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Radio Engineering: From Software Radio to Cognitive Radio

Name: Radio Engineering: From Software Radio to Cognitive Radio
Author: Jacques Palicot
ISBN: 9781848212961

by Jacques Palicot

September 2011

Intermediate to advanced

378 pages

10h 17m

English

Wiley

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Cover
Title Page
Copyright
Foreword
Acknowledgments
Introduction
Part 1. Cognitive Radio
Chapter 1. Introduction to Cognitive Radio
1.1. Joseph Mitola's cognitive radio1.1.1. Definitions1.1.2. Joseph Mitola's vision of cognitive cycle1.2. Positioning1.2.1. Convergence between networks1.2.2. Generalized mobility without service interruption1.2.3. Distribution of intelligence1.3. Spectrum management1.3.1. Current situation1.3.2. Spectrum sharing1.3.2.1. Horizontal and vertical sharing1.3.2.2. Spectrum pooling1.3.2.3. Spectrum underlay technique1.3.2.4. Spectrum overlay technique1.4. A broader vision of CR1.4.1. Taking into account the global environment1.4.2. The sensorial radio bubble for CR1.5. Difficulties of the cognitive cycle
Chapter 2. Cognitive Terminals Toward Cognitive Networks
2.1. Introduction2.2. Intelligent terminal2.2.1. Description2.2.2. Advantages2.2.3. Limitations2.3. Intelligent networks2.3.1. Description2.3.2. Advantages2.3.3. Limitations2.4. Toward a compromise2.4.1. Impact of the number of users2.4.2. Impact of spectral dimension2.5. Conclusion
Chapter 3. Cognitive Radio Sensors
3.1. Lower layer sensors3.1.1. Hole detection sensor3.1.1.1. Matched filtering3.1.1.2. Detection3.1.1.3. Energy detection3.1.1.4. Collaborative detection3.1.2. Other sensors3.1.2.1. Recognition of channel bandwidth3.1.2.2. Single- and multicarrier detection3.1.2.3. Detection of spread spectrum type3.1.2.4. Other sensors of the lower layer3.2. Intermediate layer sensors3.2.1. Introduction3.2.2. Cognitive pilot channel3.2.3. Localization-based identification3.2.3.1. Geographical location-based systems synthesis3.2.3.2. Rights of database use and update3.2.4. Blind standard recognition sensor3.2.4.1. General description3.2.4.2. Sage 1: band adaptation3.2.4.3. Stage 2: analysis with lower layer sensors3.2.4.4. Stage 3: fusion3.2.5. Comparison of abovementioned three sensors for standard recognition3.3. Higher layer sensors3.3.1. Introduction3.3.2. Potential sensors3.3.3. Video sensor and compression3.3.3.1. Active appearance models3.3.3.2. A real scenario3.3.3.3. Different stages3.4. Conclusion

Chapter 4. Decision Making and Learning
4.1. Introduction4.2. CR equipment: decision and/or learning4.2.1. Cognitive agent4.2.2. Conflicting objectives4.2.3. A modeling part in all approaches4.2.4. Decision making and learning: network equipment4.3. Decision design space4.3.1. Decision constraints4.3.1.1. Environmental constraints4.3.1.2. User constraints4.3.1.3. Equipment capacity constraints4.3.2. Cognitive radio design space4.4. Decision making and learning from the equipment's perspective4.4.1. A priori uncertainty measurements4.4.2. Bayesian techniques4.4.3. Reinforcement techniques: general case4.4.3.1. Bellman's equation4.4.3.2. Bellman's equation to reinforcement techniques4.4.3.3. Value update4.4.3.4. Iteration algorithm for policies4.4.3.5. Q-learning4.4.4. Reinforcement techniques: slot machine problem4.4.4.1. An introductory example: analogy with a slot machine4.4.4.2. Mathematical formalism and fundamental results4.4.4.3. Upper confidence bound (UCB) algorithms4.4.4.4. UCB1 algorithm4.4.4.5. UCBV algorithm4.4.4.6. Application example: opportunistic spectrum access4.4.5. Artificial intelligence4.5. Decision making and learning from network perspective: game theory4.5.1. Active or passive decision4.5.2. Techniques based on game theory4.5.2.1. Cournot's competition and best response4.5.2.2. Fictitious play4.5.2.3. Reinforcement strategy4.5.2.4. Boltzmann–Gibbs and coupled learning4.5.2.5. Imitation4.5.2.6. Learning in stochastic games4.6. Brief state of the art: classification of methods for dynamic configuration adaptation4.6.1. The expert approach4.6.2. Exploration-based decision making: genetic algorithms4.6.3. Learning approaches: joint exploration and exploitation4.7. Conclusion
Chapter 5. Cognitive Cycle Management
5.1. Introduction5.2. Cognitive radio equipment5.2.1. Composition of cognitive radio equipment5.2.2. A design proposal for CR equipment: HDCRAM5.2.3. HDCRAM and cognitive cycle5.2.4. HDCRAM levels5.2.4.1. Level L35.2.4.2. Level L25.2.4.3. Level L15.2.5. Deployment on a hardware platform5.2.6. Examples of intelligent decisions5.3. High-level design approach5.3.1. Unified modeling language (UML) design approach5.3.2. Metamodeling5.3.3. An executable metamodel5.3.4. Simulator of cognitive radio architecture5.4. HDCRAM's interfaces (APIs)5.4.1. Organization of classes of HDCRAM's metamodel5.4.1.1. Parent classes5.4.1.2. Child classes5.4.2. ReM APIs5.4.3. CRM's APIs5.4.4. Operators' APIs5.4.5. Example of deployment scenario in CR equipment5.5. Conclusion
Part 2. Software Radio As Support Technology
Chapter 6. Introduction to Software Radio
6.1. Introduction6.2. Generalities6.2.1. Definitions6.2.1.1. Ideal software radio6.2.1.2. Software-defined radio6.2.1.3. Other interesting classifications6.2.2. Interests and aftermath for telecom players6.2.2.1. Designer of terminals and access points6.2.2.2. Operator and service provider6.2.2.3. End user6.3. Major organizations of software radio6.3.1. Forums6.3.1.1. SDR Forum/Wireless Innovation Forum6.3.1.2. OMG6.3.2. Standardization organizations6.3.3. Regulators6.3.4. Some commercial and academic projects6.3.5. Military projects6.4. Hardware architectures6.4.1. Software-defined radio (ideal)6.4.2. Software-defined radio6.4.2.1. Direct conversion6.4.2.2. SR with low IF6.4.2.3. Undersampling6.4.2.4. Other architectures6.4.2.4.1. Architecture with two parallel paths6.4.2.4.2. Multichannel architecture6.5. Conclusion
Chapter 7. Transmitter/Receiver Analog Front End
7.1. Introduction7.2. Antennas7.2.1. Introduction7.2.2. For base stations7.2.2.1. Constraints on spatial discrimination7.2.2.2. Constraints on the spectral discrimination7.2.2.3. Sample topologies and concepts7.2.3. For mobile terminals7.2.3.1. Constraints7.2.3.2. Sample topologies and concepts7.3. Nonlinear amplification7.3.1. Introduction7.3.2. Characteristics of a power amplifier7.3.2.1. AM/AM and AM/PM characteristics7.3.2.2. The efficiency7.3.2.3. Input and output back-offs7.3.2.4. Memory effect7.3.3. Merit criteria of a power amplifier7.3.3.1. Intermodulation7.3.3.2. The C/I ratio7.3.3.3. Interception point7.3.3.4. 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 amplifier7.3.4.1. Input–output relationship of an amplifier7.3.4.2. The polynomial model7.3.4.3. The Saleh model7.3.4.4. The Rapp model7.3.5. Modeling of a power amplifier with memory7.3.5.1. The Saleh model7.3.5.2. The Volterra model7.3.5.3. The Wiener–Hammerstein model7.3.5.4. The polynomial model with memory7.4. Converters7.4.1. Introduction7.4.1.1. Requirements for the software radio7.4.2. Characteristics of the converters7.4.2.1. Quantization noise7.4.2.2. Thermal noise7.4.2.3. Sampling phase noise7.4.2.4. Measuring spectral purity: the spurious free dynamic range (SFDR)7.4.2.5. SFDR improvement by adding noise: the dither7.4.2.6. Switched capacitor converters: the KT/C noise7.4.2.7. Signal dynamics7.4.2.8. Blockers7.4.2.9. Linearity constraints7.4.2.10. Jammers7.4.2.11. Bandwidth and slew rate7.4.2.12. Consumption constraints: the figure of merit (FOM)7.4.2.13. Constraints on digital ports7.4.3. Digital to analog conversion architectures7.4.3.1. Current source of DAC architectures7.4.3.2. Switched capacitor DAC architecture7.4.3.3. Evolution of the DAC7.4.4. Analog to digital conversion architecture7.4.4.1. Flash structure7.4.4.2. Folding ADC7.4.4.3. Pipeline structure7.4.4.4. Successive approximation architecture7.4.4.5. Sigma-delta architecture7.4.4.6. Evolution of the ADC analog-to-digital converters7.4.5. Summarizing the converters7.5. Conclusion
Chapter 8. Transmitter/Receiver Digital Front End
8.1. Theoretical principles8.1.1. The universal transmitter/receiver8.2. DFE functions8.2.1. IQ transposition in digital domain8.2.2. Sample rate conversion8.2.2.1. Frequency conversion by decimation filter8.2.2.1.1. CIC decimation filter8.2.2.1.2. Farrow structure8.2.3. Channelization8.2.4. DFE from a practical point of view8.2.4.1. Low-pass filtering8.2.4.2. Cheapest solution in terms of computational cost8.2.5. Multichannel DFE8.3. Synchronization8.3.1. Introduction8.3.2. Symbol timing recovery8.3.2.1. Timing phase recovery8.3.2.2. Phase error detector8.3.2.3. Phase-locked loop (PLL)8.3.3. Carrier phase recovery8.3.3.1. DA estimation8.3.3.2. NDA estimation8.3.3.3. Phase recovery8.3.3.4. Direct structures: DA and NDA estimator8.3.3.5. Loop structures8.3.3.6. Phase error detector8.3.3.7. The loop filter8.3.4. Synchronization in the software radio context8.3.4.1. Analysis of the dynamic behavior of the loop with constellation change8.3.4.2. Reliability and detection of constellation8.4. The CORDIC algorithm8.4.1. Principle of the CORDIC algorithm8.4.2. Operation of the CORDIC operator8.4.2.1. Vector mode8.4.2.2. Rotation mode8.5. Conclusion
Chapter 9. Processing of Nonlinearities
9.1. Introduction9.2. Crest factor of the signals to be amplified9.2.1. Characteristic parameters of the crest factor9.2.1.1. Crest factor definition9.2.1.2. Definition of continuous and finite PR: PRc,f9.2.1.3. Definition of discrete and finite PR: PRd,f9.2.2. Relationship with the literature notations9.2.2.1. PAPR case9.2.2.2. Relationship between PAPR and PMEPR9.2.3. Distribution function of PR9.3. Variation of crest factor in different contexts9.3.1. Single-carrier signals' context9.3.1.1. Influence of Nyquist filter9.3.1.2. Influence of square-root Nyquist filter9.3.2. Multicarrier signals' context9.3.3. Software radio signals' context9.3.4. Context of cognitive radio9.3.4.1. Introduction9.3.4.2. Variations in crest factor on spectrum access by using carrier-by-carrier vision9.3.4.3. Influence of spectrum access on PAPR in the CR context9.4. Methods for reducing nonlinearities9.4.1. Introduction9.4.2. PAPR reduction methods9.4.2.1. Methods with modifications of the receiver9.4.2.2. Methods without modifying the receiver9.4.2.3. Synthesis of the PAPR reduction methods9.4.3. Methods working on the linearity of the amplifier9.4.3.1. Methods to change the function of amplification9.4.3.2. Methods without changing the amplification function9.5. Conclusion
Chapter 10. Methodology and Tools
10.1. Introduction10.2. Methods to identify common operations10.2.1. Parametrization approaches10.2.2. Pragmatic approach of parametrization to design multistandard SR10.2.2.1. Common functions10.2.2.2. Common operators10.2.3. Theoretical approach to design multistandard SR by common operators10.3. Methods and design tools10.3.1. Co-design methods10.3.1.1. Simulink® based approach10.3.1.2. An example: SynDEx10.3.1.2.1. Design approach10.3.1.2.2. Application graph10.3.1.2.3. Platform graph10.3.1.2.4. Adequation10.3.1.2.5. Code generation10.3.1.2.6. SR example10.3.1.2.7. Conclusion10.3.1.3. GNU radio10.3.2. MDA approaches10.3.2.1. Introduction10.3.2.2. MOPCOM methodology10.3.2.2.1. Tooling and code generation10.3.2.2.2. Case study10.3.2.2.3. AML-level modeling10.3.2.2.4. DML-level modeling10.3.2.3. GASPARD model10.4. Conclusion
Chapter 11. Implementation Platforms
11.1. Introduction11.2. Software radio platform11.3. Hardware architectures11.3.1. Dedicated circuits11.3.2. Processors11.3.2.1. CISC architecture11.3.2.2. RISC architecture11.3.2.3. Superscalar architecture11.3.2.4. VLIW architecture11.3.2.5. Vector architecture11.3.3. Reconfigurable architecture11.3.3.1. Fine-grain architecture11.3.3.2. Coarse-grain architecture11.4. Characterization of the implementation platform11.4.1. Flexibility/reconfiguration11.4.2. Performances11.4.3. Power consumption11.5. Qualitative assessment11.6. Architectures of software layers11.6.1. The SCA software architecture11.6.2. Intermediate software layer: ALOE11.6.3. Software architecture for reconfiguration management: HDReM11.7. Some platform examples11.7.1. The USRP platform11.7.2. OpenAirInterface11.7.3. Kansas University Agile Radio11.7.4. Berkeley Cognitive Radio Platform11.8. Conclusion
Chapter 12. General Conclusion and Perspectives
12.1. General conclusion12.2. Perspectives12.2.1. CR and sustainable development12.2.1.1. A spontaneous communication protocol12.2.2. Toward a collective intelligence12.2.3. Toward a fully cognitive radio
Appendix A. To Learn More
A.1. The special issues of journalsA.1.1. SR domain in generalA.1.2. CR domainA.2. Specialized conferencesA.3. Some reference booksA.3.1. SR domain in generalA.3.1.1. In close connection with the contents of Chapter 7A.3.1.2. In close connection with the contents of Chapter 8A.3.2. CR domain in general
Appendix B. SR and CR Projects
B.1. European projectsB.2. French projects
Appendix C. International Activity in Standardization and Forums
C.1. The IEEE 802.22 StandardC.2. SCC41 standardization (Standards Coordinating Committee 41, dynamic spectrum access networks)C.3. P1900.1 standardization (Working group on terminology and concepts for next-generation radio systems and spectrum management)C.4. P1900.2 standardization (Working group on recommended practice for interference coexistence and analysis)C.5. P1900.3 standardization (Working group on recommended practice for conformance evaluation of Software Defined Radio software modules)C.6. P1900.4 standardization (Working group on architectural building blocks enabling network – device distributed decision making for optimized radio resource usage in heterogeneous wireless access networks)C.7. P1900.5 standardization (Working group on policy language and policy architectures for managing cognitive radio for dynamic spectrum access applications)C.8. P1900.6 standardization (Working group on spectrum sensing interfaces and data structures for dynamic spectrum access and other advanced radio communications systems)C.9. ITU-R standards (Question ITU-R 241-1/5: cognitive radio systems in mobile service)C.10. ETSI technical committee on reconfigurable radio systems (TC-RRS) standardizationC.11. Forum: Wireless Innovation Forum (former by SDR forum)C.12. Forum: Wireless World Research Forum
Appendix D. Research at European and International Levels
D.1. Research centers
Acronyms and Abbreviations
Bibliography
List of Authors
Index

Content preview from Radio Engineering: From Software Radio to Cognitive Radio

Chapter 9Processing of Nonlinearities¹

9.1. Introduction

The amplifier is a key element in a telecommunications system. In this chapter, we focus on the “high-power amplifier” (HPA) at the transmitter side. The efficiency of these amplifiers is very low (less than 50% in the best case for Class A amplifiers), and also they have nonlinear characteristics. Therefore, a part of terminal's power is consumed by power amplifier. Thus, the use of constant-envelope modulation schemes, such as Gaussian minimum shift keying (GMSK), for a global system for mobile communication (GSM) is justified where amplification efficiency is optimized by operating the amplifier near-saturation point. However, most of the standardized modulation schemes, whether single-carrier or multicarrier, are not constant-envelope schemes, and the consequences of nonlinear power amplification of radio frequency (RF) signal must be treated with greater concern.

The factor PR power ratio (PR) [PAL 05a], which is defined as the ratio between the instantaneous power and the average power of a signal, is used for measuring the envelope fluctuations of the signals to be amplified. This parameter appears in the literature under several acronyms such as peak-average power ratio (PAPR), peak-mean envelope power ratio (PMEPR), or crest factor (CF).

In this chapter, we will analyze the PR in various contexts (single-carrier, multicarrier, software radio (SR), and cognitive radio (CR)) and then present some methods for ...

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Radio Engineering: From Software Radio to Cognitive Radio

by Jacques Palicot

Chapter 9Processing of Nonlinearities¹

9.1. Introduction

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