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. Published by John Wiley & Sons, Inc., Hoboken, New Jersey. Published simultaneously in Canada. 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Wiley also publishes its books in a variety of electronic formats. Some content that appears in print, however, may not be available in electronic format. 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