Cover -- Title Page -- Copyright Page -- Table of Contents -- Foreword -- Acknowledgments -- Introduction -- PART 1. COGNITIVE RADIO -- Chapter 1. Introduction to Cognitive Radio -- 1.1. Joseph Mitola's cognitive radio -- 1.1.1. Definitions -- 1.1.2. Joseph Mitola's vision of cognitive cycle -- 1.2. Positioning -- 1.2.1. Convergence between networks -- 1.2.2. Generalized mobility without service interruption -- 1.2.3. Distribution of intelligence -- 1.3. Spectrum management -- 1.3.1. Current situation -- 1.3.2. Spectrum sharing -- 1.3.2.1. Horizontal and vertical sharing -- 1.3.2.2. Spectrum pooling -- 1.3.2.3. Spectrum underlay technique -- 1.3.2.4. Spectrum overlay technique -- 1.4. A broader vision of CR -- 1.4.1. Taking into account the global environment -- 1.4.2. The sensorial radio bubble for CR -- 1.5. Difficulties of the cognitive cycle -- Chapter 2. Cognitive Terminals Toward Cognitive Networks -- 2.1. Introduction -- 2.2. Intelligent terminal -- 2.2.1. Description -- 2.2.2. Advantages -- 2.2.3. Limitations -- 2.3. Intelligent networks -- 2.3.1. Description -- 2.3.2. Advantages -- 2.3.3. Limitations -- 2.4. Toward a compromise -- 2.4.1. Impact of the number of users -- 2.4.2. Impact of spectral dimension -- 2.5. Conclusion -- Chapter 3. Cognitive Radio Sensors -- 3.1. Lower layer sensors -- 3.1.1. Hole detection sensor -- 3.1.1.1. Matched filtering -- 3.1.1.2. Detection -- 3.1.1.3. Energy detection -- 3.1.1.4. Collaborative detection -- 3.1.2. Other sensors -- 3.1.2.1. Recognition of channel bandwidth -- 3.1.2.2. Single- and multicarrier detection -- 3.1.2.3. Detection of spread spectrum type -- 3.1.2.4. Other sensors of the lower layer -- 3.2. Intermediate layer sensors -- 3.2.1. Introduction -- 3.2.2. Cognitive pilot channel -- 3.2.3. Localization-based identification -- 3.2.3.1. Geographical location-based systems synthesis.

3.2.3.2. Rights of database use and update -- 3.2.4. Blind standard recognition sensor -- 3.2.4.1. General description -- 3.2.4.2. Stage 1: band adaptation -- 3.2.4.3. Stage 2: analysis with lower layer sensors -- 3.2.4.4. Stage 3: fusion -- 3.2.5. Comparison of abovementioned three sensors for standard recognition -- 3.3. Higher layer sensors -- 3.3.1. Introduction -- 3.3.2. Potential sensors -- 3.3.3. Video sensor and compression -- 3.3.3.1. Active appearance models -- 3.3.3.2. A real scenario -- 3.3.3.3. Different stages -- 3.4. Conclusion -- Chapter 4. Decision Making and Learning -- 4.1. Introduction -- 4.2. CR equipment: decision and/or learning -- 4.2.1. Cognitive agent -- 4.2.2. Conflicting objectives -- 4.2.3. A modeling part in all approaches -- 4.2.4. Decision making and learning: network equipment -- 4.3. Decision design space -- 4.3.1. Decision constraints -- 4.3.1.1. Environmental constraints -- 4.3.1.2. User constraints -- 4.3.1.3. Equipment capacity constraints -- 4.3.2. Cognitive radio design space -- 4.4. Decision making and learning from the equipment's perspective -- 4.4.1. A priori uncertainty measurements -- 4.4.2. Bayesian techniques -- 4.4.3. Reinforcement techniques: general case -- 4.4.3.1. Bellman's equation -- 4.4.3.2. Bellman's equation to reinforcement techniques -- 4.4.3.3. Value update -- 4.4.3.4. Iteration algorithm for policies -- 4.4.3.5. Q-learning -- 4.4.4. Reinforcement techniques: slot machine problem -- 4.4.4.1. An introductory example: analogy with a slot machine -- 4.4.4.2. Mathematical formalism and fundamental results -- 4.4.4.3. Upper confidence bound (UCB) algorithms -- 4.4.4.4. UCB1 algorithm -- 4.4.4.5. UCBV algorithm -- 4.4.4.6. Application example: opportunistic spectrum access -- 4.4.5. Artificial intelligence -- 4.5. Decision making and learning from network perspective: game theory.

4.5.1. Active or passive decision -- 4.5.2. Techniques based on game theory -- 4.5.2.1. Cournot's competition and best response -- 4.5.2.2. Fictitious play -- 4.5.2.3. Reinforcement strategy -- 4.5.2.4. Boltzmann-Gibbs and coupled learning -- 4.5.2.5. Imitation -- 4.5.2.6. Learning in stochastic games -- 4.6. Brief state of the art: classification of methods for dynamic configuration adaptation -- 4.6.1. The expert approach -- 4.6.2. Exploration-based decision making: genetic algorithms -- 4.6.3. Learning approaches: joint exploration and exploitation -- 4.7. Conclusion -- Chapter 5. Cognitive Cycle Management -- 5.1. Introduction -- 5.2. Cognitive radio equipment -- 5.2.1. Composition of cognitive radio equipment -- 5.2.2. A design proposal for CR equipment: HDCRAM -- 5.2.3. HDCRAM and cognitive cycle -- 5.2.4. HDCRAM levels -- 5.2.4.1. Level L3 -- 5.2.4.2. Level L2 -- 5.2.4.3. Level L1 -- 5.2.5. Deployment on a hardware platform -- 5.2.6. Examples of intelligent decisions -- 5.3. High-level design approach -- 5.3.1. Unified modeling language (UML) design approach -- 5.3.2. Metamodeling -- 5.3.3. An executable metamodel -- 5.3.4. Simulator of cognitive radio architecture -- 5.4. HDCRAM's interfaces (APIs) -- 5.4.1. Organization of classes of HDCRAM's metamodel -- 5.4.1.1. Parent classes -- 5.4.1.2. Child classes -- 5.4.2. ReM APIs -- 5.4.3. CRM's APIs -- 5.4.4. Operators' APIs -- 5.4.5. Example of deployment scenario in CR equipment -- 5.5. Conclusion -- PART 2. SOFTWARE RADIO AS SUPPORT TECHNOLOGY -- Chapter 6. Introduction to Software Radio -- 6.1. Introduction -- 6.2. Generalities -- 6.2.1. Definitions -- 6.2.1.1. Ideal software radio -- 6.2.1.2. Software-defined radio -- 6.2.1.3. Other interesting classifications -- 6.2.2. Interests and aftermath for telecom players -- 6.2.2.1. Designer of terminals and access points.

6.2.2.2. Operator and service provider -- 6.2.2.3. End user -- 6.3. Major organizations of software radio -- 6.3.1. Forums -- 6.3.1.1. SDR Forum/Wireless Innovation Forum -- 6.3.1.2. OMG -- 6.3.2. Standardization organizations -- 6.3.3. Regulators -- 6.3.4. Some commercial and academic projects -- 6.3.5. Military projects -- 6.4. Hardware architectures -- 6.4.1. Software-defined radio (ideal) -- 6.4.2. Software-defined radio -- 6.4.2.1. Direct conversion -- 6.4.2.2. SR with low IF -- 6.4.2.3. Undersampling -- 6.4.2.4. Other architectures -- 6.5. Conclusion -- Chapter 7. Transmitter/Receiver Analog Front End -- 7.1. Introduction -- 7.2. Antennas -- 7.2.1. Introduction -- 7.2.2. For base stations -- 7.2.2.1. Constraints on spatial discrimination -- 7.2.2.2. Constraints on the spectral discrimination -- 7.2.2.3. Sample topologies and concepts -- 7.2.3. For mobile terminals -- 7.2.3.1. Constraints -- 7.2.3.2. Sample topologies and concepts -- 7.3. Nonlinear amplification -- 7.3.1. Introduction -- 7.3.2. Characteristics of a power amplifier -- 7.3.2.1. AM/AM and AM/PM characteristics -- 7.3.2.2. The efficiency -- 7.3.2.3. Input and output back-offs -- 7.3.2.4. Memory effect -- 7.3.3. Merit criteria of a power amplifier -- 7.3.3.1. Intermodulation -- 7.3.3.2. The C/I ratio -- 7.3.3.3. Interception point -- 7.3.3.4. Noise power ratio (NPR) -- 7.3.3.5. Adjacent channel power ratio (ACPR) -- 7.3.3.6. Error vector magnitude (EVM) -- 7.3.4. Modeling of a memoryless power amplifier -- 7.3.4.1. Input-output relationship of an amplifier -- 7.3.4.2. The polynomial model -- 7.3.4.3. The Saleh model -- 7.3.4.4. The Rapp model -- 7.3.5. Modeling of a power amplifier with memory -- 7.3.5.1. The Saleh model -- 7.3.5.2. The Volterra model -- 7.3.5.3. The Wiener-Hammerstein model -- 7.3.5.4. The polynomial model with memory -- 7.4. Converters -- 7.4.1. Introduction.

7.4.1.1. Requirements for the software radio -- 7.4.2. Characteristics of the converter -- 7.4.2.1. Quantization noise -- 7.4.2.2. Thermal noise -- 7.4.2.3. Sampling phase noise -- 7.4.2.4. Measuring spectral purity: the spurious free dynamic range (SFDR) -- 7.4.2.5. SFDR improvement by adding noise: the dither -- 7.4.2.6. Switched capacitor converters: the KT/C noise -- 7.4.2.7. Signal dynamics -- 7.4.2.8. Blockers -- 7.4.2.9. Linearity constraints -- 7.4.2.10. Jammers -- 7.4.2.11. Bandwidth and slew rate -- 7.4.2.12. Consumption constraints: the figure of merit (FOM) -- 7.4.2.13. Constraints on digital ports -- 7.4.3. Digital to analog conversion architectures -- 7.4.3.1. Current source of DAC architectures -- 7.4.3.2. Switched capacitor DAC architecture -- 7.4.3.3. Evolution of the DAC -- 7.4.4. Analog to digital conversion architecture -- 7.4.4.1. Flash structure -- 7.4.4.2. Folding ADC -- 7.4.4.3. Pipeline structure -- 7.4.4.4. Successive approximation architecture -- 7.4.4.5. Sigma-delta architecture -- 7.4.4.6. Evolution of the ADC analog-to-digital converters -- 7.4.5. Summarizing the converters -- 7.5. Conclusion -- Chapter 8. Transmitter/Receiver Digital Front End -- 8.1. Theoretical principles -- 8.1.1. The universal transmitter/receiver -- 8.2. DFE functions -- 8.2.1. I/Q transposition in digital domain -- 8.2.2. Sample rate conversion -- 8.2.2.1. Frequency conversion by decimation filter -- 8.2.3. Channelization -- 8.2.4. DFE from a practical point of view -- 8.2.4.1. Low-pass filtering -- 8.2.4.2. Cheapest solution in terms of computational cost -- 8.2.5. Multichannel DFE -- 8.3. Synchronization -- 8.3.1. Introduction -- 8.3.2. Symbol timing recovery -- 8.3.2.1. Timing phase recovery -- 8.3.2.2. Phase error detector -- 8.3.2.3. Phase-locked loop (PLL) -- 8.3.3. Carrier phase recovery -- 8.3.3.1. DA estimation -- 8.3.3.2. NDA estimation.

8.3.3.3. Phase recovery.