Cognitive radio engineering /

A cognitive radio is a transceiver which is aware of its environment, its own technical capabilities and limitations, and those of the radios with which it may communicate; is capable of acting on that awareness and past experience to configure itself in a way that optimizes its performance; and is...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autores principales: Bostian, Charles W. (Autor), Fayez, Almohanad S. (Autor), Kaminski, Nicholas J. (Autor)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Edison, NJ : SciTech Publishing, an imprint of the IET, 2016.
Colección:Mario Boella series on electromagnetism in information & communication.
Temas:
Cognitive radio networks.
Radio cognitive.
TECHNOLOGY & ENGINEERING > Mechanical.
cognitive radio.
Acceso en línea:Texto completo
Tabla de Contenidos:
  • Machine generated contents note:
  • 1. Introduction
  • 1.1. What Is a Cognitive Radio and Why Is It Needed
  • 1.2. Book Coverage and Philosophy
  • 1.3. Origin of Cognitive Radio
  • 1.4. Overview of Cognitive Radio Operation
  • 1.5. Illustrative Example of Cognitive Radio Application: Dynamic Spectrum Access in the Broadcast Television Bands
  • 1.5.1. Introduction
  • 1.5.2. Identifying Frequencies for Cognitive Radio Operation: TV Channel Occupancy in the United States
  • 1.5.3. FCC Rules and Commercial Standards for Unlicensed Television Band Devices (TVBDs)
  • 1.5.4. FCC Rule Implementation by the IEEE 802.11af Standard
  • 1.5.5. TV White Space Databases
  • 1.5.6. Standard ECMA-392
  • 1.5.7. Cognitive Radio Design for U.S. TV White Space
  • 1.6. Can a Radio Really Be Cognitive
  • 2. Cognitive Engine Design
  • 2.1. Introduction
  • 2.2. Basic Function of a Cognitive Engine
  • 2.3. Cognitive Engine Organization
  • 2.3.1. Optimizer
  • 2.3.2. Objective Analyzer
  • 2.3.3. Ranker
  • 2.3.4. Knowledge Base
  • 2.3.5. Radio Interface
  • 2.3.6. Sensor
  • 2.3.7. User Interface
  • 2.3.8. Controller
  • 2.4. Tools and Techniques for CE Component Design
  • 2.4.1. Machine Learning
  • 2.4.2. Optimizers
  • 2.4.3. Estimation
  • 2.4.4. Sensing
  • 2.5. Cognitive Engine Architecture
  • 2.5.1. Broad Considerations
  • 2.5.2. Monolithic Versus Distributed
  • 2.5.3. Standards
  • 2.5.4. Original CE Architecture
  • 2.5.5. CSERE Architecture
  • 2.6. Information Flow in Cognitive Engines
  • 2.6.1. Example Use of Uncertainty Coefficient
  • 2.7. Conclusion
  • 3. RF Platforms for Cognitive Radio
  • 3.1. Introduction
  • 3.2. Preliminary Considerations in Choosing an RF Platform
  • 3.3. RF Architectures
  • 3.3.1. Receivers
  • 3.3.2. Transmitter
  • 3.4. Receiver RF Specifications
  • 3.4.1. Introduction
  • 3.4.2. Noise, Noise Performance, and Weak Signal Behavior
  • 3.4.3. Strong Signal Behavior
  • 3.5. Transmitter RF Specifications
  • 3.6. MAC and Performance Considerations
  • 3.7. Radio Frequency Integrated Circuits
  • 3.7.1. Introduction
  • 3.7.2. Example: RFM69CW
  • 3.7.3. Computational Support for RFICs
  • 3.8. Platforms for Software Defined Radio
  • 3.8.1. Introduction
  • 3.8.2. Packaged RF Front Ends and All-in-one Platforms
  • 3.9. Conclusion
  • 4. Cognitive Radio Computation and Computational Platforms
  • 4.1. Role of Computing and Cognitive Radio Architecture
  • 4.2. Control Flow and Data Flow Computer Architectures
  • 4.2.1. Control Flow Computing
  • 4.2.2. Data Flow Computing
  • 4.3. Overview of Computational Devices (GPP, DSP, FPGA)
  • 4.3.1. Digital Signal Processors
  • 4.3.2. General Purpose Processors
  • 4.3.3. Field-programmable Gate Arrays
  • 4.3.4. Alternative Computational Devices
  • 4.3.5. Computational Heterogeneity
  • 4.4. Models of Computation
  • 4.4.1. Reactive and Real-time Systems
  • 4.4.2. Data Flow Models of Computation
  • 4.4.3. Process Algebra
  • 4.4.4. Calculus of Communicating Systems and (SS (B-calculus
  • 4.5. Models-of-Computation Use
  • 4.6. Conclusion
  • 5. Integrating and Programming RF and Computational Platforms for Cognitive Radio
  • 5.1. SDR Platforms
  • 5.2. Choosing a Platform
  • 5.2.1. Choosing Between RF Alternatives
  • 5.2.2. Processor Choices
  • 5.2.3. Benchmarks
  • 5.2.4. Processor Interconnect
  • 5.2.5. Other Considerations
  • 5.3. Programming
  • 5.3.1. Classic Approach
  • 5.3.2. Model-Based Design
  • 5.3.3. Application of Models-of-Computation
  • 5.4. Concluding Remarks
  • 6. Cognitive Radio Evaluation
  • 6.1. Introduction
  • 6.2. Performance Evaluation Principles
  • 6.3. Metrics and Factors for Cognitive Radio Evaluation
  • 6.3.1. Purpose
  • 6.3.2. Language
  • 6.3.3. Actions
  • 6.4. Practical Evaluation Methods
  • 6.4.1. Setup
  • 6.4.2. Logging
  • 6.4.3. Encoding
  • 6.4.4. Interpolation
  • 6.4.5. Alternative Approaches to Evaluation
  • 6.5. Example Evaluation
  • 6.5.1. Setup Phase
  • 6.5.2. Logging Phase
  • 6.5.3. Encoding Phase
  • 6.5.4. Interpolation
  • 6.6. Example Code
  • 6.6.1. Free FEC Cognitive Radio
  • 6.6.2. Fixed FEC Cognitive Radio
  • 6.6.3. Interpolation Code
  • 6.7. Conclusion
  • 7. Cognitive Radio Design for Networking
  • 7.1. Networks of Cognitive Radios Versus Cognitive Networks
  • 7.2. Cognitive Network Goals
  • 7.3. Interaction Methods for Cognitive Radios
  • 7.3.1. Social Language
  • 7.4. Components of Interaction
  • 7.4.1. Observability
  • 7.4.2. Understanding
  • 7.5. Analyzing Interactions
  • 7.5.1. Example Analysis
  • 7.5.2. Analysis Results
  • 7.6. Group Learning
  • 7.7. Building a Cognitive Network with Social Language
  • 7.7.1. MAC Layer Considerations
  • 7.7.2. Behavior-based Design and Social Language
  • 7.7.3. Tasks and Behaviors
  • 7.7.4. Hardware Considerations and Implementation
  • 7.7.5. Implementing Behaviors in Software and Hardware
  • 7.7.6. Network Evaluation
  • 7.7.7. Entry Scenario
  • 7.7.8. Social Learning
  • 7.7.9. Total System Behavior
  • 8. Cognitive Radio Applications
  • 8.1. Introduction
  • 8.2. Zoned Dynamic Spectrum Access
  • 8.3. Cognitive WiFi and LTE Operation in TV White Space Spectrum
  • 8.3.1. WiFi Frequency Translators
  • 8.3.2. LTE Frequency Converters
  • 8.4. LTE Cognitive Repeaters for Indoor Applications
  • 8.5. Cognitive Radio and Cognitive Radar: Communications and Radar System Coexistence
  • 8.5.1. Cognitive Radar
  • 8.5.2. Legacy Radar and Communications System Coexistence
  • 8.6. Ka Band Geostationary Satellite Applications
  • 8.6.1. Introduction
  • 8.7. Public Safety and Emergency First Responder Communication
  • 8.7.1. Introduction
  • 8.7.2. Virginia Tech Public Safety Cognitive Radio
  • 8.7.3. Current (2016) Situation
  • 8.8. Cognitive Radio and Autonomous Vehicles
  • 8.9. Smart Grids.

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