Artificial Intelligence in Wireless Communications

Front Cover
Artech House, 2009 - Computers - 213 pages
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This cutting-edge resource offers practical overview of cognitive radio, a paradigm for wireless communications in which a network or a wireless node changes its transmission or reception parameters. The alteration of parameters is based on the active monitoring of several factors in the external and internal radio environment. This book offers a detailed description of cognitive radio and its individual parts. Practitioners learn how the basic processing elements and their capabilities are implemented as modular components. Moreover, the book explains how each component can be developed and tested independently, before integration with the rest of the engine. Practitioners discover how cognitive radio uses artificial intelligence to achieve radio optimization. The book also provides an in-depth working example of the developed cognitive engine and an experimental scenario to help engineers understand its performance and behavior.
 

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Contents

62 Cognitive Engine Architecture with CBDT
99
621 Memory and Forgetfulness
101
63 Cognitive Engine CaseBased Decision Theory Implementation
102
64 Simple CBDT Example
105
65 Cognitive Radio Example Problem
113
66 Conclusion
117
References
118
Cognitive Radio Networking and Rendezvous
119

24 Artificial Intelligence in Wireless Communications
24
25 Artificial Intelligence Techniques
25
251 Neural Networks
26
252 Hidden Markov Models HMM
27
253 Fuzzy Logic
28
26 Conclusions
29
References
30
Overview and Basics of Software Defined Radios
33
31 Background
34
32 Benefits of Using SDR
36
33 Problems Faced by SDR
38
34 GNU Radio Design
39
341 The Universal Software Radio Peripheral
40
342 The USRP Version 2
41
344 Parallel Programming in GNU Radio
44
345 Flow Graph for Simulation and Experimentation
45
346 Available Knobs and Meters
47
35 Conclusions
50
References
51
Optimization of Radio Resources
53
Objective Functions
55
421 Bit Error Rate BER
56
422 Bandwidth Hz
61
423 Spectral Efficiency bitsHz
62
424 Interference
63
425 Signal to Interference Plus Noise Ratio SINR
64
426 Throughput
65
427 Power
66
428 Computational Complexity
67
A Different Perspective
68
442 PopulationBased Analysis
71
45 Conclusion
73
References
74
Genetic Algorithms for Radio Optimization
77
The Knapsack Problem
78
53 Multiobjective GA
84
54 Wireless System Genetic Algorithm
86
541 Details of Chromosome Structure
88
542 Objective Function Definition
90
543 Optimal Individual Selection
91
55 Conclusions
93
References
94
Decision Making with CaseBased Learning
97
61 CaseBased Decision Theory
98
71 Waveform Distribution and Rendezvous
120
72 Cognitive Radio Networks
121
73 Distributed AI
122
74 Conclusions
123
Example Cognitive Engine
125
81 Functional System Design
126
82 Simple Simulations
129
822 BER and Power 1
131
823 BER and Power 2
132
824 Throughput
134
83 Interference Environment
137
Simple BER Tests
138
Sensor Problems
140
Correcting for Sensors
141
Throughput with Low Spectral Footprint
146
84 CaseBased Decision Theory Example
148
85 OvertheAir Results
149
86 Conclusions
155
References
156
Conclusions
157
91 Application to Multicarrier Waveforms
158
92 Strategies Not Waveforms
159
93 Enhanced Learning Systems
160
94 Final Thoughts
161
Analysis of GNU Radio Simulation
163
Additional BER Formulas
169
References
171
OProfile and Results of Profiling GNU Radio
173
References
176
XML and DTD Representation of the Cognitive Components
181
D2 Objectives Sensor
185
D3 Meters Sensor
186
D4 PSD Sensor
187
Optimal Solutions of Knapsack Problems
191
Simulation of an SINR Sensor
195
F2 Simulation
196
F3 MATLAB Code
198
F32 Plotting SINR with No Interference Power
200
F33 Plotting SINR with Varying Interference Power
201
Acronyms
203
About the Authors
207
Index
211
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