E-Book, Englisch, 848 Seiten
ISBN: 978-0-08-092316-1
Verlag: Elsevier Science & Techn.
Format: EPUB
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)
* 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
Autoren/Hrsg.
Weitere Infos & Material
1;Front cover;1
2;Half title page;2
3;Title page;4
4;Copyright page;5
5;Table of contents;6
6;Preface;14
6.1;CRs Know Radio Like TellMe Knows 800 Numbers;14
6.2;Future iCRs See What You See, Discovering RF Uses, Needs, and Preferences;15
6.3;CRs Hear What You Hear, Augmenting Your Personal Skills;15
6.4;Ideal CRs Learn to Differentiate Speakers to Reduce Confusion;16
6.5;More Flexible Secondary Use of the Radio Spectrum;17
7;Acknowledgments;18
8;Chapter 1: History and Background of Cognitive Radio Technology;22
8.1;The Vision of Cognitive Radio;22
8.2;History and Background Leading to Cognitive Radio;22
8.3;A Brief History of Software Defined Radio;24
8.4;Basic SDR;27
8.5;Cognitive Radio;34
8.6;Spectrum Management;37
8.7;US Government Roles in Cognitive Radio;42
8.8;How Smart Is Useful?;43
8.9;Organization of This Book;44
8.10;References;47
9;Chapter 2: Communications Policy and Spectrum Management;48
9.1;Introduction;48
9.2;Cognitive Radio Technology Enablers;49
9.3;New Opportunities in Spectrum Access;51
9.4;Policy Challenges for Cognitive Radios;60
9.5;Telecommunications Policy and Technology Impact on Regulation;69
9.6;Global Policy Interest in Cognitive Radios;75
9.7;Summary;82
9.8;Exercises;84
9.9;References;84
10;Chapter 3: The Software-Defined Radio as a Platform for Cognitive Radio;86
10.1;Introduction;86
10.2;Hardware Architecture;88
10.3;Software Architecture;100
10.4;SDR Development and Design;103
10.5;Applications;115
10.6;Development;118
10.7;Cognitive Waveform Development;120
10.8;Summary;123
10.9;References;124
11;Chapter 4: Cognitive Radio: The Technologies Required;126
11.1;Introduction;126
11.2;Radio Flexibility and Capability;126
11.3;Aware, Adaptive, and Cognitive Radios;132
11.4;Comparison of Radio Capabilities and Properties;135
11.5;Available Technologies for Cognitive Radios;136
11.6;Funding and Research in Cognitive Radios;144
11.7;Timeline for Cognitive Radios;154
11.8;Update of CR-Specific Technologies;156
11.9;Summary;159
11.10;Exercises;160
11.11;References;161
12;Chapter 5: Spectrum Awareness and Access Considerations;164
12.1;Dynamic Spectrum Awareness and Access Objectives;164
12.2;Prior Work in Spectrum Awareness and Access;165
12.3;Some End-to-End DSA Example Implementations;167
12.4;Dynamic Spectrum Awareness;168
12.5;Front-End Linearity Management;182
12.6;Dynamic Spectrum Access Objectives;197
12.7;Spectral Footprint Management Objectives;207
12.8;Implications on Network-Level Decision Making;209
12.9;Summary;212
12.10;Exercises;212
12.11;References;213
13;Chapter 6: Cognitive Policy Engines;216
13.1;The Promise of Policy Management for Radios;216
13.2;Background and Definitions;216
13.3;Spectrum Policy;218
13.4;Antecedents for Cognitive Policy Management;220
13.5;Policy Engine Architectures for Radio;226
13.6;Integration of Policy Engines into Cognitive Radio;231
13.7;The Future of Cognitive Policy Management;237
13.8;Summary;240
13.9;References;241
14;Chapter 7: Cognitive Techniques: Physical and Link Layers;244
14.1;Introduction;244
14.2;Optimizing Physical and Link Layers for Multiple Objectives under Current Channel Conditions;245
14.3;Defining the Cognitive Radio;246
14.4;Developing Radio Controls (Knobs) and Performance Measures (Meters);247
14.5;Multiobjective Decision-Making Theory and Its Application to Cognitive Radio;253
14.6;The Multiobjective Genetic Algorithm for Cognitive Radios;261
14.7;Advanced Genetic Algorithm Techniques;273
14.8;Need for a Higher-Layer Intelligence;277
14.9;How the Intelligent Computers Operate;279
14.10;Summary;281
14.11;References;283
15;Chapter 8: Cognitive Techniques: Position Awareness;286
15.1;Introduction;286
15.2;Radio Geolocation and Time Services;287
15.3;Network Localization;291
15.4;Additional Geolocation Approaches;293
15.5;Network-Based Approaches;302
15.6;Boundary Decisions;302
15.7;Example of Cellular Phone 911 Geolocation for First Responders;306
15.8;Interfaces to Other Cognitive Technologies;307
15.9;Summary;308
15.10;Exercise;309
15.11;References;309
16;Chapter 9: Cognitive Techniques: Three Types of Network Awareness;310
16.1;Introduction;310
16.2;Applications and Their Requirements;310
16.3;Network Awareness: Protocols;312
16.4;Situation-Aware Protocols in Edge Network Technologies;316
16.5;Network Awareness: Node Capabilities and Cooperation;318
16.6;A Distributed System of Radios—The Radio Team;319
16.7;Network Awareness: Node Location and Cognition for Self-Placement;321
16.8;Summary;323
16.9;Exercises;323
16.10;References;324
17;Chapter 10: Cognitive Services for the User;326
17.1;Introduction;326
17.2;Speech and Language Processing;327
17.3;Concierge Services;341
17.4;Summary;343
17.5;References;343
18;Chapter 11: Network Support: The Radio Environment Map;346
18.1;Introduction;346
18.2;REM: The Vehicle for Providing Network Support to CRs;347
18.3;Obtaining Cognition with REM: A Systematic Top-Down Approach;351
18.4;High-Level System Design of REM;359
18.5;Network Support Scenarios and Applications;373
18.6;Example Applications of REM to Cognitive Wireless Networks;376
18.7;Summary and Open Issues;384
18.8;Exercises;385
18.9;References;385
19;Chapter 12: Cognitive Research: Knowledge Representation and Learning;388
19.1;Introduction;388
19.2;Knowledge Representation and Reasoning;392
19.3;Machine Learning;403
19.4;Implementation Considerations;414
19.5;Summary;416
19.6;Exercises;418
19.7;References;419
20;Chapter 13: The Role of Ontologies in Cognitive Radios;422
20.1;Overview of Ontology-Based Radios;422
20.2;Knowledge-Intense Characteristics of Cognitive Radios;422
20.3;Ontologies and Their Roles in Cognitive Radio;427
20.4;A Layered Ontology and Reference Model;433
20.5;Examples;439
20.6;Open Research Issues;444
20.7;Summary;447
20.8;Exercises;447
20.9;References;448
21;Chapter 14: Cognitive Radio Architecture;450
21.1;Introduction;450
21.2;CRA-I: Functions, Components, and Design Rules;452
21.3;CRA-II: The Cognition Cycle;469
21.4;CRA-III: The Inference Hierarchy;474
21.5;CRA-IV: Architecture Maps;482
21.6;CRA-V: Building the CRA on SDR Architectures;488
21.7;Cognition Architecture Research Topics;499
21.8;Industrial-Strength CR Design Rules;499
21.9;Summary and Future Directions;501
21.10;Exercises;502
21.11;References;503
22;Chapter 15: Cognitive Radio Performance Analysis;504
22.1;Introduction;504
22.2;The Analysis Problem;506
22.3;Traditional Engineering Analysis Techniques;512
22.4;Applying Game Theory to the Analysis Problem;523
22.5;Relevant Game Models;532
22.6;Summary;550
22.7;Exercises;551
22.8;References;552
23;Chapter 16: Cognitive Radio in Multiple-Antenna Systems;556
23.1;Introduction;556
23.2;Multiple-Antenna Techniques;557
23.3;Cognitive Capability in an MA System;562
23.4;Application to Next-Generation Wireless Communications;574
23.5;Summary;576
23.6;References;577
24;Chapter 17: Cognitive Radio Policy Language and Policy Engine;578
24.1;Introduction;578
24.2;Benefits of a Policy-Based Approach;580
24.3;neXt-Generation Spectrum Policy Architecture;582
24.4;Policy Language and Engine Design;584
24.5;SRI Spectrum Policy Language;588
24.6;SRI Policy Engine;594
24.7;SRI Policy Engine Demonstration;603
24.8;Lessons Learned and Future Work;609
24.9;Summary;611
24.10;References;612
25;Chapter 18: Spectrum Sensing Based on Spectral Correlation;614
25.1;Introduction;614
25.2;The Statistical Nature of Communication Signals;625
25.3;Spectrum Sensing Based on Spectral Correlation;634
25.4;Application to Modern Communication Signals;637
25.5;Summary;650
25.6;Exercises;651
25.7;References;653
26;Chapter 19: Rendezvous in Cognitive Radio Networks;656
26.1;Introduction;656
26.2;The Use of Control Channels;658
26.3;Blind Rendezvous;659
26.4;Link Maintenance and the Effect of Primary Users;664
26.5;Summary;665
26.6;References;665
27;Chapter 20: Spectrum-Consumption Models;666
27.1;Introduction;666
27.2;Reconciling DSA and Spectrum Management;667
27.3;The Location-Based Method to Specify RF Spectrum Rights;674
27.4;Optimized Data Structures for the LBSR;690
27.5;Constructing Rights;697
27.6;Applications;703
27.7;Future Research and Work;706
27.8;Summary;707
27.9;References;707
28;Chapter 21: Protocols for Adaptation in Cognitive Radio;710
28.1;Introduction;710
28.2;Modulation;711
28.3;Error-Control Codes;712
28.4;Performance Measures for a Code-Modulation Library;713
28.5;Special Subsets of the Code-Modulation Library;717
28.6;Receiver Statistics;719
28.7;Initial Power Adjustment;720
28.8;Adaptive Transmission;731
28.9;Protocol Throughput Performance for Dynamic Channels;733
28.10;Summary;739
28.11;Exercises;740
28.12;References;741
29;Chapter 22: Cognitive Networking;744
29.1;Introduction;744
29.2;Current CN Research;748
29.3;Research Holes and Future Directions;757
29.4;Summary;760
29.5;References;760
30;Chapter 23: The Role of IEEE Standardization in Next-Generation Radio and Dynamic Spectrum Access Developments;764
30.1;Introduction;764
30.2;Definitions and Terminology;768
30.3;Overview of the IEEE Standards Activities;770
30.4;IEEE 802 Cognitive Radio-Related Activities;772
30.5;IEEE SCC41: Dynamic Spectrum Access Networks;781
30.6;Potential for New Products and Systems;793
30.7;Summary;795
30.8;References;795
31;Chapter 24: The Really Hard Problems;798
31.1;Introduction;798
31.2;Discussion and Summary of CR Technologies;798
31.3;Services Offered to Wireless Networks Through Infrastructure;805
31.4;References;810
32;Glossary;812
33;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...
