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E-raamat: Neuro-Fuzzy Equalizers for Mobile Cellular Channels

(Government College of Engineering Kannur, Kannur, India)
  • Formaat: 236 pages
  • Ilmumisaeg: 22-Nov-2017
  • Kirjastus: CRC Press Inc
  • Keel: eng
  • ISBN-13: 9781351831789
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Neuro-Fuzzy Equalizers for Mobile Cellular ChannelsSuurem pilt
  • Formaat: 236 pages
  • Ilmumisaeg: 22-Nov-2017
  • Kirjastus: CRC Press Inc
  • Keel: eng
  • ISBN-13: 9781351831789
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Equalizers are present in all forms of communication systems. Neuro-Fuzzy Equalizers for Mobile Cellular Channels details the modeling of a mobile broadband communication channel and designing of a neuro-fuzzy adaptive equalizer for it. This book focuses on the concept of the simulation of wireless channel equalizers using the adaptive-network-based fuzzy inference system (ANFIS). The book highlights a study of currently existing equalizers for wireless channels. It discusses several techniques for channel equalization, including the type-2 fuzzy adaptive filter (type-2 FAF), compensatory neuro-fuzzy filter (CNFF), and radial basis function (RBF) neural network.







Neuro-Fuzzy Equalizers for Mobile Cellular Channels

starts with a brief introduction to channel equalizers, and the nature of mobile cellular channels with regard to the frequency reuse and the resulting CCI. It considers the many channel models available for mobile cellular channels, establishes the mobile indoor channel as a Rayleigh fading channel, presents the channel equalization problem, and focuses on various equalizers for mobile cellular channels. The book discusses conventional equalizers like LE and DFE using a simple LMS algorithm and transversal equalizers. It also covers channel equalization with neural networks and fuzzy logic, and classifies various equalizers.This being a fairly new branch of study, the book considers in detail the concept of fuzzy logic controllers in noise cancellation problems and provides the fundamental concepts of neuro-fuzzy. The final chapter offers a recap and explores venues for further research. This book also establishes a common mathematical framework of the equalizers using the RBF model and develops a mathematical model for ultra-wide band (UWB) channels using the channel co-variance matrix (CCM).





Introduces the novel concept of the application of adaptive-network-based fuzzy inference system (ANFIS) in the design of wireless channel equalizers











Provides model ultra-wide band (UWB) channels using channel co-variance matrix





Offers a formulation of a unified radial basis function (RBF) framework for ANFIS-based and fuzzy adaptive filter (FAF) Type II, as well as compensatory neuro-fuzzy equalizers





Includes extensive use of MATLAB® as the simulation tool in all the above cases
List of Figures
xiii
List of Tables
xv
Preface xvii
Acknowledgments xxi
List of Abbreviations
xxiii
1 Introduction
1(8)
1.1 Introduction
1(1)
1.2 Need for Equalizers
2(1)
1.3 Review of Contemporary Literature
3(2)
1.4 Major Contributions of the Book
5(4)
Further Reading
5(4)
2 Overview of Mobile Channels and Equalizers
9(36)
2.1 Introduction
9(1)
2.2 Mobile Cellular Communication System
9(7)
2.2.0.1 Call Initiation
10(1)
2.2.0.2 Frequency Reuse
11(1)
2.2.1 Co-Channel Interference and System Capacity
12(2)
2.2.2 Adjacent Channel Interference
14(1)
2.2.3 Digital Modulation Types and Relative Efficiencies
15(1)
2.3 Fading Characteristics of Mobile Channels
16(2)
2.3.0.1 Tapped Delay Line (TDL) Channel Model
17(1)
2.3.0.2 Rayleigh and Ricean Fading Models
17(1)
2.4 Channel Models
18(9)
2.4.1 Suburban Path Loss Model
18(1)
2.4.2 Urban (Alternative Flat Suburban) Path Loss Model
19(1)
2.4.2.1 Multipath Delay Profile
20(1)
2.4.2.2 RMS Delay Spread
21(1)
2.4.2.3 Fade Distribution, K-Factor
21(1)
2.4.2.4 Doppler Spectrum
22(1)
2.4.2.5 Spatial Characteristics, Coherence Distance
22(1)
2.4.2.6 CCI
23(1)
2.4.3 Multiple Input Multiple Output (MIMO) Matrix Models
23(1)
2.4.4 Modified Stanford University Interim (SUI) Channel Models
23(2)
2.4.5 FCC Model
25(1)
2.4.6 ITU-R Models
25(1)
2.4.7 Free Space Model
26(1)
2.4.8 Two-Ray or Dual Slope Model
26(1)
2.4.9 Wideband Tapped Delay Line Channel Model
26(1)
2.4.10 Conclusions on Model Selection
26(1)
2.5 Classification of Equalizers
27(13)
2.5.1 A Note on Historical Development
27(1)
2.5.2 Classification of Adaptive Equalizers
28(2)
2.5.2.1 Nonlinear Equalizers
30(1)
2.5.3 Optimal Symbol-by-Symbol Equalizer
30(2)
2.5.4 Symbol-by-Symbol Linear Equalizers
32(2)
2.5.5 Block FIR Decision Feedback Equalizers
34(1)
2.5.6 Symbol-by-Symbol Adaptive Nonlinear Equalizer
35(1)
2.5.6.1 RBF Equalizer
35(2)
2.5.6.2 Fuzzy Adaptive Equalizer (FAE)
37(1)
2.5.6.3 Equalizer Based on Feedforward Neural Networks
38(1)
2.5.6.4 A Type-2 Neuro Fuzzy Adaptive Filter
39(1)
2.5.7 Equalizer Based on the Nearest Neighbor Rule
39(1)
2.6 Conclusion
40(5)
Further Reading
40(5)
3 Neuro-Fuzzy Equalizers for Cellular Channels
45(28)
3.1 Introduction to Neuro-Fuzzy Systems
45(4)
3.1.1 Fuzzy Systems and Type-1 Fuzzy Sets
46(1)
3.1.2 Type-2 Fuzzy Sets
46(1)
3.1.2.1 Extension Principle
46(2)
3.1.3 Operations on Type-2 Fuzzy Sets
48(1)
3.2 Type-2 Fuzzy Adaptive Filter
49(16)
3.2.1 TE for Time-Varying Channels
51(4)
3.2.1.1 Designing the Type-2 FAF
55(1)
3.2.1.2 Simulations
56(1)
3.2.1.3 Observations
56(3)
3.2.2 DFE for Time-Varying Channel Using a Type-2 FAF
59(1)
3.2.2.1 Design of a DFE Based on a Type-2 FAF
59(3)
3.2.2.2 Simulations
62(1)
3.2.2.3 Observations
62(1)
3.2.3 Inferences
62(3)
3.3 Adaptation of the Type-2 FAF for the Indoor Environment
65(4)
3.3.1 Log--Distance Path Loss Model
65(1)
3.3.2 Ericsson Multiple Breakpoint Model
65(1)
3.3.3 Attenuation Factor Model
65(1)
3.3.4 DFE for an Indoor Mobile Radio Channel
66(1)
3.3.4.1 Channel Equation
66(3)
3.3.5 Co-Channel Interference Suppression
69(1)
3.4 Conclusion
69(4)
Further Reading
70(3)
4 ANFIS-Based Channel Equalizer
73(40)
4.1 Introduction
73(1)
4.2 Methods of Channel Equalizer Analysis and Design
74(6)
4.2.0.1 FIS
75(2)
4.2.0.2 ANFIS
77(1)
4.2.1 ANFIS Architecture and Functional Layers
78(1)
4.2.1.1 Node Functions
79(1)
4.3 Mobile Channel Equalizer Based on ANFIS
80(23)
4.3.1 Simulation of a Channel Equalizer Using MATLAB®
80(2)
4.3.2 Description of the ANFIS-Based Channel Equalizer
82(3)
4.3.3 Results of Simulations
85(17)
4.3.4 Interpretation of Results and Observations
102(1)
4.4 Equalization of UWB Systems Using ANFIS
103(7)
4.4.1 Introduction to UWB
103(1)
4.4.2 Conventional Channel Models for UWB
104(1)
4.4.2.1 The Modified SV/IEEE 802.15.3a Model
105(1)
4.4.2.2 The 802.15.4a Model for High Frequencies (4a HF)
105(1)
4.4.2.3 The 802.15.4a Model for Low Frequencies (4a LF)
105(1)
4.4.2.4 Channel Covariance Matrix (CCM) Formulation
106(1)
4.4.2.5 Simulation of an ANFIS Equalizer for UWB Based on CCM
107(3)
4.4.3 Conclusions on an ANFIS-Based Equalizer for UWB
110(1)
4.5 Conclusion
110(3)
Further Reading
111(2)
5 Compensatory Neuro-Fuzzy Filter (CNFF)
113(12)
5.1 Introduction
113(1)
5.2 CNFF
114(3)
5.2.1 Outline of the CNFF
114(1)
5.2.2 Details of Compensatory Operations
115(2)
5.3 Structure of CNFFs
117(5)
5.3.1 Online Learning Algorithm
118(1)
5.3.1.1 Structure Learning Algorithm
118(1)
5.3.1.2 Parameter Learning Algorithm
119(1)
5.3.1.3 A Digital Communication System with AWGN and CCI
119(2)
5.3.1.4 Channel Models and Simulation
121(1)
5.3.2 Simulation Results
121(1)
5.4 Conclusion
122(3)
Further Reading
123(2)
6 Radial Basis Function Framework
125(18)
6.1 Introduction
125(1)
6.2 RBF Neural Networks
126(2)
6.2.1 Review of Previous Work
126(1)
6.2.1.1 Motivation for the Unified Framework
127(1)
6.3 Type-2 FAF Equalizer
128(1)
6.3.0.1 A Simplified Mathematical Formulation for FAF-II
129(1)
6.4 CNFF
129(2)
6.4.0.1 A Mathematical Formulation of CNFF
131(1)
6.5 ANFIS-Based Channel Equalizer
131(9)
6.5.0.1 A Mathematical Formulation of the ANFIS Equalizer
132(1)
6.5.0.2 Simulations
133(7)
6.6 Conclusion
140(3)
Further Reading
141(2)
7 Modular Approach to Channel Equalization
143(26)
7.1 Introduction
143(2)
7.2 Nonlinear Channel Models
145(1)
7.3 Nonlinear Channel Equalizers
146(18)
7.3.1 Nonlinear Equalizers Based on RBF Neural Network
146(17)
7.3.2 Nonlinear Equalizers Based on MLPs
163(1)
7.3.3 Nonlinear Equalizers Based on FAFs
164(1)
7.4 A Modular Approach for Nonlinear Channel Equalizers
164(1)
7.5 Simulation Results
165(1)
7.6 Conclusion
165(4)
Further Reading
166(3)
8 OFDM and Spatial Diversity
169(10)
8.1 Introduction
169(1)
8.2 CDMA
170(3)
8.2.1 Processing Gain of CDMA Systems
171(1)
8.2.2 Generation of CDMA
171(1)
8.2.3 CDMA Forward Link Encoding
172(1)
8.2.4 CDMA Reverse Link Decoding
173(1)
8.3 COFDM
173(3)
8.3.1 OFDM Transmission and Reception
174(1)
8.3.1.1 Adding a Guard Period to OFDM
175(1)
8.4 Conclusion
176(3)
Further Reading
177(2)
9 Conclusion
179(4)
9.1 Introduction
179(1)
9.2 Major Achievements of the Work
180(1)
9.3 Limitations of the Work
181(1)
9.4 Scope for Further Research
181(2)
Further Reading
182(1)
Index 183
K.C. Raveendranathan holds a bachelors degree in electronics and communication engineering, masters in electrical communication engineering, and Ph.D. in computer science and engineering. He worked in BEL Bangalore prior to joining College of Engineering Trivandrum, as a faculty. Now he is working as principal and professor in LBS Institute of Technology for Women Poojappura, Trivandrum, Kerala, India. Raveendranathan has over 25 years of teaching experience in various reputed government engineering colleges in Kerala. He has published over 12 papers in national/international conferences and journals and guided over a dozen UG and PG theses. He has also authored three textbooks. He is a life member of ISTE, Life Fellow of IETE, Life Fellow and Chartered Engineer of IE (India), and a senior member of IEEE.
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