Based on Machine Learning
408(11)
7.3.1 System model
409(1)
7.3.2 Molecular MIMO channel model
410(5)
7.3.2.1 Channel model and fitting
411(1)
7.3.2.2 Training ANN
412(1)
7.3.2.3 Using ANN output for theoretical BER evaluation
413(2)
7.3.3 Results and analysis
415(4)
7.3.3.1 Received signal analysis
415(1)
7.3.3.2 RMSE analysis
415(3)
7.3.3.3 Theoretical BER analysis
418(1)
References
419(4)
Index 423
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