Cognitive Radio Architecture: The Engineering Foundations of Radio XML

An exciting new technology, described by the one who invented it This is the first book dedicated to cognitive radio, a promising new technology that is poised to revolutionize the telecommunications industry with increased wireless flexibility. Cognitive radio technology integrates computational intelligence into software-defined radio for embedded intelligent agents that adapt to RF environments and user needs. Using this technology, users can more fully exploit the radio spectrum and services available from wireless connectivity. For example, an attempt to send a 10MB e-mail in a zone where carrier charges are high might cause a cognitive radio to alert its user and suggest waiting until getting to the office to use the LAN instead. Cognitive Radio Architecture examines an "ideal cognitive radio" that features autonomous machine learning, computer vision, and spoken or written language perception. The author of this exciting new book is the inventor of the technology and a leader in the field. Following his step-by-step introduction, readers can start building aware/adaptive radios and then make steps towards cognitive radio. After an introduction to adaptive, aware, and cognitive radio, the author develops three major themes in three sections: Foundations Radio Competence User Domain Competence The book makes the design principles of cognitive radio more accessible to students of teleinformatics, as well as to wireless communications systems developers. It therefore embraces the practice of cognitive radio as well as the theory. In particular, the publication develops a cognitive architecture that integrates disparate disciplines, including autonomous machine learning, computer vision, and language perception technologies. In addition, for the convenience of the reader, Web resources introducing key concepts such as speech applications programmer interfaces (APIs) are included. Although still five to ten years away from full deployment, telecommunications giants and research labs around the world are already dedicating R&D to this new technology. Telecommunications engineers as well as advanced undergraduate and graduate students can learn the promising possibilities of this innovative technology from the one who invented it.  

Table of 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 2.6 Synoptic iCR Functional Definition 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 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 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 Office Assistant Use Case 333 9.3 Cognitive Assistants for Wireless 334 9.4 User Skill Enhancements 343 9.5 Exercises 346 10 User-Domain Knowledge 347 10.1 Users’ Natural Language Expression 348 10.2 Acoustic Sensory Perception 352 10.3 Visual Sensory Perception 359 10.4 Audio-Visual Integration 363 10.5 Lexical Conceptual Semantics (LCS) 366 10.6 Other Sensors 369 10.7 Architecture Implications 369 10.8 Exercises 369 11 Implementing User-Domain Skills 372 11.1 Integrating Cognition 373 11.2 Autonomous Extensibility 382 11.3 Supervised Extensibility 401 11.4 Uncertainty 407 11.5 Learning Requires Grounding 417 11.6 Sleep Cycles 423 11.7 Pitfalls and Opportunities 424 11.8 Exercises 426 12 Semantic Radio 428 12.1 CYC, eBusiness Solutions, and the Semantic Web 428 12.2 CYC Case Study 429 12.3 CYC Implications 437 12.4 Web Languages 439 12.5 Radio XML 439 12.6 Conclusions 443 Glossary 444 References 451 Index 467