Cognitive Radio Architecture: The Engineering Foundations of Radio XML

488 Pages·2006·7.964 MB·English

by  Joseph Mitola III

COGNITIVE RADIO
ARCHITECTURE
The Engineering Foundations of
Radio XML
JOSEPH MITOLA III
A JOHN WILEY & SONS, INC., PUBLICATION

COGNITIVE RADIO
ARCHITECTURE

COGNITIVE RADIO
ARCHITECTURE
The Engineering Foundations of
Radio XML
JOSEPH MITOLA III
A JOHN WILEY & SONS, INC., PUBLICATION
This book is printed on acid-free paper.
Copyright © 2006 by John Wiley & Sons, Inc. All rights reserved.
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Library of Congress Cataloging-in-Publication Data:
Mitola, Joseph.
Cognitive radio architecture : the engineering foundations of radio XML / by
Joseph Mitola.
p. cm.
Includes bibliographical references and index.
ISBN-10: 0-471-74244-9
ISBN-13: 978-0-471-74244-9
1. Software radio. 2. XML (Document markup language) I. Title.
TK5103.4875.M58 2006
621.384–dc22
2005051362
Printed in the United States of America
10 9 8 7 6 5 4 3 2 1
CONTENTS
PREFACE ix
ACKNOWLEDGMENTS xi
1 INTRODUCTION 1
1.1 Perception / 3
1.2 Aware, Adaptive, or Cognitive? / 5
1.3 Adaptation / 8
1.4 Cognition / 10
1.5 Cognitive Radio and Public Policy / 15
1.6 Are We There Yet? / 16
1.7 Key Questions / 18
1.8 Organization of the Text / 19
1.9 Exercises / 20
I FOUNDATIONS
2 TECHNICAL OVERVIEW 25
2.1 The iCR Has Seven Capabilities / 25
2.2 Sensing and Perception: What and Whom to Perceive / 27
2.3 Ideal Cognitive Radio (iCR) Platform Evolution / 41
2.4 The serModel of Machine Learning for iCR / 47
2.5 Architecture / 51
v
vi CONTENTS
2.6 Synoptic iCR Functional Defi nition / 56
2.7 Exercises / 56
3 EVOLVING FROM AWARE AND ADAPTIVE TO
COGNITIVE RADIO 58
3.1 Revolution or Evolution? / 58
3.2 Moving Day / 59
3.3 Developing AML for Genie / 62
3.4 Learning Etiquette / 73
3.5 Value Proposition for AML in AACR / 75
3.6 Exercises / 79
4 AUTONOMOUS MACHINE LEARNING FOR AACR 80
4.1 Machine Learning Framework / 80
4.2 Histogram as a Discovery Algorithm / 85
4.3 User-Domain Learning / 88
4.4 Radio-Domain Learning / 97
4.5 Reinforcement, Extension, and Constraint Discovery / 108
4.6 Learning Strategies / 118
4.7 Exercises / 121
5 COGNITIVE RADIO ARCHITECTURE 123
5.1 CRA I: Functions, Components, and Design Rules / 124
5.2 CRA II: The Cognition Cycle / 134
5.3 CRA III: The Inference Hierarchy / 138
5.4 CRA IV: Architecture Maps / 143
5.5 CRA V: Building the CRA on SDR Architectures / 144
5.6 Cognition Architecture Research Topics / 152
5.7 Exercises / 152
II RADIO-DOMAIN COMPETENCE
6 RADIO-DOMAIN USE CASES 157
6.1 Radio Use-Case Metrics / 157
6.2 FCC Unused TV Spectrum Use Case / 163
6.3 Demand Shaping Use Case / 170
6.4 Military Market Segment Use Cases / 176
CONTENTS vii
6.5 RF Knowledge That Saves Lives / 177
6.6 Prognostication / 180
6.7 Exercises / 180
7 RADIO KNOWLEDGE 183
7.1 Radio-Domain Overview / 183
7.2 Knowledge of the HF Radio Band / 195
7.3 Knowledge of the LVHF Radio Band / 208
7.4 Radio Noise and Interference / 224
7.5 Knowledge of the VHF Radio Band / 228
7.6 Knowledge of the UHF Radio Band / 237
7.7 Knowledge of the SHF Radio Band / 246
7.8 Knowledge of EHF, Terahertz, and Free Space Optics / 256
7.9 Satellite Communications Knowledge / 260
7.10 Cross-Band/Mode Knowledge / 267
8 IMPLEMENTING RADIO-DOMAIN SKILLS 275
8.1 Cognitive Radio Architecture Structures Radio Skills / 276
8.2 Embedded Databases Enable Skills / 281
8.3 Production Systems Enable Skills / 288
8.4 Embedded Inference Enables Skills / 291
8.5 Radio Knowledge Objects (RKOs) / 296
8.6 Evolving Skills Via RKO and RDH / 303
8.7 Implementing Spatial Skills / 305
8.8 Generalized <Information-landscape/> / 318
8.9 Microworlds / 323
8.10 Radio Skills Conclusions / 325
8.11 Exercises / 326
III USER-DOMAIN COMPETENCE
9 USER-DOMAIN USE CASES 331
9.1 Emergency Companion Use Case / 331
9.2 Offi ce Assistant Use Case / 333
9.3 Cognitive Assistants for Wireless / 334
9.4 User Skill Enhancements / 343
9.5 Exercises / 346