Academic Press is an imprint of Elsevier 30 Corporate Drive, Suite 400 Burlington, MA 01803 This book is printed on acid-free paper Copyright © 2009 by Elsevier Inc All rights reserved Designations used by companies to distinguish their products are often claimed as trademarks or registered trademarks In all instances in which Academic Press is aware of a claim, the product names appear in initial capital or all capital letters Readers, however, should contact the appropriate companies for more complete information regarding trademarks and registration No part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, photocopying, scanning, or otherwise, without prior written permission of the publisher Permissions may be sought directly from Elsevier’s Science & Technology Rights Department in Oxford, UK: phone: (+44) 1865 843830, fax: (+44) 1865 853333, e-mail: permissions@ elsevier.com You may also complete your request on-line via the Elsevier homepage (http:// elsevier.com), by selecting “Support & Contact” then “Copyright and Permission” and then “Obtaining Permissions.” Library of Congress Cataloging-in-Publication Data Application submitted ISBN 13: 978-0-12-374535-4 For information on all Academic Press publications, visit our Website at www.books.elsevier.com Printed in the United States 09  10  11  12  13  10  9  8  7  6  5  4  3  2  W orking together to grow libraries in developing countries www.elsevier.com | www.bookaid.org | www.sabre.org Preface Dr Joseph Mitola III Stevens Institute of Technology Castle 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 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 Adapted from J Mitola III, Cognitive Radio Architecture: The Engineering Foundations of Radio XML, Wiley, 2006 xiv   Preface 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 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 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 J Mitola III, Cognitive Radio Architecture, 2006 M Katz and S Fitzek, Cooperation in Wireless Networks, Elsevier, 2007 Preface   xv 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) 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 Semantic 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 xvi   Preface 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 to years The CR must both know a lot about radio and learn a lot about you, the 〈Owner/〉, recording and analyzing personal information, and the related aggregation of personal information places a premium on trustworthy privacy technologies Therefore, the CRA incorporates 〈Owner/〉 speaker recognition as one of multiple soft biometrics in a biometric cryptology framework to protect the 〈Owner/〉’s personal information with greater assurance and convenience than password protection More Flexible Secondary Use of the Radio Spectrum In 2008, the US Federal Communications Commission (FCC) issued its second Report and Order (R&O) that radio spectrum allocated to TV, but unused in a particular broadcast market (e.g., because of the transition from analog to digital TV) could be used by CRs as secondary users under Part 15 rules for low-power devices—for example, to create ad hoc networks SDR Forum member companies have demonstrated CR products with these elementary spectrum-perception and use capabilities Wireless products, both military and commercial, already implement the FCC vignettes Integrated visual- and speech-perception capabilities needed to evolve the DSA CR to the situation-aware iCR are not many years distant Productization is underway Thus, many chapters of Bruce’s outstanding book emphasize CR spectrum agility, suggesting pathways toward enhanced perception technologies, with new long-term growth paths for the wireless industry Those who have contributed to this book hope that it will help you understand and create new opportunities for CR technologies Acknowledgments This Second Edition of Cognitive RadioTechnology has been a collaborative effort of many leading researchers in the field of cognitive radio with whom I have had the pleasure of interacting over the last 10 years through participation in the Software Defined Radio Forum, and in some cases, a few of whom I have worked with over nearly my entire career To each of these contributors, I owe great thanks, as well as to all the other participants in the SDR Forum who have contributed their energy to advance the state of the art In addition to the authors, each contributor or contributor’s team in turn, has also been supported by their staffs and we appreciate their contributions as well I owe much to my family, Elizabeth, Alexandra, and Nicholas, who suffered my long distractions with their patience, love, understanding, and substantial help in editing and reviewing I also owe many thanks to my editor, Sandy Rush, who has patiently guided me through this difficult but very creative process I dedicate this book to my mother, who provided the perfect mixture of guidance and responsibility; to my grandfather; to my father; and Aunt Margaret, whose early guidance into the many aspects of science led me to this career I also acknowledge the support from General Dynamics C4 Systems for the support to work in this exciting new field Bruce A Fette Chapter This chapter is dedicated to the regulatory community that struggles tirelessly to balance technical rigor with good policy making Pail Kolodzy Chapters and The chapters are dedicated to Mona and Ashley Thank you both for your love and friendship, and thank you for the time I needed to work on this chapter John Polson xviii   Acknowledgments Chapter The authors of this chapter wish to thank all of the researchers, colleagues, and friends who have contributed to our work Specifically, we are pleased to recognize the members of the Virginia Tech research group, including Ph.D students Bin Le, David Maldonado, and Adam Ferguson; master’s students David Scaperoth and Akilah Hugine; and faculty members Allen MacKenzie and Michael Hsiao Finally, a very big thank you goes to three former colleagues who helped start this research: Christian Rieser, Tim Gallagher, and Walling Cyre Thomas W Rondeau, Charles W Bostian Chapter Ronald Brachman, Barbara Yoon, and J Christopher Ramming helped to refine my understanding of cognition and cognitive networking Joseph Mitola III and Preston Marshall greatly enhanced my knowledge of radio systems, and Mitola interested me in the intersection of radios and robotics Harry Lee and Marc Olivieri helped me to understand fine-scale variations in RF reception Larry Jackel and Thomas Wagner helped me to understand the challenges of decentralized control of robots In addition, the author is indebted to Joseph Mitola III, Daniel Koditschek, and Bruce Fette for their kindness in reviewing and critiquing draft versions of this chapter Jonathan M Smith Chapter 10 The work for this chapter was sponsored by the Department of Defense under Air Force contract FA8721-05-C-0002 Opinions, interpretations, conclusions, and recommendations are those of the authors and are not necessarily endorsed by the US government The authors are grateful to Joe Mitola for creating the DARPA seedling effort that supported this work Joseph P Campbell, William M Campbell, Scott M Lewandowski, Alan V McCree, Clifford J Weinstein Chapter 11 Youping Zhao was supported through funding from Cisco, Electronics and Telecommunications Research Institute (ETRI), and Texas Instruments, and advised by Jeffrey H Reed Bin Le was supported by the National Science Foundation (NSF) under Grant No CNS-0519959 and advised by Charles W Bostian Special thanks to Bruce Fette, Jody Neel, David Maldonado, Joseph Gaeddert, Lizdabel Morales, Kyung K Bae, Shiwen Mao, and David Raymond for their helpful discussions and comments Any opinions, findings, and conclusions or recommendations expressed in this chapter are those of the author(s) and not necessarily reflect the views of the sponsor(s) Youping Zhao, Bin Le, Jeffrey H Reed Acknowledgments   xix Chapter 13 The work presented in this chapter was partially supported by NSF Grant No 0225442 Mieczyslaw M Kokar, David Brady, Kenneth Baclawski Chapter 14 This chapter is dedicated to Lynné, Barb, Max, and Madeline Sophia Although the views expressed are exclusively my own, I would like to express appreciation to The MITRE Corporation’s commitment to technical excellence in the public interest through which one can step back and study the evolution of cognitive radio architecture from a variety of perspectives—US DoD, military, emergency services, aviation, commercial, and global Joseph Mitola III Chapter 16 Work done for this chapter was supported by HY-SDR Research Center at Hanyang University, Seoul, Korea, under the ITRC program of Ministry of Knowledge Economy, and by the Korea Science and Engineering Foundation (KOSEF) grant funded by the Korean government to INHA-WiTLAB as a National Research Laboratory Jae Moung Kim, Seungwon Choi, Yusuk Yun, Sung Hwan Sohn, Ning Han, Gyeonghua Hong, Chiyoung Ahn Chapter 17 Research for this chapter was supported by DARPA’s neXt Generation Communications Program under Contract Nos FA8750-05-C-0230 and FA8750-05-C-0150 SRI’s XG project web page can be found at http://xg.csl.sri.com Grit Denker, Daniel Elenius, David Wilkins Chapter 19 The work for this chapter was partially supported by DARPA through Air Force Research Laboratory (AFRL) Contract FA8750-07-C-0169 The views and conclusions contained in it are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the Defense Advanced Research Projects Agency or the US government Luiz A Dasilva, Ryan W Thomas Chapter 21 The preparation of this chapter was supported by Grant N00014-04-1-0563 from the US Office of Naval Research Thomas Royster was also supported by a fellowship from the US National Science Foundation The authors thank Steven Boyd for many beneficial suggestions during the preparation of the chapter Michael B Pursley, Thomas C Royster IV xx   Acknowledgments Chapter 23 The authors would like to thank the following participants in IEEE activities with whom the direct interactions have been most valuable Special recognition and acknowledgment for reviewing and commenting: James M Baker, BAE Systems, Apuvra Mody, BAE Systems, AS&T, IEEE 802.22 voting member For their contributions, we acknowledgments Douglas Sicker and James Hoffmeyer Recognition is due also to Matt Sherman, Christian Rodriquez, Jacob Wood, David Putnam, Paul Kolodzy, and Vic Hsiao for topical discussions Ralph Martinez, Donya He 814   Index Internet Engineering Task Force cont’d Policy Framework Working Group, 204 script MIB infrastructure, 200 sponsored standardization efforts, 199 standards for interoperability, 203 Internet Protocol (IP), 13, 259, 289 Internet Protocol networks, 199 Internet search engines and AI, Inter-network, 290–291 Interruptible spectrum access, 38–39, 62 Java, 95–96 editions, 95 garbage collection, 95 problems with, 96 reflection in CR, 14–15 remote method invocation, 204 runtime engine, 200 theorem prover (JTP), 210 virtual machine (JVM), 95 Joint Tactical Radio System (JTRS) initiative to develop and procure SDR systems for military, 28 military opportunities for cognitive policy management and, 218 procurement program and the military, 137 software communications architecture and, 82, 211, 471 waveforms maintained in library of, 122 Kalman filter GPS receiver, 115 Knapsack example, 236–240 chromosome initialization, 237 crossover, 238 evaluation and replacement, 239 mutation, 238, 239–240 parents, choosing, 237–238 results, 239 Knobs, 226–232, 235, 244, 245 Knowledge databases language evolution, Knowledge representation and learning, 367–399, 780–781 architectural layers within CR diagram, 396 Bayesian logic, 385–386 behavior, predictable, 394 behavioral, 376–377 brittleness and edge conditions, 394 CBR and, 377–378 classifiers, 384 computational requirements, 393–394 decision trees, 386 genetic algorithms, 392–393 implementation considerations, 393–394 layers of increasing cognitive capabilities list, 397 machine learning, 382–393 multiobjective GA and, 243 neural networks, 390–392 ontology and frame systems, 375–376 reasoning and, 371–382 reinforced-based learning, 387–389 rule-based systems, 378–381 summary of representation, 381, 382 symbolic, 373–375 temporal difference, 389–390 user authentication, 310–312 Knowledge-based reasoning, 121 Knowledge-intensive applications, 411–412 Knowledge-intensive characteristics, 401–406 command execution, 405 constraints and requirements, 402, 403 dynamic interoperability at any stack layer, 405–406 information collection and fusion, 403–404 query by user, self, or radio, 404–405 query responsiveness and command execution, 405 resource negotiation, 405 self-awareness, 404 situation awareness and advice, 404 Knowledge Query Markup Language (KQML), 730 Language awareness, 333, 334 Language identification (LID), 314–315 Layered ontology data link layer ontology, 413–418, 422–423 physical layer ontology, 412–413 Learning algorithm mission, 370 Leray–Schauder–Tychonoff fixed point theorem, 492, 508 LifeCycle, 85, 86 Lightweight directory access protocol (LDAP), 203, 215 Linear programming, 79–80 Link availability and IEEE 802.22, 758 Lipschitz continuous function, 492 List processing (LISP), Index   815 LoadableDevice, 87, 88 Local area network (LAN), 294, 743 Location awareness, 332, 334 Location modeling, 660–661 Location-based spectrum rights (LBSRs), 783 benefit of, 680 components of, ten, 653–655 conveying machine-readable policy and, 683 future research and work, 685–686 intent of, 655 managing spectrum with, 666 maximum power density and, 655 method, 653–669 as modeling approach to capture consumption of spectrum, 645–646, 652 optimized data structures for, 669–676 protection margin of, 658 replicate any policy ability, 662 SM approaches and, 682 spectrum reuse and, 683 spectrum usage decision and, 661–662 Log-distance pathloss model, 657, 658 Logical reasoning ontology, 425–426 Lombard effect, 317 LOng RAnge Navigation (LORAN), 266, 278, 779 Low battery power, 367 Low noise amplifier (LNA) external power amplifier and, 68 FEC and, 72 front-end energy distribution and, 155, 156, 158 front-end linearity management, 161 high levels of performance, 166 high selectivity filters and, 171 IMD3 energy as function of, 164 input, typical, 153, 154 intermodulaton, 162 linearity reduction, 174 time domain output, 162 Lyapunov function for contraction mapping, 496 game theory and, 502 potential games, 511, 512, 516 supermodular games and, 523, 525 Lyapunov stability, 491, 494, 495, 516, 525 Lyapunov’s direct method for discrete time systems, 495–496 McGurk effect, 317 Machine learning (ML), 382–393, 748 architecture, 370 aspect, important, 382–383 Bayesian logic, 385–386 classifiers, 384 decision trees, 386 GAs and, 392–393 memorization, 383–384 neural networks, 390–392 reinforcement-based, 387–389 simulation and gaming, 393 temporal difference, 389–390 Machine readable policy-controlled radio, 113 Machine translation (MT), 316–317 Management information base (MIB), 757 Markov models, 499–502 absorbing Markov chains, 500–502 analysis insights, 499–500 chain, 499, 510, 704, 705, 712, 717, 718 ergodicity theorem, 500 finite state, 704 game theory and, 527 hidden, 321 performance results for systems, 706 policy engine design and, 213n.2 time-varying interference and, 717 time-varying propagation loss and, 713, 715 transition time for, 714 Maximum likelihood estimation (MLE), 76 Medium access control (MAC), 70–71 for ad hoc networks, 637 adjustments to improve performance, 224 ALOHA protocol and, 300 geolocation-enabled routing and, 272 IEEE 802.11 and, 292 layer, 737–738 network protocols and, 121 for preempting an outage, 406 programming interface and, 94 protocol importance, 136 protocol purpose, 758–759 radio architecture and, 470 SCA application programming higher-layer problems and, 694 times for, 300 Memorization, 383–384 Memory management unit (MMU), 73 816   Index Meters, 226–232 tabulation by layer example table, 227 wireless system GA and, 257 Military opportunities for CP management, 217–218 Million instructions per second (MIPS), 474 Minimum mean square error (MMSE), 76, 539 Mission, context, and background awareness, 333, 334 Mobile Ad hoc NETworking (MANET), 121, 189, 637 Mobile transmitter fixed receiver system, 49–50 mobile receiver system, 50 Mobility and trajectory awareness, 333, 334 Model-based reflex agent, 119, 120 Modem, 9, 70 Modulation, 690–691 chips, 699 coding and, 758 Moore’s law, 2, 467, 469, 599 Multi-agent systems (MASs), 734, 735 Multicore systems and system-on-chip, 78–79 Multidimensional analysis of chromosomes, 246–248 Multimode radio, Multi-objective decision making (MODM), 233 constraint modeling, 233 genetic algorithm approach, 235–240 action, 242 CBDT and, 243–244 cognition loop, 240 feedback, 243 learning, 244 modeling, 241–242 multidimensional analysis of chromosomes, 246–248 objective function definition, 247 radio parameters as genes in chromosome, 244–246 pareto front, 233–234 problem, 234–235, 258, 261 search space, 258 theory and application to CR, 232–240 Multiple-antenna (MA) systems, 535–556 advantage, 535 beamforming system, 536–537, 539, 544, 552, 553, 782 CE and, 543, 550–553 cognitive capability, 541–543 correlation measurement, antenna, 548–549 criteria, critical, 539–541 direction of arrival estimation, 547–548 eigenvalue-based detection methods using, 549–550 environment observation, radio, 545–550 environmental parameters, 550, 551 NG wireless communications, application to, 553–555 objective selection, 552–553, 554 operational procedure diagram, 544 primary user in current channel, 544–545 smart antenna, 543; see also Smart antenna spatial multiplexing, 538–539 spectral sensing methods, 546–547 structure, 542–545 systems, 536–539 techniques, 536–541 transmission control parameters, 551–552 transmit diversity system, 537–538 Multiple input, multiple output (MIMO) antenna, 68 multipath-intense environment and, 335 space–time adaptive processing concept, 298 Multiple learning methods, 396 Multipoint relays (MPRs), 349, 350, 351–352 Nash equilibrium (NE), 488, 506–507 ε-NE, 516 existence, 507–508 finite improvement path and, 509, 510 identification, 507, 508, 510, 511 lattice, 522 potential games and, 515, 516, 517 supermodule games and, 521, 522, 525 using, 529 National Institute of Standards and Technology (NIST), 266 National Science Foundation (NSF) Computer Science and Telecommunications Board study, 22 pursuing increased spectrum access, 37 research projects, 38, 124 spectrum measurements sponsored by, 148 testbed programs, 778 National Telecommunications and Information Administration (NTIA), 46, 58–59 communication policies before CR, 44 Index   817 spectrum policy management and, 17, 197, 198 on test band for CR, 134 working on CR, 124, 134 Natural language (NL) and CRA inference hierarchy, 456–457 Network applications and requirements, 289–291 formal model, 485, 487–488 Network awareness, three types of, 289–304 applications and requirements, 289–291, 292 cognitive control, 294–295 distributed learning of environment, 300 distributing work within team, 300 dynamic protocol composition, 292–294 layering and information hiding, 290–291 node capabilities and cooperation, 297–298 node location and cognition for selfplacement, 300–302 protocols, 291, 292–295 radio team, 298–300 situation-aware protocols in edge network technologies, 295–297 Network control and management system (NCMS), 754, 755, 757 Network Knowledge Representation Language (NKRL), 731, 732 Network localization, 125, 270–272 functions, miscellaneous, 272 geolocation-enabled routing, 125, 272 spatially variant service, 270–272 Network Policy Management Architecture radio integration, 215–216 Network support: radio environment map, 325–366 awareness, 330–333, 334 city map versus REM, 328 external, 326–327 infrastructure-based and centralized global REM, 352–355 illustration of, 327 internal, 326–327 motivations, 325–326 obtaining situation awareness with, 333, 335–337 role in cognition cycle, 329–330 scenarios and application, 352–355 systematic top-down approach to obtaining cognition, 330–338 Network-based approaches, 281 Network-based system, 43, 44 Network-level decision-making implications, 188–191 DSA-enabled dynamic bandwidth topology, 189 DSA-enabled dynamic topology and network organization, 190–191 Networking definitions, 29, 62 Networking protocols, 121–122 Neural networks, 120, 390–392 Next Generation Internet (NGI), 200 Niching, genetic algorithm, 252 Noise and interference tolerance, 608–609, 610 Noise characterization, 319–320 Nondeterministic behaviors, 39–40 Normal form game model, 504–506 North American digital cellular (NADC) systems, 620, 622 Object Management Group (OMG), 431 Object-oriented programming (OOP), 79, 80, 375 Obligation policies definition, 201 Observable parameters See Meters Observe-orient-decide-act (OODA) loop, 729–736 act, 735–736 decide, 733–735 observe, 730–731 orient, 731–733 research holes, 736 Observe–orient links radio skill sets, 459 scene interpretation, 457–459 Ontology, 406, 426, 781 adaptation of training sequence length example, 420–422 basics, 407 communications device flowchart, 376 data link layer, 413–418, 422–423 delays and errors response example, 418–420 development and consensus, 423 examples, 418–423 frame systems and, 375–376 interoperability, 411 knowledge-intensive characteristics See Knowledge-intensive characteristics 818   Index Ontology cont’d languages, web-based, 407–409 layers, 412–418 learning, 425 mapping, 423–424 open research issues, 423–426 physical layer, 412–413 policy management, 411 querying, 409–410, 411 reasoning, 120, 410–411 reasoning efficiency, 425–426 role, 401–428 role in knowledge-intensive applications, 411–412 runtime modifiability, 411 self-awareness, 404, 406, 411 validation, 411 Ontology-based radio (OBR), 401, 409, 410, 419, 425 Open systems interconnection (OSI), stack, 93, 225 Opportunistic spectrum access (OSA), 33, 36–39 chronology, 37–38 DARPA and, 37, 38 dynamic frequency selection, 39, 62 spectrum utilization, 36–37 using policies, 558–559 Optimal link-state routing protocol (OLSR), 349, 350 Ordinal potential game, 511, 512, 514 Orthogonal frequency division multiplexing (OFDM) aware radios and, 112 congested spectral environment and, heavily, 224 IEEE 802.11a/g, 629–630, 631 IEEE 802.22 use of, 757, 758 matched filtering and, 600 multicarrier waveform structure and, 135 near-universal applicability and, 611 noise and interference tolerance and, 609 subcarriers, 358 waveform, 128, 129 OWL-DL, OWL Full, OWL Lite, OWL-QL See Web Ontology Language (OWL) Packet error rate (PER), 166, 247 Pareto front, 233–234, 248 Pareto optimality, 490, 508 Past experience awareness, 333, 334 Perfect next-state information (PNSI) protocol, 712, 713 Perfect previous-state information (PPSI) protocol, 712 Performance analysis, 483–533 access nodes, 483, 484 analysis objectives, 488–491 analysis problem, 485–491 convergence conditions, 490 desirability of expected behavior, 490 dynamic systems approach See Dynamic systems approach engineering analysis techniques, traditional, 491–502; see also Engineering analysis techniques formal model of CRN, 485, 487–488 game models, relevant, 511–529 game theory application, 502–511 network stability, 490–491 questions about, 484–485 Performance-enhancing proxies (PEPs), 203, 204, 206, 211, 292 Performance measures, 226–227; see also Meters Personal digital assistant (PDA) behavior expected in future, 781 cognitive, 464, 465 GPS and, 788 information collection and fusion, 403–404 intelligent, 401 SDR and, 476 self-awareness and, 404 situation awareness and, 404 wireless, 472, 475 Phase shift keying (PSK), 245 Physical (PHY) layer ontology, 406, 412–413 Physical layers, 135–136 Physical and link layers, 223–264 cognitive radio definition, 225–226 constraint modeling, 233 GA techniques, advanced, 252–256 higher-layer intelligence need, 256–258 intelligent computers operation, 258–260 knobs and meters development, 226–232 modeling outcome as primary objective, 230–232 modulator and demodulator, 470 MODM theory application, 232–240 multiobjective GA, 240–251 Index   819 optimizing for multiple-objectives, 224–225 parameters, 227–230 REM and, 787 Picard–Lindelöf theorem, 492, 496 Pick quietest band first (PQBF) algorithm, 168–169, 170, 172 Plan phase, 451 Policy awareness, 333, 334 Policy-based radio definitions, 30, 748 Policy challenges, 39–47 communications policy, 44–45 DSA and, 40–42 nondeterministic behavior, 39–40, 62 security, 42–44 US Telecommunication Policy, 45–47 Policy conformance reasoner (PCR), 146–147 Policy Core Information Model (PCIM), 203, 204, 205 Policy decision point (PDP) domain managers as, 200 function, 197, 211, 215 Java and, 210 nonlinear priorities and, 202 policy enforcement point versus, 206 policy management system concept and, 196 standardization efforts for policy management, 203, 204 Policy Definition Language (PDL), 202 Policy-enabled devies, 52–53 Policy-Enabled Mobile Applications (POEMA), 213 Policy enforcement point (PEP), 196, 197, 211, 215 Policy engine (PE), 16, 118–119, 195, 196, 557; see also Cognitive policy engine architecture for radio, 205–210 components, 779 constraints, 16 design, 213–215 design considerations, 564–567 development approaches, 208 functional design diagram, 214 integration, 210–216 interface to, 286 management functions, 211 Network Policy Management Architecture, 215–216 operations concept, 205–207 SRI, 573–582 SRI demonstration, 582–588 stateless, 566 technical approaches, 207–209 Policy language (PL) and policy engine, 557–592 advantages of using Maude, 577–578 benefits of policy-based approach, 559–561 design considerations, 563–567 disallowing policies, 589–590 encoding policies in Maude, 578–580 future work, 590 implementation in Maude, 575–582 lessons learned, 588–590 NG spectrum policy architecture, 561–563 notation, note on, 570 ontologies, 568–570 operations, 588–589 opportunistic spectrum access using policies, 558–559 reasoning with constraints, 574–575 SRI policy engine, 573–582 SRI policy engine demonstration, 582–588 SRI spectrum of PL, 567–573 Policy management system academic research, 200–202 architecture, 197, 215–216 commercial applications, 202–203, 218 concept diagram, 196 DARPA projects, 199–200 enabling technologies, 209–210 future for technology, 216–219 future work, 590 military opportunities, 217–218 obstacles to adoption, 218–219 promise of, 195 software design diagram, 212 standardization efforts, 203–205 technical approaches, 207–209 Policy service, 211 Ponder framework, 200–201, 202 Port, 87 PortSupplier, 85, 86 Position awareness, 265–288 approaches, 272–281 boundary decisions, 281–285 cellular telephone 911 geolocation for first responders, 285–286 geolocation approaches, 272–280 GPS and, 266–269 820   Index Position awareness cont’d interfaces, 286–287 network localization, 270–272 network-based approaches, 281 radio geolocation and time services, 266–270 transformation, coordinate system, 269 POSIX, 13, 83 Potential function, 511 Potential games, 511–520 bilateral symmetric interaction, 517, 518, 519–520 convergence, 515–516 designing networks, 516–520 desirability, 515 exact, 511, 512–514 examples, 511–512 fixed point and steady states for, 515 identification, 512–514 networks, designing, 516–520 ordinal, 511, 512, 514 stability, 516 Power adjustment, initial See Initial power adjustment Power supply and energy efficiency awareness, 333, 334 Prayer epochs, 448, 465 Primitive sequences, CRA inference hierarchy, 455 Priority awareness, 333, 334 Problems, really hard, 777–789 cellular infrastructure support to cognition, 785–786 cognitive services offered through infrastructure, 787–788 data radios, 786–787 discussion and summary of CR technologies, 777–784 protocols and etiquettes, 789 regulatory, 789 services offered to wireless networks through infrastructure, 784–789 stand-alone radios with cognition, 785 Profile descriptor, 91 Propagation models, 657–658, 677–678 Properties descriptor (PRF), 91, 93 PropertySet, 85, 86 Protocol architecture, 290 Protocol composition, dynamic, 292–294 Protocol stack, 290, 292 Protocols and etiquettes, 24, 62, 789 Protocols for adaptation, 689–721 adaptive transmission, 710–712 dynamic channels, protocol throughput performance for, 712–718 error-control codes, 691–692 initial power adjustment, 699–709 modulation, 690–691 performance measures for code-modulation library, 692–696 receiver statistics, 698–699 subsets of code-modulation library, special, 696–698 time-varying interference, 715, 716–718 time-varying propagation loss, 713–715, 716, 718 Proxim Tsunami radios, 249 Pseudo-concave function, 493–494 Pseudo-contraction, 497, 498 Public switched telephone network (PSTN), 430 Python, 96–97 Quadrature amplitude modulation (QAM) in mix of cooperative techniques, 224 M-ary phase shift keying and, 407 multiobjective GA and, 245 orthogonal frequency division multiplexing and, 611, 629 protocols in adaptation and, 690, 691, 692, 694 Quasiconcave function, 508 Querying, 409–410 Radio adaptive, 112–113, 114 architecture, 470–471 defense radio, 784, 785 evolution toward CRA, 477–478 flirting and AI technology, 134 frequency hopping, 28, 112 functions–transforms model of, 474–475 machine readable policy-controlled, 113 telematics, 785 Radio access networks (RANs) distributed radio resource usage optimization, 770 dynamic spectrum access and, 743, 769 IEEE 802.22 and, 753, 773 IEEE P1900 and, 751 Index   821 IEEE P1900.4 and, 763, 764, 765, 766, 768, 771, 773 IEEE P1900.5 and, 773 Radio access technology (RAT), 332, 341 Radio Act (1927), 45 Radio controls See Knobs Radio environment map (REM), 272, 325–366, 780; see also Network support: radio environment map ad hoc spectrum-sharing networks, 358, 360–362 APIs and, 348 applications to CWNs, example, 355–362 architecture, 337–338 classifications, 338–339 database design guidelines, 339–341 database implementation options, 339, 340, 341, 343, 344 design, 338–352 digitizing and indexing information elements table, 337 dissemination schemes and overhead analysis, 349–352 GPS and, 788 implementation techniques, 341, 342, 343, 344, 345, 346 indexing and retrieving of information, 348 infrastructure-based network and centralized global, 352–355 integrating various databases for building up, diagram for, 344 internal and external network support, 326–327 learning, reasoning, and decision mechanisms, 345–346 memory management, 346–347 obtaining cognition with, 330–338 PL and, 787 role in cognition cycle, 329–330 situation awareness with, obtaining, 333, 335–337 supporting elements for exploiting, 344–348 Radio Extensible Markup Language (RXML) CRA I, 431, 432, 434, 440 CRA II, 451 CRA III, 460 CRA IV, 463–464 CRA V, 467, 475 Radio flexibility and capability, 105–111 continuum of, 106–107 examples of software-capable radios, 107, 108–109 SDR examples, 107, 111 software-programmable radios examples, 107, 109–110 Radio frequency (RF) environment and waveform awareness, 332, 334 externals, 8, 10, 67, 68 front end (RFFE), 8, 28, 69, 111 integrated circuits (RFIC), 146, 342 power amplifier (PA), 68 situation awareness (RFSA), 602 Radio geolocation and time services and GPS, 266–270 accuracy-obtained and coordinate system, 267–269 control segment, 267 navigation message, 268 satellite signals, 267–268 signal processing, 268–269 space segment, 266–267 user segment, 267 Radio knowledge in architecture, 440–442 Radio Knowledge Representation Language (RKRL), 145, 730, 731, 781 Radio Communication Act (1989), 34 Radiometer, 615 RAKE filter, 9, 112, 335 Random better response dynamic, 523 Random sampling, 522–523 Rational, 507 Real World Reasoning (REAL), 124 Reasoning, 410–411 case-based, 377–378 efficiency of, 425–426 reactive, 321 rule-based, 378–381 temporal, 381 Received signal strength (RSS) approach, 272, 280, 439 Receiver statistics, 698–699 Reduced instruction set computer (RISC), 73, 470 Reflection runtime structure, 410 Reflex agent with state, 120 Regulation awareness, 333, 334 Rei Language, 202, 210 822   Index Reinforcement-based learning, 387–389, 390 Relative pooling tournament evaluation, 248–249 Relative tournament evaluation, 248 ReleaseObject(), 86, 90 ReleaseResource(), 86 Rendezvous in CRNs, 635–644 blind, 636, 637, 638–642 classification of solutions flowchart, 636 link maintenance and effect of primary users, 643 random, 639–640 sequence-based, 640–642 unaided, 636 use of control channels, 637–638 Rendezvous problem, 131–132 infrastructure-aided, 131–132 MAC and, 136 unaided, 132, 636 Repeated game model, 506 Research and funding, 123–133 Research ReServation Protocol (RSVP), 206, 259 Resistor-capacitor-inductance (RCL) circuit, 158 Resource Description Framework (RDF), 52, 407, 408, 409, 410 Resource Description Framework Schema Language (RDFS), 408 ResourceFactory, 86–87, 90 Roaming, 35, 50 Rote learning, 383–384 Round-trip time (RTT), 291 Round-trip timing and distance measuring equipment, 273, 274 Rule-based systems, 356, 378–381 architecture diagram, basic, 379 reasoning and, 410–411 reasoning example diagram, 380 RunTest(), 86 SDR See Software-defined radio Search for transmission opportunities, 559 Secondary spectrum markets, 684–685 Security sublayer (SSL) definition, 756–757 Self-awareness, 404, 406, 411 Self-coexistence and inter-BS coordination, 760 Self-monitoring timing, 452–453 Self-referential components, 444–446 inconsistency, 444–445 watchdog timer, 446 Semantic Web (SeW) Language, 52, 137, 200, 207, 481, 567 Sensing and environmental awareness, 258–259 Sensing definitions, 29, 62 Sensor(s) biometric, 116, 117, 138, 312 infrastructure update, 135 spectrum analyzer resemblance, 115 Service area points (SAPs), 754 Shannon bound, 186 Shannon limit analysis, 184 Shibboleths, 314 Signal-to-interference and noise ratio (SINR) maximizing power control example, 509 meters and, 228–229 physical and link layers and, 224, 230, 231, 232 radio simulation tools prediction of, 342 standard interference function model and, 497, 498, 499 supermodular games and, 524, 525 unpredicted noise injection and, 72 Signal-to-noise ratio (SNR) Java Reflection in CR and, 14 matched filtering and, 600 meters and, 228 network awareness and, 297, 298 spectral footprint management objectives and, 188 Simple Network Management Protocol (SNMP), 5, 200 Simple reflex agent, 119, 120 Simulation and gaming, 393 Situation-Aware Protocols in Edge Network Technologies (SAPIENT), 295–297, 780 Situation awareness (SA), 117–118 as knowledge-intense characteristic, 404 REM and, 326, 333, 335–337 Sleep epoch, 448, 453, 464, 465 Slice radio See Velcro radio Smalltalk, Smart agent model, 119–120, 137 Smart antenna beamforming or null forming, 224 in CR, 15–16, 137–138, 543 usage, 782 Index   823 Smooth supermodular game conditions, 520–526 Software assembly descriptor (SAD), 91 Software architecture, 79–82 aspect-oriented programming, 81 component-based programming, 80–81 design patterns, 81–82 design philosophies, 79–81 linear programming, 79–80 object-oriented programming, 80 Software capable radio examples, 107, 108–109 properties, 114 Software certification security, 43 Software Communication Architecture (SCA), 82–94, 122 API and, 13, 84, 93–94, 98, 471, 735 application software, 94–97 base components, 84, 85–87 boot-up sequence, 89, 91, 92 COBRA middleware, 13, 83, 84, 471 component development, 84, 97–98 core framework, 13, 84 CRA V and, 471–474 files, 91 framework, 84 framework control, 84, 87–89 integration, 211–212 parts of, 84 pattern use, 82 POSIX, 471 profiles, 84 waveform development, 84, 98–99 XML and, 13 Software component descriptor (SCD), 91, 93 Software-defined radio (SDR), 3–6, 30 advantage, 28 applications, 2, 10 basic, 6–13 chips, 10 cognitive waveform development, 99–102 component development, 97–98 computational processing resources in, 10–12 design philosophy and, 81 design space diagram, 469 development and design, 10–11, 82–94 digital module radio as, 4, examples, 107, 111 Forum, 5, 6, 13, 29, 68, 124, 134, 135, 211, 431, 470, 472 GNURadio development and, 82, 97, 107 hardware architecture See Hardware architecture, software-defined radio hardware versus software, 65 platform definition file, 245 as platform for CR, 65–103 properties, 114 smart antennas, 10 software architecture, 12–13 Software Communications Architecture, 13, 82–94 sophistication level, SPEAKeasy I and II, 3, 4, timeline diagram, transit signal-processing block diagram, traditional, voice usage, 305 waveforms and protocols, Web browsing restriction on, 72 Software package descriptor (SPD), 91, 93 Software programmable radio, 107, 109–110, 114 Software reconfigurable radio definition, 30 Software technology, 118–122; see also Digital signal processing AI techniques, 3, 119–121 communications architecture, 28, 82, 122 network protocols, 121–122 policy engine, 16, 118–119 signal processing, 121 Software-adaptable network (SAN), 729, 730, 735, 737–738 Spatial awareness, 117–118, 124, 125 Spatial dynamics, 51 SPEAKeasy I, 3, 4, SPEAKeasy II, 3, 4, Speaker recognition, 306–314 applications, 313–314 biometric processing security architecture, 312–313 biometric sensor, 312 enrollment phase, 306, 308–310 user authentication, 310–312 verification phase, 306, 308–310 Speaker stress characterization, 319 Spectral correlation See Spectrum sensing based on spectral correlation Spectral efficiency, 757 824   Index Spectral power histograms, 155, 156 Spectrum access allocation in United States and New Zealand, 31–32 assignments, 32–34 context diagram, 130 current techniques, 31–36 dynamic, 40–42 dynamic frequency selection, 39, 40 dynamic objectives, 176–186 opportunistic See Opportunistic spectrum access opportunities in, new, 30–39 prior work in, 144–146 regimes diagram, 33 regulatory constraints, 126 unlicensed devices, 31, 33, 34 usage density, 177 Spectrum awareness access considerations and, 143–194 CR role, 144, 145 frequency occupancy, 115–116 funding and research for DSA and, 126–131 interference avoidance problem, 116 prior work in, 144–146 subleasing or borrow potential, 122–123 utilization diagram, 127 Spectrum conservation as national priority, Spectrum consumption models, 645–687 applications, 682–682 compliance and computing compatible reuse, 664–669 components of location-based spectrum rights, 653–655 constructing rights, 676–682 directional vectors used for power and propagation maps, 658–660 future research and work, 685–686 location-based method to specify RF spectrum rights, 653–669 location components, 660 modeling signal space and, 655–657 optimized data structures for location-based spectrum rights, 669–676 policy limitations, 650–651 protocol and policy, 661–662 reconciling dynamic access and spectrum management, 646–653 time models, 662 vector examples, concise, 673–676 Spectrum efficiency, 221 Spectrum environment characterization summary, 147–149 Spectrum information channels, 637 Spectrum management (SM), 16–21, 27–28, 61–63, 645 aggregating spectrum demand, 20 alternative to dynamic spectrum access and, 652–653 command and control management model, 647 commercial opportunities, 17, 21, 218, 685 dynamic, 683–684 goal of, persistent, 646–648 IEEE standardization role in development, 744 information base, 200 interference management, 17, 27 license holders, 17 manufacturing differences, 18 mobility factor determination, 179–180 noise aggregation, 18, 20 priority access, 20–21 reconciling with DSA, 646–653 roles of, primary, 27 simplified, 529 spectrum access SEE Spectrum access subleasing methods and, 20 unlicensed, 17–18, 19 Spectrum manager (SM) definition, 756 Spectrum masks, 656 broadcaster and, 680, 682 examples diagram, 670 minimum power density and, 661 modeling spectral consumption and signal space and, 655 modeling transmitter and receiver rights and, 663, 664 more concise making, 669 spectral envelope and, 676, 677 underlay margin computations and, 666 Spectrum opportunity, 149, 151, 152 Spectrum outage probability (SOP), 184, 185 Spectrum policy management, 197–198 system requirements, 198–199 variances in, 118 Spectrum Policy Task Force (SPTF) establishment of, 22, 53, 59 goal of spectrum management and, persistent, 646–647 Index   825 management of spectrum policy and, 198 opportunistic spectrum and, 37 on spectrum policy, 59 spectrum policy recommendations, 60 on subleasing spectrum, 122 Spectrum regulator, 14 Spectrum sensing based on spectral correlation, 593–634 application to modern communication signals, 616–630, 631 approach to sensing algorithm development, 616–617 archetypal example, 602–604, 605 considerations, 594, 595 constrained spectrum sensing, 598–599 cycle detection, 601–602 cyclic autocorrelation function, 606 delay-and-multiply detection, 601, 616 energy detection, 600–601, 615–616 examples, 613–616 general spectrum sensing, 596–597 hidden-node problem, 595–596 matched filtering, 600–601 role of signal classification, 599 solutions overview, 599–602 stationary and nonstationary signals, 605–606 statistical estimators, efficient, 611–613 statistical nature of communication signals, 604, 605–613 statistical properties, key, 608–611 Spectrum-sensing function (SSF) definitions, 754, 755 Spectrum Sharing Innovation Testbed, 61 Spectrum subleasing, 119 Spectrum utilization benefits, 183–184 examples, 36–37 Speech and language processing, 306–320 applications, 313–314 background noise suppression, 317–318 enrollment and verification, 306, 308–310 language identification, 314–315 machine translation, 316–317 noise characterization, 319–320 speaker recognition, 306, 308–314 speaker stress characterization, 319 speech coding, 318–319 speech-to-text conversion, 316 technologies diagram, 307 text-to-speech conversion, 315–316 user identification, 310–312 Speech coders, 318–319 Stand-alone radios with cognition, 785 Standard inference function model, 497–499, 523, 524, 525 State–space models and searching, 120 Strip spectral-correlation analyzer (SSCA), 611, 612–613 Supermodular games, 520–526 adaptive dynamic process, 522 analysis, 524–525 convergence, 522 desirability, 522 examples, 521, 523–524 fixed points in, 521–522 increasing differences, 520 Nash equilibrium and, 521, 522, 525 properties, 521 random sampling, 522–523 smooth, 520 stability, 523 stage game model, 524 validation, 525–526, 527, 528 Supervised learning, 382–383 Swiss army knife (SAK) solutions, 602 Symbolic knowledge representation, 373–375 System control and DomainManager, 90–93 System strategy reasoner (SSR), 146, 147, 562 Tarski’s fixed-point theorem, 521 Technologies required, 105–141 available technologies, 115–123 aware, adaptive, and CR, 111–114 comparison of radio capabilities and properties, 114–115 funding and research, 123–133 hardware and demonstrations update, 137–138 policy update, 137 radio flexibility and capacity, 105–111 reasoners update, 136–137 timeline for CRs, 133–135 update, 135–138 Technology enablers, 27, 28–30 Telecommunication Act (1996), 34 Telecommunication policy See also US telecommunication policy basic geometries, 48–50 dynamic policies introduction, 50–52 826   Index Telecommunication policy cont’d interference avoidance, 53–54 overarching impact, 54 policy-enabled devices introduction, 52–53 technology impact on regulation, 48–54 Telephony services, 786 Telematics, 785 Television white space, 144, 145, 680 Temporal difference, 389–390 Temporal knowledge, 381, 382, 384 TestableObject, 85, 86 Text-to-speech (TTS) conversion, 315–316 Time of arrival (ToA) approach, 115, 268, 273, 274, 286 Time-based approaches, 273–279 estimation, 278, 279 LORAN, 278 RTT distance measuring equipment, 273, 274 TDoA approach See Time difference of arrival approach ToA approach See Time of arrival approach TV broadcast, 278 Time difference of arrival (TDoA) approach, 115, 274–278, 286 common coordinate system, transforming to, 277–278 curve, 274–276 information, obtaining, 279 source transmitter position, 278 Time division multiple access (TDMA) adaptive radios and, 112 family of signals, cellular, 620, 621–622 MAC and, 135, 136 multiple cellular telephone interfaces and, 285 networking protocols, 121 predictability in protocols and, 662 radio transition toward cognition question and, 476 as time slotted structure, Time dynamic in spectrum policy, 51 Time of day, 116, 117 Timeline, 133–135 decisions, directions, and standards, 134 new products manufacturing, 134–135 roots of SDR, Toffler Associates, 59 Training sequence length adaptation example, 420–422 Transmission Control Protocol (TCP), 259, 290, 291, 292 Transmission security (TRANSEC), 28 Transparent interface, 320 Trunked radio, 20 Turing test, 119 Ultra-wideband (UWB) radio, 177 Underlay masks, 655, 656–657 broadcaster and, 682 constructing rights and, 677 encoding for transmission, 669–671 as receiver component, 663 receiver rights and, 664 Unified Modeling Language (UML), 412–413, 763 Software Defined Radio Forum and, 431, 471–473 Unilateral deviation, 506 Unintential radiator, 35 Unlicensed devices, 31, 33, 34, 35 Unmanned aerial vehicle (UAV), 678, 683 Unsupervised learning, 383 US Department of Commerce (DoC), 46, 61, 266; see also National Telecommuni­ cations and Information Administration US Department of Defense (DoD), 37, 58, 82, 83, 122 GPS and, 266 Modular Multifunctional Information Transfer Systems Forum and, 432 potential for new products and systems, 773–774 SCA and, 471, 735 spectrum utilization interest by, 744 US government role in CR, 21–22 US National Institute of Standards and Technology (NIST), 10 US Radio Act (1934), 33 US telecommunications policy, 45–47 FCC and, 45–46 NTIA and, 46 US State Department and, 46 technology, pace with, 46–47 User authentication, 310–312 Utility-based agent, 119, 120 Velcro radio, 468 Vertical calibrations, 726 Index   827 Very high frequency omnidirectional ranging (VHF VOR), 266, 280 Very high-speed integrated circuit (VHSIC) VHDL, 12, 76, 121 Video coder, 10 Virtual data integration, 424 Vision of cognitive radio, Virginia Tech–CWT cognitive engine, 241 VoCoder (voice coder) baseband processor engines and, 73 capture signal and, 133 modem and, 70 SDR hardware architecture and, 10 as software products, 780 SPEAKeasy I and, voice telephony today and, 71 Voice biometrics, 308, 310, 311 Voice communication, 71, 318 Voice over Internet Protocol (VoIP), 318, 752, 786, 787 Voltage standing wave radio (VSWR), 10, 67, 68 Wake epoch, 448, 464 Waypoint definition, 754 Weak improvement cycle Web Ontology Language (OWL) binary relationships, 408 data link layer ontology and, 414, 416, 418 declarative knowledge and, 375 KAoS and, 200, 411 as knowledge base, 410 as major ontology language, 407 Maude versus, 578 OWL-DL, 408, 409, 425 OWL Full, 408, 409, 425 OWL Lite, 408, 409, 425 OWL-QL, 409 policy-enabled devices and, 52–53 query language for, 409 Semantic Web Rule Language and, 410 SRI Spectrum Policy Language and, 567 Weight values and objective functions, 260 Weighted potential game, 511 White space, 144, 145, 153, 198 White Spaces Coalition, 739 Wideband code division multiple access (WCDMA), 69 WiFi alliance, 528 WiFi Protocol data link layer ontology and, 414, 415, 416, 422 hierarchy, 415 multiple spectrum policies and, 50–51 WiMAX waveforms properties, 18, 19, 744 Wireless communication challenges, 557 Wireless Innovation Alliance, 738–739 Wireless local area network (WLAN), 752 See also IEEE, 802.11 activity map, 354 example application of global REM and, 353–354 IEEE 802.11h and, 39 ISM bands and, 647 network localization and, 125 radio geolocation and time services, 266 REM and, 355 unlicensed devices and, 36 use case evolution, 429 Wireless Network after Next (WNaN), 21, 28, 124, 146, 190, 738 Wireless network and service offered through infrastructure, 784–789 Wireless regional area network (WRAN), 135; see also IEEE, 802.22 applying REM to, 355–358, 359–360 REM and, 144, 339, 340, 341, 342, 348, 352, 353 REM memory footprint and, 347 Wireless system generic algorithm (WSGA) chromosome multidimensional analysis and, 247 chromosome sketch, 245 cognitive system module and, 241 example, 249–251 feedback and, 243 knowledge base, 243 new radio configuration and, 242 rewards and punishments, 257–258 Wireless Telegraphy Act (1998), 34 Wireless World Research Forum (WWRF), 355 World Geoditic System (WGS 84), 661 World Radio Conference, 645 World Radiocommunication Conferences (WRCs), 31, 46, 56, 197 Writable parameters See Knobs WxWidgets, 97 828   Index XML cognitive loop and, 246 DARPA Agent Markup Language and, 200 data exchange and, 407 DomainManager and, 91 objective function definition, 247 SCA and, 13, 89 XML Topic Maps (XTM), 407 Yttrium–iron–garnet (YIG) circulators, 69 ZigBee (IEEE 802.15.4), 744 devices, 355 waveforms properties, 19 ... Definitions for policy-based radios and dynamic frequency selection radios are also provided These two new radio classes are specific implementations of CRs Policy-based radios are discussed in Section... authorized channel based on relative channel availability Policy-Based Radio: A radio that is governed by a predetermined set of rules for behavior The rules define the operating limits of such a radio. .. Cataloging-in-Publication Data Application submitted ISBN 13: 97 8-0 -1 2-3 7453 5-4 For information on all Academic Press publications, visit our Website at www.books.elsevier.com Printed in the United