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Fette B. Cognitive Radio Technology
Издательство Academic Press, 2009, -828 pp.

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 leaing.
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.
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 lea 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 lea about your radio use pattes 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 leaing (RL) and case-based reasoning (CBR) are mature machine leaing 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 leaing 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.

History and Background of Cognitive Radio Technology
Communications Policy and Spectrum Management
The Software-Defined Radio as a Platform for Cognitive Radio
Cognitive Radio: The Technologies Required
Spectrum Awareness and Access Considerations
Cognitive Policy Engines
Cognitive Techniques: Physical and Link Layers
Cognitive Techniques: Position Awareness
Cognitive Techniques: Three Types of Network Awareness
Cognitive Services for the User
Network Support: The Radio Environment Map
Cognitive Research: Knowledge Representation and Leaing
The Role of Ontologies in Cognitive Radios
Cognitive Radio Architecture
Cognitive Radio Performance Analysis
Cognitive Radio in Multiple-Antenna Systems
Cognitive Radio Policy Language and Policy Engine
Spectrum Sensing Based on Spectral Correlation
Rendezvous in Cognitive Radio Networks
Spectrum-Consumption Models
Protocols for Adaptation in Cognitive Radio
Cognitive Networking
The Role of IEEE Standardization in Next-Generation Radio and Dynamic Spectrum Access Developments
The Really Hard Problems
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