Cognitive Radio Technology (eBook)

eBook Download: EPUB

2009 | 2. Auflage
848 Seiten
Elsevier Science (Verlag)
978-0-08-092316-1 (ISBN)

The new edition of this cutting edge reference, which gives a thorough knowledge of principles, implementation details, standards, policy issues in one volume, enables the RF and wireless engineer to master and apply today's cognitive radio technologies.

Bruce Fette, PhD, is Chief Scientist in the Communications Networking Division of General Dynamics C4 Systems in Scottsdale, AZ. He?worked with the Software Defined Radio (SDR) Forum from its inception, currently performing the role of Technical Chair, and is a panelist for the IEEE Conference on Acoustics Speech and Signal Processing Industrial Technology Track. He currently heads the General Dynamics Signal Processing Center of Excellence in the Communication Networks Division. Dr. Fette has 36 patents and has been awarded the 'Distinguished Innovator Award'.

* Foreword and a chapter contribution by Joe Mitola, the creator of the field
* Discussion of cognitive aids to the user, spectrum owner, network operator
* Explanation of capabilities such as time - position awareness, speech and language awareness, multi-objective radio and network optimization, and supporting database infrastructure
* Detailed information on product implementation to aid product developers
* Thorough descriptions of each cognitive radio component technology provided by leaders of their respective fields, and the latest in high performance analysis - implementation techniques
* Explanations of the complex architecture and terminology of the current standards activities
* Discussions of market opportunities created by cognitive radio technology

This book gives a thorough knowledge of cognitive radio concepts, principles, standards, spectrum policy issues and product implementation details. In addition to 16 chapters covering all the basics of cognitive radio, this new edition has eight brand-new chapters covering cognitive radio in multiple antenna systems, policy language and policy engine, spectrum sensing, rendezvous techniques, spectrum consumption models, protocols for adaptation, cognitive networking, and information on the latest standards, making it an indispensable resource for the RF and wireless engineer. The new edition of this cutting edge reference, which gives a thorough knowledge of principles, implementation details, standards, policy issues in one volume, enables the RF and wireless engineer to master and apply today's cognitive radio technologies. Bruce Fette, PhD, is Chief Scientist in the Communications Networking Division of General Dynamics C4 Systems in Scottsdale, AZ. He worked with the Software Defined Radio (SDR) Forum from its inception, currently performing the role of Technical Chair, and is a panelist for the IEEE Conference on Acoustics Speech and Signal Processing Industrial Technology Track. He currently heads the General Dynamics Signal Processing Center of Excellence in the Communication Networks Division. Dr. Fette has 36 patents and has been awarded the "e;Distinguished Innovator Award"e;. - Foreword and a chapter contribution by Joe Mitola, the creator of the field- Discussion of cognitive aids to the user, spectrum owner, network operator- Explanation of capabilities such as time position awareness, speech and language awareness, multi-objective radio and network optimization, and supporting database infrastructure- Detailed information on product implementation to aid product developers- Thorough descriptions of each cognitive radio component technology provided by leaders of their respective fields, and the latest in high performance analysis implementation techniques- Explanations of the complex architecture and terminology of the current standards activities- Discussions of market opportunities created by cognitive radio technology

Front cover 1
Half title page 2
Title page 4
Copyright page 5
Table of contents 6
Preface 14
CRs Know Radio Like TellMe Knows 800 Numbers 14
Future iCRs See What You See, Discovering RF Uses, Needs, and Preferences 15
CRs Hear What You Hear, Augmenting Your Personal Skills 15
Ideal CRs Learn to Differentiate Speakers to Reduce Confusion 16
More Flexible Secondary Use of the Radio Spectrum 17
Acknowledgments 18
Chapter 1: History and Background of Cognitive Radio Technology 22
The Vision of Cognitive Radio 22
History and Background Leading to Cognitive Radio 22
A Brief History of Software Defined Radio 24
Basic SDR 27
Cognitive Radio 34
Spectrum Management 37
US Government Roles in Cognitive Radio 42
How Smart Is Useful? 43
Organization of This Book 44
References 47
Chapter 2: Communications Policy and Spectrum Management 48
Introduction 48
Cognitive Radio Technology Enablers 49
New Opportunities in Spectrum Access 51
Policy Challenges for Cognitive Radios 60
Telecommunications Policy and Technology Impact on Regulation 69
Global Policy Interest in Cognitive Radios 75
Summary 82
Exercises 84
References 84
Chapter 3: The Software-Defined Radio as a Platform for Cognitive Radio 86
Introduction 86
Hardware Architecture 88
Software Architecture 100
SDR Development and Design 103
Applications 115
Development 118
Cognitive Waveform Development 120
Summary 123
References 124
Chapter 4: Cognitive Radio: The Technologies Required 126
Introduction 126
Radio Flexibility and Capability 126
Aware, Adaptive, and Cognitive Radios 132
Comparison of Radio Capabilities and Properties 135
Available Technologies for Cognitive Radios 136
Funding and Research in Cognitive Radios 144
Timeline for Cognitive Radios 154
Update of CR-Specific Technologies 156
Summary 159
Exercises 160
References 161
Chapter 5: Spectrum Awareness and Access Considerations 164
Dynamic Spectrum Awareness and Access Objectives 164
Prior Work in Spectrum Awareness and Access 165
Some End-to-End DSA Example Implementations 167
Dynamic Spectrum Awareness 168
Front-End Linearity Management 182
Dynamic Spectrum Access Objectives 197
Spectral Footprint Management Objectives 207
Implications on Network-Level Decision Making 209
Summary 212
Exercises 212
References 213
Chapter 6: Cognitive Policy Engines 216
The Promise of Policy Management for Radios 216
Background and Definitions 216
Spectrum Policy 218
Antecedents for Cognitive Policy Management 220
Policy Engine Architectures for Radio 226
Integration of Policy Engines into Cognitive Radio 231
The Future of Cognitive Policy Management 237
Summary 240
References 241
Chapter 7: Cognitive Techniques: Physical and Link Layers 244
Introduction 244
Optimizing Physical and Link Layers for Multiple Objectives under Current Channel Conditions 245
Defining the Cognitive Radio 246
Developing Radio Controls (Knobs) and Performance Measures (Meters) 247
Multiobjective Decision-Making Theory and Its Application to Cognitive Radio 253
The Multiobjective Genetic Algorithm for Cognitive Radios 261
Advanced Genetic Algorithm Techniques 273
Need for a Higher-Layer Intelligence 277
How the Intelligent Computers Operate 279
Summary 281
References 283
Chapter 8: Cognitive Techniques: Position Awareness 286
Introduction 286
Radio Geolocation and Time Services 287
Network Localization 291
Additional Geolocation Approaches 293
Network-Based Approaches 302
Boundary Decisions 302
Example of Cellular Phone 911 Geolocation for First Responders 306
Interfaces to Other Cognitive Technologies 307
Summary 308
Exercise 309
References 309
Chapter 9: Cognitive Techniques: Three Types of Network Awareness 310
Introduction 310
Applications and Their Requirements 310
Network Awareness: Protocols 312
Situation-Aware Protocols in Edge Network Technologies 316
Network Awareness: Node Capabilities and Cooperation 318
A Distributed System of Radios—The Radio Team 319
Network Awareness: Node Location and Cognition for Self-Placement 321
Summary 323
Exercises 323
References 324
Chapter 10: Cognitive Services for the User 326
Introduction 326
Speech and Language Processing 327
Concierge Services 341
Summary 343
References 343
Chapter 11: Network Support: The Radio Environment Map 346
Introduction 346
REM: The Vehicle for Providing Network Support to CRs 347
Obtaining Cognition with REM: A Systematic Top-Down Approach 351
High-Level System Design of REM 359
Network Support Scenarios and Applications 373
Example Applications of REM to Cognitive Wireless Networks 376
Summary and Open Issues 384
Exercises 385
References 385
Chapter 12: Cognitive Research: Knowledge Representation and Learning 388
Introduction 388
Knowledge Representation and Reasoning 392
Machine Learning 403
Implementation Considerations 414
Summary 416
Exercises 418
References 419
Chapter 13: The Role of Ontologies in Cognitive Radios 422
Overview of Ontology-Based Radios 422
Knowledge-Intense Characteristics of Cognitive Radios 422
Ontologies and Their Roles in Cognitive Radio 427
A Layered Ontology and Reference Model 433
Examples 439
Open Research Issues 444
Summary 447
Exercises 447
References 448
Chapter 14: Cognitive Radio Architecture 450
Introduction 450
CRA-I: Functions, Components, and Design Rules 452
CRA-II: The Cognition Cycle 469
CRA-III: The Inference Hierarchy 474
CRA-IV: Architecture Maps 482
CRA-V: Building the CRA on SDR Architectures 488
Cognition Architecture Research Topics 499
Industrial-Strength CR Design Rules 499
Summary and Future Directions 501
Exercises 502
References 503
Chapter 15: Cognitive Radio Performance Analysis 504
Introduction 504
The Analysis Problem 506
Traditional Engineering Analysis Techniques 512
Applying Game Theory to the Analysis Problem 523
Relevant Game Models 532
Summary 550
Exercises 551
References 552
Chapter 16: Cognitive Radio in Multiple-Antenna Systems 556
Introduction 556
Multiple-Antenna Techniques 557
Cognitive Capability in an MA System 562
Application to Next-Generation Wireless Communications 574
Summary 576
References 577
Chapter 17: Cognitive Radio Policy Language and Policy Engine 578
Introduction 578
Benefits of a Policy-Based Approach 580
neXt-Generation Spectrum Policy Architecture 582
Policy Language and Engine Design 584
SRI Spectrum Policy Language 588
SRI Policy Engine 594
SRI Policy Engine Demonstration 603
Lessons Learned and Future Work 609
Summary 611
References 612
Chapter 18: Spectrum Sensing Based on Spectral Correlation 614
Introduction 614
The Statistical Nature of Communication Signals 625
Spectrum Sensing Based on Spectral Correlation 634
Application to Modern Communication Signals 637
Summary 650
Exercises 651
References 653
Chapter 19: Rendezvous in Cognitive Radio Networks 656
Introduction 656
The Use of Control Channels 658
Blind Rendezvous 659
Link Maintenance and the Effect of Primary Users 664
Summary 665
References 665
Chapter 20: Spectrum-Consumption Models 666
Introduction 666
Reconciling DSA and Spectrum Management 667
The Location-Based Method to Specify RF Spectrum Rights 674
Optimized Data Structures for the LBSR 690
Constructing Rights 697
Applications 703
Future Research and Work 706
Summary 707
References 707
Chapter 21: Protocols for Adaptation in Cognitive Radio 710
Introduction 710
Modulation 711
Error-Control Codes 712
Performance Measures for a Code-Modulation Library 713
Special Subsets of the Code-Modulation Library 717
Receiver Statistics 719
Initial Power Adjustment 720
Adaptive Transmission 731
Protocol Throughput Performance for Dynamic Channels 733
Summary 739
Exercises 740
References 741
Chapter 22: Cognitive Networking 744
Introduction 744
Current CN Research 748
Research Holes and Future Directions 757
Summary 760
References 760
Chapter 23: The Role of IEEE Standardization in Next-Generation Radio and Dynamic Spectrum Access Developments 764
Introduction 764
Definitions and Terminology 768
Overview of the IEEE Standards Activities 770
IEEE 802 Cognitive Radio-Related Activities 772
IEEE SCC41: Dynamic Spectrum Access Networks 781
Potential for New Products and Systems 793
Summary 795
References 795
Chapter 24: The Really Hard Problems 798
Introduction 798
Discussion and Summary of CR Technologies 798
Services Offered to Wireless Networks Through Infrastructure 805
References 810
Glossary 812
Index 824

Preface

Dr. Joseph Mitola III

Stevens Institute of TechnologyCastle Point on the Hudson, New Jersey

This preface1 takes a visionary look at ideal cognitive radios (iCRs) that integrate advanced software-defined radios (SDRs) with CR techniques to arrive at radios that learn to help their user using computer vision, high-performance speech understanding, GPS navigation, sophisticated adaptive networking, adaptive physical layer radio waveforms, and a wide range of machine learning processes.

1Adapted from J. Mitola III, Cognitive Radio Architecture: The Engineering Foundations of Radio XML, Wiley, 2006.

CRs Know Radio Like TellMe Knows 800 Numbers

When you dial 1-800-555-1212, a speech synthesis algorithm may say, “Toll Free Directory Assistance powered by TellMe ®. Please say the name of the listing you want.” If you mumble, it says, “OK, United Airlines. If that is not what you wanted press 9, otherwise wait while I look up the number.” Reportedly, some 99 percent of the time TellMe gets it right, replacing the equivalent of thousands of directory assistance operators of yore. TellMe, a speech-understanding system, achieves a high degree of success by its focus on just one task: finding a toll-free telephone number. Narrow task focus is one key to algorithm successes.

The cognitive radio architecture (CRA) is the building block from which to build cognitive wireless networks (CWN), the wireless mobile offspring of TellMe. CRs and networks are emerging as practical, real-time, highly focused applications of computational intelligence technology. CRs differ from the more general artificial intelligence (AI) based services (e.g., intelligent agents, computer speech, and computer vision) in degree of focus. Like TellMe, ideal cognitive radios (iCRs) focus on very narrow tasks. For iCRs, the task is to adapt radio-enabled information services to the specific needs of a specific user. TellMe, a network service, requires substantial network computing resources to serve thousands of users at once. CWNs, on the other hand, may start with a radio in your purse or on your belt—a cell phone on steroids—focused on the narrow task of creating from myriad available wireless information networks and resources just what is needed by one user: you. Each CR fanatically serves the needs and protects the personal information of just one owner via the CRA using its audio and visual sensory perception and autonomous machine learning.

TellMe is here and now, while iCRs are emerging in global wireless research centers and industry forums such as the Software-Defined Radio Forum and Wireless World Research Forum (WWRF). This book introduces the technologies to evolve SDR to dynamic spectrum access (DSA) and towards iCR systems. It introduces technical challenges and approaches, emphasizing DSA and iCR as a technology enabler for rapidly emerging commercial CWN services.

Future iCRs See What You See, Discovering RF Uses, Needs, and Preferences

Although the common cell phone may have a camera, it lacks vision algorithms, so it does not see what it is imaging. It can send a video clip, but it has no perception of the visual scene in the clip. With vision processing algorithms, it could perceive and categorize the visual scene to cue more effective radio behavior. It could tell whether it were at home, in the car, at work, shopping, or driving up the driveway at home. If vision algorithms show you are entering your driveway in your car, an iCR could learn to open the garage door for you wirelessly. Thus, you would not need to fish for the garage door opener, yet another wireless gadget. In fact, you would not need a garage door opener anymore, once CRs enter the market. To open the car door, you will not need a key fob either. As you approach your car, your iCR perceives this common scene and, as trained, synthesizes the fob radio frequency (RF) transmission to open the car door for you.

CRs do not attempt everything. They learn about your radio use patterns leveraging a-priori knowledge of radio, generic users, and legitimate uses of radios expressed in a behavioral policy language. Such iCRs detect opportunities to assist you with your use of the radio spectrum, accurately delivering that assistance with minimum tedium.

Products realizing the visual perception of this vignette are demonstrated on laptop computers today. Reinforcement learning (RL) and case-based reasoning (CBR) are mature machine learning technologies with radio network applications now being demonstrated in academic and industrial research settings as technology pathfinders for iCR2 and CWN. 3 Two or three Moore's law cycles, or three to five years from now, these vision and learning algorithms will fit into your cell phone. In the interim, CWNs will begin to offer such services, presenting consumers with new trade-offs between privacy and ultrapersonalized convenience.

2J. Mitola III, Cognitive Radio Architecture, 2006.

3M. Katz and S. Fitzek, Cooperation in Wireless Networks, Elsevier, 2007.

CRs Hear What You Hear, Augmenting Your Personal Skills

The cell phone you carry is deaf. Although this device has a microphone, it lacks embedded speech-understanding technology, so it does not perceive what it hears. It can let you talk to your daughter, but it has no perception of your daughter, nor of your conversation's content. If it had speech-understanding technology, it could perceive your dialog. It could detect that you and your daughter are talking about a common subjects such as a favorite song. With iCR, speech algorithms detect your daughter telling you by cell phone that your favorite song is now playing on WDUV. As an SDR, not just a cell phone, your iCR determines that she and you both are in the WDUV broadcast footprint and tunes its broadcast receiver chipset to FM 105.5 so that you can hear “The Rose.” With your iCR, you no longer need a transistor radio in your pocket, purse, or backpack. In fact, you may not need an MP3 player, electronic game, and similar products as high-end CR's enter the market (the CR may become the single pocket pal instead). While today's personal electronics value propositions entail product optimization, iCR's value proposition is service integration to simplify and streamline your daily life. The iCR learns your radio listening and information use patterns, accessing songs, downloading games, snipping broadcast news, sports, and stock quotes you like as the CR reprograms its internal SDR to better serve your needs and preferences. Combining vision and speech perception, as you approach your car, your iCR perceives this common scene and, as you had the morning before, tunes the car radio to WTOP for your favorite “traffic and weather together on the eights.”

For effective machine learning, iCRs save speech, RF, and visual cues, all of which may be recalled by the radio or the user, acting as an information prosthetic to expand the user's ability to remember details of conversations, and snapshots of scenes, augmenting the skills of the ?Owner/?. 4 Because of the brittleness of speech and vision technologies, CRs may also try to “remember everything” like a continuously running camcorder. Since CRs detect content (e.g., speakers’ names and keywords such as “radio” and “song”), they may retrieve content requested by the user, expanding the user's memory in a sense. CRs thus could enhance the personal skills of their users (e.g., memory for detail).

4Semantic Web: Researchers formulate CRs as sufficiently speech-capable to answer questions about ?Self/? and the ?Self/? use of ?Radio/? in support of its ?Owner/?. When an ordinary concept, such as “owner,” has been translated into a comprehensive ontological structure of computational primitives (e.g., via Semantic Web technology), the concept becomes a computational primitive for autonomous reasoning and information exchange. Radio XML, an emerging CR derivative of the eXtensible Markup Language (XML) offers to standardize such radio-scene perception primitives. They are highlighted in this brief treatment by ?Angle-brackets/?. All CR have a ?Self/?, a ?Name/?, and an ?Owner/?. The ?Self/? has capabilities such as ?GSM/? and ?SDR/?, a self-referential computing architecture, which is guaranteed to crash unless its computing ability is limited to real-time response tasks; this is appropriate for a CR but may be too limiting for general-purpose computing.

Ideal CRs Learn to Differentiate Speakers to Reduce Confusion

To further limit combinatorial explosion in speech, CR may form speaker models—statistical summaries of speech patterns—particularly of the ?Owner/?. Speaker modeling is particularly reliable when the ?Owner/? uses the iCR as a cell phone to place a call. Contemporary speaker classification algorithms differentiate male from female speakers with a high level of accuracy. With a few different speakers to be recognized (i.e., fewer than 10 in a family) and with reliable side information (e.g., the speaker's telephone number), today's state-of-the-art algorithms recognize individual speakers with better than 95 percent accuracy.

Over time, each iCR can learn the speech patterns of its ?Owner/? in order to learn from the ?Owner/? and not be confused by other speakers. The iCR may thus leverage experience incrementally to achieve increasingly sophisticated dialogs. Today, a 3-GHz laptop supports this level of speech understanding and dialog synthesis in real time, making it likely to be available in a cell phone in 3 to 5...

Erscheint lt. Verlag	28.4.2009
Sprache	englisch
Themenwelt	Sachbuch/Ratgeber
	Technik ► Elektrotechnik / Energietechnik
	Technik ► Nachrichtentechnik
ISBN-10	0-08-092316-X / 008092316X
ISBN-13	978-0-08-092316-1 / 9780080923161

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