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Radio resource allocation and dynamic spectrum access [[electronic resource] /] / Badr Benmammar, Asma Amraoui
| Radio resource allocation and dynamic spectrum access [[electronic resource] /] / Badr Benmammar, Asma Amraoui |
| Autore | Benmammar Badr |
| Edizione | [1st edition] |
| Pubbl/distr/stampa | London, : ISTE |
| Descrizione fisica | 1 online resource (94 p.) |
| Disciplina | 621.38411 |
| Altri autori (Persone) | AmraouiAsma |
| Collana | Focus series in waves |
| Soggetto topico |
Cognitive radio networks
Radio resource management (Wireless communications) Radio frequency allocation - Management |
| ISBN |
1-118-57511-3
1-118-57435-4 1-118-57483-4 1-299-18662-9 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Title Page; Contents; ACRONYMS; INTRODUCTION; CHAPTER 1. WIRELESS AND MOBILE NETWORKS; 1.1. Introduction; 1.2. Wireless networks; 1.2.1. Definition; 1.2.2. Function of a wireless network; 1.2.2.1. Network with infrastructure; 1.2.2.2. Network without infrastructure The network without infrastructure, which is referred to as ad hoc network or independen; 1.2.3. Types of wireless networks; 1.2.3.1. Wireless personal area network The wireless personal area network (WPAN) is composed of connections between devices tha; 1.2.3.2. Wireless local area network
1.2.3.3. Wireless metropolitan area network1.2.3.4. Wireless wide area network; 1.2.3.5. Wireless regional area network; 1.2.4. Different types of existing wireless networks; 1.2.4.1. Networks using infrared waves Infrared waves are commonly used in everyday (in television remote controls, for example); 1.2.4.2. Networks using radio waves; 1.2.5. IEEE 802.22 standard; 1.3. Mobile networks; 1.3.1. Wireless and mobility; 1.3.2. Mobility; 1.3.3. Cellular architecture; 1.3.4. Architecture of a cellular network; 1.3.5. Telephony; 1.3.6. Development of cellular systems; 1.3.6.1. First generation 1.3.6.2. Second generation1.3.6.3. Third generation; 1.3.6.4. Fourth generation; 1.4. WiMAX mobile and 4G; 1.5. Conclusion; CHAPTER 2. COGNITIVE RADIO; 2.1. Introduction; 2.2. Software radio; 2.2.1. Software-defined radio (SDR); 2.3. Introduction to cognitive radio; 2.3.1. History; 2.3.2. Definition; 2.3.3. Relationship between cognitive radio and software-defined radio; 2.3.4. Structure; 2.3.5. Cognition cycle; 2.3.6. Components of cognitive radio; 2.3.7. Functions of cognitive radio; 2.4. Languages of cognitive radio; 2.5. Domains of cognitive radio applications; 2.6. Conclusion CHAPTER 3. MULTI-AGENT SYSTEMS3.1. Introduction; 3.2. Definition of an agent; 3.2.1. The multidimensional characteristics of an agent; 3.2.2. An agent's concrete architecture; 3.2.2.1. Architecture of logical agents; 3.2.2.2. Reactive architecture; 3.2.2.3. BDI architecture; 3.2.2.4. Multilevel architecture The objective of a multilevel architecture is to conduct a constructive synthesis of the reacti; 3.2.3. Model of an agent; 3.3. Multi-agent systems; 3.3.1. Communication between agents; 3.3.1.1. Coordination protocols; 3.3.1.2. Cooperation protocols; 3.3.1.3. Negotiation 3.4. Application of MAS in telecommunications3.4.1. MAS applications on the Web; 3.4.2. Application of MAS in virtual private networks; 3.4.3. Using MAS in the setting of third generation mobiles; 3.4.4. Application of MAS in network supervision and management; 3.5. Conclusion; CHAPTER 4. DYNAMIC SPECTRUM ACCESS; 4.1. Introduction; 4.2. Intelligent algorithms; 4.2.1. Neural networks; 4.2.2. Fuzzy logic; 4.2.3. Genetic algorithms; 4.3. Dynamic spectrum access; 4.3.1. Spectrum access using the auction approach; 4.3.2. Spectrum access using game theory 4.3.3. Spectrum access using Markov's approach |
| Record Nr. | UNINA-9910138859603321 |
Benmammar Badr
|
||
| London, : ISTE | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Radio resource allocation and dynamic spectrum access / / Badr Benmammar, Asma Amraoui
| Radio resource allocation and dynamic spectrum access / / Badr Benmammar, Asma Amraoui |
| Autore | Benmammar Badr |
| Edizione | [1st edition] |
| Pubbl/distr/stampa | London, : ISTE |
| Descrizione fisica | 1 online resource (94 p.) |
| Disciplina | 621.38411 |
| Altri autori (Persone) | AmraouiAsma |
| Collana | Focus series in waves |
| Soggetto topico |
Cognitive radio networks
Radio resource management (Wireless communications) Radio frequency allocation - Management |
| ISBN |
1-118-57511-3
1-118-57435-4 1-118-57483-4 1-299-18662-9 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Title Page; Contents; ACRONYMS; INTRODUCTION; CHAPTER 1. WIRELESS AND MOBILE NETWORKS; 1.1. Introduction; 1.2. Wireless networks; 1.2.1. Definition; 1.2.2. Function of a wireless network; 1.2.2.1. Network with infrastructure; 1.2.2.2. Network without infrastructure The network without infrastructure, which is referred to as ad hoc network or independen; 1.2.3. Types of wireless networks; 1.2.3.1. Wireless personal area network The wireless personal area network (WPAN) is composed of connections between devices tha; 1.2.3.2. Wireless local area network
1.2.3.3. Wireless metropolitan area network1.2.3.4. Wireless wide area network; 1.2.3.5. Wireless regional area network; 1.2.4. Different types of existing wireless networks; 1.2.4.1. Networks using infrared waves Infrared waves are commonly used in everyday (in television remote controls, for example); 1.2.4.2. Networks using radio waves; 1.2.5. IEEE 802.22 standard; 1.3. Mobile networks; 1.3.1. Wireless and mobility; 1.3.2. Mobility; 1.3.3. Cellular architecture; 1.3.4. Architecture of a cellular network; 1.3.5. Telephony; 1.3.6. Development of cellular systems; 1.3.6.1. First generation 1.3.6.2. Second generation1.3.6.3. Third generation; 1.3.6.4. Fourth generation; 1.4. WiMAX mobile and 4G; 1.5. Conclusion; CHAPTER 2. COGNITIVE RADIO; 2.1. Introduction; 2.2. Software radio; 2.2.1. Software-defined radio (SDR); 2.3. Introduction to cognitive radio; 2.3.1. History; 2.3.2. Definition; 2.3.3. Relationship between cognitive radio and software-defined radio; 2.3.4. Structure; 2.3.5. Cognition cycle; 2.3.6. Components of cognitive radio; 2.3.7. Functions of cognitive radio; 2.4. Languages of cognitive radio; 2.5. Domains of cognitive radio applications; 2.6. Conclusion CHAPTER 3. MULTI-AGENT SYSTEMS3.1. Introduction; 3.2. Definition of an agent; 3.2.1. The multidimensional characteristics of an agent; 3.2.2. An agent's concrete architecture; 3.2.2.1. Architecture of logical agents; 3.2.2.2. Reactive architecture; 3.2.2.3. BDI architecture; 3.2.2.4. Multilevel architecture The objective of a multilevel architecture is to conduct a constructive synthesis of the reacti; 3.2.3. Model of an agent; 3.3. Multi-agent systems; 3.3.1. Communication between agents; 3.3.1.1. Coordination protocols; 3.3.1.2. Cooperation protocols; 3.3.1.3. Negotiation 3.4. Application of MAS in telecommunications3.4.1. MAS applications on the Web; 3.4.2. Application of MAS in virtual private networks; 3.4.3. Using MAS in the setting of third generation mobiles; 3.4.4. Application of MAS in network supervision and management; 3.5. Conclusion; CHAPTER 4. DYNAMIC SPECTRUM ACCESS; 4.1. Introduction; 4.2. Intelligent algorithms; 4.2.1. Neural networks; 4.2.2. Fuzzy logic; 4.2.3. Genetic algorithms; 4.3. Dynamic spectrum access; 4.3.1. Spectrum access using the auction approach; 4.3.2. Spectrum access using game theory 4.3.3. Spectrum access using Markov's approach |
| Record Nr. | UNINA-9910820091803321 |
|
Benmammar Badr
|
||
| London, : ISTE | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Signal processing for cognitive radios / / Sudharman K. Jayaweera
| Signal processing for cognitive radios / / Sudharman K. Jayaweera |
| Autore | Jayaweera Sudharman K. <1972-> |
| Pubbl/distr/stampa | Hoboken, New Jersey : , : John Wiley & Sons, Inc., , [2015] |
| Descrizione fisica | 1 online resource (1598 p.) |
| Disciplina | 621.382/2 |
| Soggetto topico |
Cognitive radio networks
Signal processing |
| ISBN |
1-118-82484-9
1-118-82481-4 1-118-98676-8 |
| Classificazione | TEC041000 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
-- PREFACE xv -- PART I INTRODUCTION TO COGNITIVE RADIOS 1 -- 1 Introduction 3 -- 1.1 Introduction, 3 -- 1.2 Signal Processing and Cognitive Radios, 4 -- 1.3 Software-Defined Radios, 6 -- 1.3.1 Software-Defined Radio Platforms, 14 -- 1.3.2 Software-Defined Radio Systems, 15 -- 1.4 From Software-Defined Radios to Cognitive Radios, 19 -- 1.4.1 The Spectrum Scarcity Problem, 19 -- 1.4.2 Emergence of CRs, 21 -- 1.5 What this Book is About, 22 -- 1.6 Summary, 26 -- 2 The Cognitive Radio 27 -- 2.1 Introduction, 27 -- 2.2 A Functional Model of a Cognitive Radio, 30 -- 2.2.1 Spectrum Knowledge Acquisition (Spectrum Awareness), 30 -- 2.2.2 Communications Decision-Making, 33 -- 2.2.3 Learning in Cognitive Radios, 33 -- 2.3 The Cognitive Radio Architecture, 35 -- 2.3.1 Spectrum Sensing Region of a Cognitive Engine, 36 -- 2.3.2 Radio Reconfiguration Region of a Cognitive Engine, 36 -- 2.3.3 Learning Region of a Cognitive Engine, 37 -- 2.3.4 Memory Region of a Cognitive Engine, 37 -- 2.4 The Ideal Cognitive Radio, 38 -- 2.5 Signal Processing Challenges in Cognitive Radios, 39 -- 2.6 Summary, 40 -- 3 Cognitive Radios and Dynamic Spectrum Sharing 42 -- 3.1 Introduction, 42 -- 3.2 Interference and Spectrum Opportunities, 46 -- 3.3 Dynamic Spectrum Access, 50 -- 3.4 Dynamic Spectrum Leasing, 54 -- 3.5 Challenges in DSS Cognitive Radios, 55 -- 3.6 Cognitive Radios and Future of Wireless Communications, 60 -- 3.7 Summary, 61 -- PART II THEORETICAL FOUNDATIONS 65 -- 4 Introduction to Detection Theory 67 -- 4.1 Introduction, 67 -- 4.2 Optimality Criteria: Bayesian versus Non-Bayesian, 71 -- 4.2.1 The Bayesian Approach, 72 -- 4.2.2 A Non-Bayesian Approach: Neyman / Pearson Optimality Criterion, 73 -- 4.3 Parametric Signal Detection Theory, 75 -- 4.3.1 Bayesian Optimal Detection, 76 -- 4.3.2 Neyman / Pearson Optimal Detection, 82 -- 4.3.3 Another Non-Bayesian Alternative: The Generalized Likelihood Ratio Test, 99 -- 4.3.4 Parametric Signal Detection in Additive Noise, 103 -- 4.4 Nonparametric Signal Detection Theory, 122.
4.4.1 Signal Detection in Additive Zero-Median Noise: The Sign Test, 124 -- 4.4.2 Signal Detection in Additive Symmetric Noise: The Rank Test, 125 -- 4.4.3 Signal Detection in Additive Zero Median, Zero Mean, Finite-Variance Noise: The t-Test, 126 -- 4.5 Summary, 127 -- 5 Introduction to Estimation Theory 132 -- 5.1 Introduction, 132 -- 5.2 Random Parameter Estimation: Bayesian Estimation, 134 -- 5.2.1 Minimum Mean-Squared Error Estimation, 134 -- 5.2.2 MMSE Estimation of Vector Parameters, 135 -- 5.2.3 Linear Minimum Mean-Squared Error Estimation, 138 -- 5.2.4 Maximum A Posteriori Probability Estimation, 139 -- 5.3 Nonrandom Parameter Estimation, 140 -- 5.3.1 Theory of Minimum Variance Unbiased Estimation, 142 -- 5.3.2 Best Linear Unbiased Estimator, 147 -- 5.3.3 Maximum Likelihood Estimation, 152 -- 5.3.4 Performance Bounds: Cramer-Rao Lower Bound, 154 -- 5.4 Summary, 158 -- 6 Power Spectrum Estimation 164 -- 6.1 Introduction, 164 -- 6.2 PSD Estimation of a Stationary Discrete-Time Signal, 168 -- 6.2.1 Correlogram Method, 168 -- 6.2.2 Periodogram Method, 170 -- 6.2.3 Performance of the Periodogram PSD Estimate, 172 -- 6.3 Blackman / Tukey Estimator of the Power Spectrum, 177 -- 6.4 Other PSD Estimators Based on Modified Periodograms, 181 -- 6.4.1 Bartlett PSD Estimator, 181 -- 6.4.2 Welch PSD Estimator, 183 -- 6.5 PSD Estimation of Nonstationary Discrete-Time Signals, 186 -- 6.5.1 Temporally Windowed Observations, 188 -- 6.5.2 Temporal and Spectral Smoothing of PSD Estimates of Nonstationary Discrete-Time Signals, 189 -- 6.5.3 DFT-Based PSD Computation, 191 -- 6.6 Spectral Correlation of Cyclostationary Signals, 192 -- 6.6.1 Spectral Correlation and Spectral Autocoherence, 196 -- 6.6.2 Time-Averaged Spectral Correlation, 197 -- 6.6.3 Estimation of Spectral Correlation, 198 -- 6.7 Summary, 200 -- 7 Markov Decision Processes 207 -- 7.1 Introduction, 207 -- 7.2 Markov Decission Processes, 209 -- 7.3 Finite-Horizon MDPs, 212 -- 7.3.1 Definitions, 212 -- 7.3.2 Optimal Policies for MDPs, 216. 7.4 Infinite-Horizon MDPs, 222 -- 7.4.1 Stationary Optimal Policies for Infinite-Horizon MDPs, 224 -- 7.4.2 Bellman-Optimality Equations, 227 -- 7.5 Partially Observable Markov Decision Processes, 232 -- 7.5.1 Definitions, 233 -- 7.5.2 Policy Evaluation for a Finite-Horizon POMDP, 238 -- 7.5.3 Optimality Equations for a Finite-Horizon POMDP, 241 -- 7.5.4 Optimal Policy Computation for a Finite-Horizon POMDP, 242 -- 7.5.5 Infinite-Horizon POMDPs, 257 -- 7.6 Summary, 259 -- 8 Bayesian Nonparametric Classification 269 -- 8.1 Introduction, 269 -- 8.2 K-Means Classification Algorithm, 274 -- 8.3 X-Means Classification Algorithm, 276 -- 8.4 Dirichlet Process Mixture Model, 278 -- 8.4.1 Dirichlet Process, 278 -- 8.4.2 Construction of the Dirichlet Process, 279 -- 8.4.3 DPMM, 282 -- 8.5 Bayesian Nonparametric Classification Based on the DPMM and the Gibbs Sampling, 283 -- 8.5.1 DPMM-Based Classification of Scalar Observations, 287 -- 8.5.2 DPMM-Based Classification of Multidimensional Gaussian Observations, 298 -- 8.5.3 DPMM-Based Classification of Possibly Non-Gaussian Multidimensional Observations, 308 -- 8.6 Summary, 315 -- PART III SIGNAL PROCESSING IN COGNITIVE RADIOS 321 -- 9 Wideband Spectrum Sensing 323 -- 9.1 Introduction, 323 -- 9.2 Wideband Spectrum Sensing Problem, 325 -- 9.3 Wideband Spectrum Scanning Problem, 326 -- 9.4 Spectrum Segmentation and Subbanding, 328 -- 9.5 Wideband Spectrum Sensing Receiver, 330 -- 9.5.1 Homodyne Receiver Configuration, 332 -- 9.5.2 Super Heterodyne Digital Receiver Configuration, 334 -- 9.5.3 A/D Conversion and the Discrete-Time Received Signal Model, 335 -- 9.6 Subband Selection Problem in Wideband Spectrum Sensing, 336 -- 9.6.1 Subband Dynamics, 338 -- 9.6.2 A POMDP Model for Subband Selection, 340 -- 9.6.3 An Optimal Subband Selection Policy for Spectrum Sensing, 347 -- 9.6.4 A Reduced-Complexity Optimal Sensing Decision-Making Algorithm with Independent Channels, 350 -- 9.6.5 A Reduced Complexity Optimal Sensing Decision-Making Algorithm with Independent Subbands, 354. 9.6.6 Optimal Myopic Sensing Decision Policies, 354 -- 9.7 A Reduced Complexity Optimal Subband Selection Framework with an Alternative Reward Function, 355 -- 9.7.1 A New Model for Subband Dynamics, 357 -- 9.7.2 A Simplified Reward Function and a Reduced-Complexity Optimal Policy, 359 -- 9.7.3 A Reduced Complexity Optimal Policy for Independent Subbands, 362 -- 9.7.4 Optimal Myopic Policies with Reduced Dimensional Subband State Vectors, 363 -- 9.8 Machine-Learning Aided Subband Selection Policies, 364 -- 9.8.1 Q-Learning, 365 -- 9.8.2 Q-Learning in a POMDP: A Q-Learning Algorithm for Subband Selection, 368 -- 9.9 Summary, 372 -- 10 Spectral Activity Detection inWideband Cognitive Radios 377 -- 10.1 Introduction, 377 -- 10.2 Optimal Wideband Spectral Activity Detection, 379 -- 10.3 Wideband Spectral Activity Detection, 386 -- 10.4 Wavelet Transform-Based Wideband Spectral Activity Detection, 392 -- 10.4.1 Wavelet Transform, 394 -- 10.4.2 Edge Detection with Wavelet Transform, 395 -- 10.4.3 Spectral Activity Detection Based on Edge Detection, 397 -- 10.5 Wideband Spectral Activity Detection in Non-Gaussian Noise, 398 -- 10.5.1 Arbitrary but Known Noise Distribution, 399 -- 10.5.2 Robust Spectral Activity Detection, 406 -- 10.6 Wideband Spectral Activity Detection with Compressive Sampling, 413 -- 10.6.1 Compressive Sampling, 415 -- 10.6.2 Compressive Sensing of Wideband Spectrum, 419 -- 10.7 Summary, 421 -- 11 Signal Classification inWideband Cognitive Radios 429 -- 11.1 Introduction, 429 -- 11.2 Signal Classification Problem in a Wideband Cognitive Radio, 431 -- 11.3 Feature Extraction for Signal Classification, 435 -- 11.3.1 Carrier/Center Frequency, 435 -- 11.3.2 Cyclostationary Features, 436 -- 11.3.3 Modulation Type and Order Features, 441 -- 11.4 A Signal Classification Architecture for a Wideband Cognitive Radio, 445 -- 11.5 Bayesian Nonparametric Signal Classification, 447 -- 11.6 Sequential Bayesian Nonparametric Signal Classification, 462 -- 11.7 Summary, 469. 12 Primary Signal Detection in DSA Cognitive Networks 472 -- 12.1 Introduction, 472 -- 12.2 Spectrum Sensing Problem in Dynamic Spectrum Sharing CR Networks, 475 -- 12.3 Autonomous Spectrum Sensing for Dynamic Spectrum Sharing, 479 -- 12.3.1 Secondary User Sensing Observations, 480 -- 12.3.2 Channel-State (Idle/Busy) Decisions, 481 -- 12.4 Limitations of Autonomous Spectrum Sensing, 489 -- 12.5 Cooperative Spectrum Sensing for Dynamic Spectrum Sharing, 492 -- 12.6 Cooperative Channel-State Detection, 495 -- 12.6.1 Local Processing and Sensing Reports from Secondary Users, 498 -- 12.6.2 Final Channel-State Decisions at the SSDC: Decision Fusion, 502 -- 12.7 Summary, 516 -- 13 Spectrum Decision-Making in DSA Cognitive Networks 519 -- 13.1 Introduction, 519 -- 13.2 Primary Channel Dynamic Model, 520 -- 13.3 Sensing Decisions in DSS Networks with Autonomous Cognitive Radios, 522 -- 13.3.1 Optimal Sensing Policy Determination, 525 -- 13.3.2 Optimal Myopic Sensing Policy Determination, 530 -- 13.4 Sensing Decisions in Cooperative DSS Networks, 533 -- 13.4.1 Optimal SSDC Decisions for Independent Channel Dynamics, 537 -- 13.4.2 Optimal Myopic Sensing Decisions at the SSDC with Independent Channel Dynamics, 541 -- 13.5 Summary, 550 -- 14 Dynamic Spectrum Leasing in Cognitive Radio Networks 553 -- 14.1 Introduction, 553 -- 14.2 DSL with Direct Rewards to Primary Users, 555 -- 14.2.1 Interference at the Primary Receiver, 560 -- 14.2.2 A Game Model for Dynamic Spectrum Leasing, 565 -- 14.2.3 Nash Equilibria in Noncooperative Games, 570 -- 14.2.4 Existence of a Nash Equilibrium in the DSL Game, 573 -- 14.3 DSL Based on Asymmetric Cooperation with Primary Users, 587 -- 14.3.1 A Primary / Secondary Coexistence Model, 588 -- 14.3.2 Asymmetric Cooperative Communications-Based DSL between Primary Users and a Centralized Secondary Network, 591 -- 14.3.3 Asymmetric Cooperative Communications-Based DSL between Primary Users and Autonomous Cognitive Secondary Users, 604 -- 14.4 Summary, 609. 15 Cooperative Cognitive Communications 613 -- 15.1 Introduction, 613 -- 15.2 Cooperative Spectrum Sensing, 619 -- 15.3 Cooperative Spectrum Sensing and Channel-Access Decisions, 621 -- 15.4 Cooperative Communications Strategies in Cognitive Radio Networks, 624 -- 15.5 Asymmetric Cooperative Relaying in DSA Cognitive Radios, 627 -- 15.5.1 Secondary User Optimal Power Allocation for Asymmetric Cooperative Relaying, 629 -- 15.5.2 Centralized Assignment of Cognitive Radios for Cooperation with Primary Users: An Ideal Approach, 635 -- 15.5.3 Centralized Assignment of Cognitive Radios for Cooperation with Primary Users: A Realistic Approach, 640 -- 15.6 Summary, 644 -- 16 Machine Learning in Cognitive Radios 647 -- 16.1 Introduction, 647 -- 16.2 Artificial Neural Networks, 650 -- 16.2.1 Learning Algorithms for LTUs, 651 -- 16.2.2 Layered Neural Networks, 655 -- 16.2.3 Learning in Layered Feed-Forward Networks: Back-Propagation Algorithm, 656 -- 16.2.4 Neural Networks in Cognitive Radios, 662 -- 16.3 Support Vector Machines, 664 -- 16.3.1 Statistical Learning Theory, 665 -- 16.3.2 Structural Risk Minimization with Support Vector Machines, 669 -- 16.3.3 Linear Support Vector Machines, 670 -- 16.3.4 Nonlinear Support Vector Machines, 674 -- 16.3.5 Kernel Function Implementation of Support Vector Machines, 677 -- 16.3.6 SVMs in Cognitive Radios, 679 -- 16.4 Reinforcement Learning, 681 -- 16.4.1 Temporal Difference Learning, 683 -- 16.4.2 Q-Learning in a POMDP: Replicated Q-Learning, 684 -- 16.4.3 Reinforcement Learning in Cognitive Radios, 686 -- 16.5 Multiagent Learning, 688 -- 16.5.1 Game-Theoretic Multiagent Learning, 691 -- 16.5.2 Cooperative Multiagent Learning, 694 -- 16.5.3 Multiagent Learning in Cognitive Radio Networks, 696 -- 16.6 Summary, 698 -- Appendix A Nyquist Sampling Theorem 704 -- Appendix B A Collection of Useful Probability Distributions 711 -- B.1 Univariate Distributions, 711 -- B.2 Multivariate Distributions, 713 -- Appendix C Conjugate Priors 716 -- REFERENCES 721. INDEX 740. |
| Record Nr. | UNINA-9910132275003321 |
|
Jayaweera Sudharman K. <1972->
|
||
| Hoboken, New Jersey : , : John Wiley & Sons, Inc., , [2015] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Signal processing for cognitive radios / / Sudharman K. Jayaweera
| Signal processing for cognitive radios / / Sudharman K. Jayaweera |
| Autore | Jayaweera Sudharman K. <1972-> |
| Pubbl/distr/stampa | Hoboken, New Jersey : , : John Wiley & Sons, Inc., , [2015] |
| Descrizione fisica | 1 online resource (1598 p.) |
| Disciplina | 621.382/2 |
| Soggetto topico |
Cognitive radio networks
Signal processing |
| ISBN |
1-118-82484-9
1-118-82481-4 1-118-98676-8 |
| Classificazione | TEC041000 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
-- PREFACE xv -- PART I INTRODUCTION TO COGNITIVE RADIOS 1 -- 1 Introduction 3 -- 1.1 Introduction, 3 -- 1.2 Signal Processing and Cognitive Radios, 4 -- 1.3 Software-Defined Radios, 6 -- 1.3.1 Software-Defined Radio Platforms, 14 -- 1.3.2 Software-Defined Radio Systems, 15 -- 1.4 From Software-Defined Radios to Cognitive Radios, 19 -- 1.4.1 The Spectrum Scarcity Problem, 19 -- 1.4.2 Emergence of CRs, 21 -- 1.5 What this Book is About, 22 -- 1.6 Summary, 26 -- 2 The Cognitive Radio 27 -- 2.1 Introduction, 27 -- 2.2 A Functional Model of a Cognitive Radio, 30 -- 2.2.1 Spectrum Knowledge Acquisition (Spectrum Awareness), 30 -- 2.2.2 Communications Decision-Making, 33 -- 2.2.3 Learning in Cognitive Radios, 33 -- 2.3 The Cognitive Radio Architecture, 35 -- 2.3.1 Spectrum Sensing Region of a Cognitive Engine, 36 -- 2.3.2 Radio Reconfiguration Region of a Cognitive Engine, 36 -- 2.3.3 Learning Region of a Cognitive Engine, 37 -- 2.3.4 Memory Region of a Cognitive Engine, 37 -- 2.4 The Ideal Cognitive Radio, 38 -- 2.5 Signal Processing Challenges in Cognitive Radios, 39 -- 2.6 Summary, 40 -- 3 Cognitive Radios and Dynamic Spectrum Sharing 42 -- 3.1 Introduction, 42 -- 3.2 Interference and Spectrum Opportunities, 46 -- 3.3 Dynamic Spectrum Access, 50 -- 3.4 Dynamic Spectrum Leasing, 54 -- 3.5 Challenges in DSS Cognitive Radios, 55 -- 3.6 Cognitive Radios and Future of Wireless Communications, 60 -- 3.7 Summary, 61 -- PART II THEORETICAL FOUNDATIONS 65 -- 4 Introduction to Detection Theory 67 -- 4.1 Introduction, 67 -- 4.2 Optimality Criteria: Bayesian versus Non-Bayesian, 71 -- 4.2.1 The Bayesian Approach, 72 -- 4.2.2 A Non-Bayesian Approach: Neyman / Pearson Optimality Criterion, 73 -- 4.3 Parametric Signal Detection Theory, 75 -- 4.3.1 Bayesian Optimal Detection, 76 -- 4.3.2 Neyman / Pearson Optimal Detection, 82 -- 4.3.3 Another Non-Bayesian Alternative: The Generalized Likelihood Ratio Test, 99 -- 4.3.4 Parametric Signal Detection in Additive Noise, 103 -- 4.4 Nonparametric Signal Detection Theory, 122.
4.4.1 Signal Detection in Additive Zero-Median Noise: The Sign Test, 124 -- 4.4.2 Signal Detection in Additive Symmetric Noise: The Rank Test, 125 -- 4.4.3 Signal Detection in Additive Zero Median, Zero Mean, Finite-Variance Noise: The t-Test, 126 -- 4.5 Summary, 127 -- 5 Introduction to Estimation Theory 132 -- 5.1 Introduction, 132 -- 5.2 Random Parameter Estimation: Bayesian Estimation, 134 -- 5.2.1 Minimum Mean-Squared Error Estimation, 134 -- 5.2.2 MMSE Estimation of Vector Parameters, 135 -- 5.2.3 Linear Minimum Mean-Squared Error Estimation, 138 -- 5.2.4 Maximum A Posteriori Probability Estimation, 139 -- 5.3 Nonrandom Parameter Estimation, 140 -- 5.3.1 Theory of Minimum Variance Unbiased Estimation, 142 -- 5.3.2 Best Linear Unbiased Estimator, 147 -- 5.3.3 Maximum Likelihood Estimation, 152 -- 5.3.4 Performance Bounds: Cramer-Rao Lower Bound, 154 -- 5.4 Summary, 158 -- 6 Power Spectrum Estimation 164 -- 6.1 Introduction, 164 -- 6.2 PSD Estimation of a Stationary Discrete-Time Signal, 168 -- 6.2.1 Correlogram Method, 168 -- 6.2.2 Periodogram Method, 170 -- 6.2.3 Performance of the Periodogram PSD Estimate, 172 -- 6.3 Blackman / Tukey Estimator of the Power Spectrum, 177 -- 6.4 Other PSD Estimators Based on Modified Periodograms, 181 -- 6.4.1 Bartlett PSD Estimator, 181 -- 6.4.2 Welch PSD Estimator, 183 -- 6.5 PSD Estimation of Nonstationary Discrete-Time Signals, 186 -- 6.5.1 Temporally Windowed Observations, 188 -- 6.5.2 Temporal and Spectral Smoothing of PSD Estimates of Nonstationary Discrete-Time Signals, 189 -- 6.5.3 DFT-Based PSD Computation, 191 -- 6.6 Spectral Correlation of Cyclostationary Signals, 192 -- 6.6.1 Spectral Correlation and Spectral Autocoherence, 196 -- 6.6.2 Time-Averaged Spectral Correlation, 197 -- 6.6.3 Estimation of Spectral Correlation, 198 -- 6.7 Summary, 200 -- 7 Markov Decision Processes 207 -- 7.1 Introduction, 207 -- 7.2 Markov Decission Processes, 209 -- 7.3 Finite-Horizon MDPs, 212 -- 7.3.1 Definitions, 212 -- 7.3.2 Optimal Policies for MDPs, 216. 7.4 Infinite-Horizon MDPs, 222 -- 7.4.1 Stationary Optimal Policies for Infinite-Horizon MDPs, 224 -- 7.4.2 Bellman-Optimality Equations, 227 -- 7.5 Partially Observable Markov Decision Processes, 232 -- 7.5.1 Definitions, 233 -- 7.5.2 Policy Evaluation for a Finite-Horizon POMDP, 238 -- 7.5.3 Optimality Equations for a Finite-Horizon POMDP, 241 -- 7.5.4 Optimal Policy Computation for a Finite-Horizon POMDP, 242 -- 7.5.5 Infinite-Horizon POMDPs, 257 -- 7.6 Summary, 259 -- 8 Bayesian Nonparametric Classification 269 -- 8.1 Introduction, 269 -- 8.2 K-Means Classification Algorithm, 274 -- 8.3 X-Means Classification Algorithm, 276 -- 8.4 Dirichlet Process Mixture Model, 278 -- 8.4.1 Dirichlet Process, 278 -- 8.4.2 Construction of the Dirichlet Process, 279 -- 8.4.3 DPMM, 282 -- 8.5 Bayesian Nonparametric Classification Based on the DPMM and the Gibbs Sampling, 283 -- 8.5.1 DPMM-Based Classification of Scalar Observations, 287 -- 8.5.2 DPMM-Based Classification of Multidimensional Gaussian Observations, 298 -- 8.5.3 DPMM-Based Classification of Possibly Non-Gaussian Multidimensional Observations, 308 -- 8.6 Summary, 315 -- PART III SIGNAL PROCESSING IN COGNITIVE RADIOS 321 -- 9 Wideband Spectrum Sensing 323 -- 9.1 Introduction, 323 -- 9.2 Wideband Spectrum Sensing Problem, 325 -- 9.3 Wideband Spectrum Scanning Problem, 326 -- 9.4 Spectrum Segmentation and Subbanding, 328 -- 9.5 Wideband Spectrum Sensing Receiver, 330 -- 9.5.1 Homodyne Receiver Configuration, 332 -- 9.5.2 Super Heterodyne Digital Receiver Configuration, 334 -- 9.5.3 A/D Conversion and the Discrete-Time Received Signal Model, 335 -- 9.6 Subband Selection Problem in Wideband Spectrum Sensing, 336 -- 9.6.1 Subband Dynamics, 338 -- 9.6.2 A POMDP Model for Subband Selection, 340 -- 9.6.3 An Optimal Subband Selection Policy for Spectrum Sensing, 347 -- 9.6.4 A Reduced-Complexity Optimal Sensing Decision-Making Algorithm with Independent Channels, 350 -- 9.6.5 A Reduced Complexity Optimal Sensing Decision-Making Algorithm with Independent Subbands, 354. 9.6.6 Optimal Myopic Sensing Decision Policies, 354 -- 9.7 A Reduced Complexity Optimal Subband Selection Framework with an Alternative Reward Function, 355 -- 9.7.1 A New Model for Subband Dynamics, 357 -- 9.7.2 A Simplified Reward Function and a Reduced-Complexity Optimal Policy, 359 -- 9.7.3 A Reduced Complexity Optimal Policy for Independent Subbands, 362 -- 9.7.4 Optimal Myopic Policies with Reduced Dimensional Subband State Vectors, 363 -- 9.8 Machine-Learning Aided Subband Selection Policies, 364 -- 9.8.1 Q-Learning, 365 -- 9.8.2 Q-Learning in a POMDP: A Q-Learning Algorithm for Subband Selection, 368 -- 9.9 Summary, 372 -- 10 Spectral Activity Detection inWideband Cognitive Radios 377 -- 10.1 Introduction, 377 -- 10.2 Optimal Wideband Spectral Activity Detection, 379 -- 10.3 Wideband Spectral Activity Detection, 386 -- 10.4 Wavelet Transform-Based Wideband Spectral Activity Detection, 392 -- 10.4.1 Wavelet Transform, 394 -- 10.4.2 Edge Detection with Wavelet Transform, 395 -- 10.4.3 Spectral Activity Detection Based on Edge Detection, 397 -- 10.5 Wideband Spectral Activity Detection in Non-Gaussian Noise, 398 -- 10.5.1 Arbitrary but Known Noise Distribution, 399 -- 10.5.2 Robust Spectral Activity Detection, 406 -- 10.6 Wideband Spectral Activity Detection with Compressive Sampling, 413 -- 10.6.1 Compressive Sampling, 415 -- 10.6.2 Compressive Sensing of Wideband Spectrum, 419 -- 10.7 Summary, 421 -- 11 Signal Classification inWideband Cognitive Radios 429 -- 11.1 Introduction, 429 -- 11.2 Signal Classification Problem in a Wideband Cognitive Radio, 431 -- 11.3 Feature Extraction for Signal Classification, 435 -- 11.3.1 Carrier/Center Frequency, 435 -- 11.3.2 Cyclostationary Features, 436 -- 11.3.3 Modulation Type and Order Features, 441 -- 11.4 A Signal Classification Architecture for a Wideband Cognitive Radio, 445 -- 11.5 Bayesian Nonparametric Signal Classification, 447 -- 11.6 Sequential Bayesian Nonparametric Signal Classification, 462 -- 11.7 Summary, 469. 12 Primary Signal Detection in DSA Cognitive Networks 472 -- 12.1 Introduction, 472 -- 12.2 Spectrum Sensing Problem in Dynamic Spectrum Sharing CR Networks, 475 -- 12.3 Autonomous Spectrum Sensing for Dynamic Spectrum Sharing, 479 -- 12.3.1 Secondary User Sensing Observations, 480 -- 12.3.2 Channel-State (Idle/Busy) Decisions, 481 -- 12.4 Limitations of Autonomous Spectrum Sensing, 489 -- 12.5 Cooperative Spectrum Sensing for Dynamic Spectrum Sharing, 492 -- 12.6 Cooperative Channel-State Detection, 495 -- 12.6.1 Local Processing and Sensing Reports from Secondary Users, 498 -- 12.6.2 Final Channel-State Decisions at the SSDC: Decision Fusion, 502 -- 12.7 Summary, 516 -- 13 Spectrum Decision-Making in DSA Cognitive Networks 519 -- 13.1 Introduction, 519 -- 13.2 Primary Channel Dynamic Model, 520 -- 13.3 Sensing Decisions in DSS Networks with Autonomous Cognitive Radios, 522 -- 13.3.1 Optimal Sensing Policy Determination, 525 -- 13.3.2 Optimal Myopic Sensing Policy Determination, 530 -- 13.4 Sensing Decisions in Cooperative DSS Networks, 533 -- 13.4.1 Optimal SSDC Decisions for Independent Channel Dynamics, 537 -- 13.4.2 Optimal Myopic Sensing Decisions at the SSDC with Independent Channel Dynamics, 541 -- 13.5 Summary, 550 -- 14 Dynamic Spectrum Leasing in Cognitive Radio Networks 553 -- 14.1 Introduction, 553 -- 14.2 DSL with Direct Rewards to Primary Users, 555 -- 14.2.1 Interference at the Primary Receiver, 560 -- 14.2.2 A Game Model for Dynamic Spectrum Leasing, 565 -- 14.2.3 Nash Equilibria in Noncooperative Games, 570 -- 14.2.4 Existence of a Nash Equilibrium in the DSL Game, 573 -- 14.3 DSL Based on Asymmetric Cooperation with Primary Users, 587 -- 14.3.1 A Primary / Secondary Coexistence Model, 588 -- 14.3.2 Asymmetric Cooperative Communications-Based DSL between Primary Users and a Centralized Secondary Network, 591 -- 14.3.3 Asymmetric Cooperative Communications-Based DSL between Primary Users and Autonomous Cognitive Secondary Users, 604 -- 14.4 Summary, 609. 15 Cooperative Cognitive Communications 613 -- 15.1 Introduction, 613 -- 15.2 Cooperative Spectrum Sensing, 619 -- 15.3 Cooperative Spectrum Sensing and Channel-Access Decisions, 621 -- 15.4 Cooperative Communications Strategies in Cognitive Radio Networks, 624 -- 15.5 Asymmetric Cooperative Relaying in DSA Cognitive Radios, 627 -- 15.5.1 Secondary User Optimal Power Allocation for Asymmetric Cooperative Relaying, 629 -- 15.5.2 Centralized Assignment of Cognitive Radios for Cooperation with Primary Users: An Ideal Approach, 635 -- 15.5.3 Centralized Assignment of Cognitive Radios for Cooperation with Primary Users: A Realistic Approach, 640 -- 15.6 Summary, 644 -- 16 Machine Learning in Cognitive Radios 647 -- 16.1 Introduction, 647 -- 16.2 Artificial Neural Networks, 650 -- 16.2.1 Learning Algorithms for LTUs, 651 -- 16.2.2 Layered Neural Networks, 655 -- 16.2.3 Learning in Layered Feed-Forward Networks: Back-Propagation Algorithm, 656 -- 16.2.4 Neural Networks in Cognitive Radios, 662 -- 16.3 Support Vector Machines, 664 -- 16.3.1 Statistical Learning Theory, 665 -- 16.3.2 Structural Risk Minimization with Support Vector Machines, 669 -- 16.3.3 Linear Support Vector Machines, 670 -- 16.3.4 Nonlinear Support Vector Machines, 674 -- 16.3.5 Kernel Function Implementation of Support Vector Machines, 677 -- 16.3.6 SVMs in Cognitive Radios, 679 -- 16.4 Reinforcement Learning, 681 -- 16.4.1 Temporal Difference Learning, 683 -- 16.4.2 Q-Learning in a POMDP: Replicated Q-Learning, 684 -- 16.4.3 Reinforcement Learning in Cognitive Radios, 686 -- 16.5 Multiagent Learning, 688 -- 16.5.1 Game-Theoretic Multiagent Learning, 691 -- 16.5.2 Cooperative Multiagent Learning, 694 -- 16.5.3 Multiagent Learning in Cognitive Radio Networks, 696 -- 16.6 Summary, 698 -- Appendix A Nyquist Sampling Theorem 704 -- Appendix B A Collection of Useful Probability Distributions 711 -- B.1 Univariate Distributions, 711 -- B.2 Multivariate Distributions, 713 -- Appendix C Conjugate Priors 716 -- REFERENCES 721. INDEX 740. |
| Record Nr. | UNINA-9910819093303321 |
|
Jayaweera Sudharman K. <1972->
|
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| Hoboken, New Jersey : , : John Wiley & Sons, Inc., , [2015] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Spectrum sensing techniques and applications / / Marcelo Sampaio de Alencar, Fabrício Braga Soares de Carvalho, and Walson Terllizzie Araújo Lopes
| Spectrum sensing techniques and applications / / Marcelo Sampaio de Alencar, Fabrício Braga Soares de Carvalho, and Walson Terllizzie Araújo Lopes |
| Autore | Alencar Marcelo S. <1957-> |
| Pubbl/distr/stampa | New York, New York : , : Momentum Press Engineering, , [2018] |
| Descrizione fisica | 1 online resource (1 volume) : illustrations |
| Disciplina | 621.384 |
| Collana | Communications and signal processing collection |
| Soggetto topico | Cognitive radio networks |
| Soggetto genere / forma | Electronic books. |
| ISBN | 1-60650-980-2 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910467148103321 |
|
Alencar Marcelo S. <1957->
|
||
| New York, New York : , : Momentum Press Engineering, , [2018] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Spectrum sensing techniques and applications / / Marcelo Sampaio de Alencar, Fabrício Braga Soares de Carvalho, and Walson Terllizzie Araújo Lopes
| Spectrum sensing techniques and applications / / Marcelo Sampaio de Alencar, Fabrício Braga Soares de Carvalho, and Walson Terllizzie Araújo Lopes |
| Autore | Alencar Marcelo S. <1957-> |
| Pubbl/distr/stampa | New York, New York : , : Momentum Press Engineering, , [2018] |
| Descrizione fisica | 1 online resource (1 volume) : illustrations |
| Disciplina | 621.384 |
| Collana | Communications and signal processing collection |
| Soggetto topico | Cognitive radio networks |
| ISBN | 1-60650-980-2 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910796478303321 |
|
Alencar Marcelo S. <1957->
|
||
| New York, New York : , : Momentum Press Engineering, , [2018] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Spectrum sensing techniques and applications / / Marcelo Sampaio de Alencar, Fabrício Braga Soares de Carvalho, and Walson Terllizzie Araújo Lopes
| Spectrum sensing techniques and applications / / Marcelo Sampaio de Alencar, Fabrício Braga Soares de Carvalho, and Walson Terllizzie Araújo Lopes |
| Autore | Alencar Marcelo S. <1957-> |
| Pubbl/distr/stampa | New York, New York : , : Momentum Press Engineering, , [2018] |
| Descrizione fisica | 1 online resource (1 volume) : illustrations |
| Disciplina | 621.384 |
| Collana | Communications and signal processing collection |
| Soggetto topico | Cognitive radio networks |
| ISBN | 1-60650-980-2 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910809557503321 |
|
Alencar Marcelo S. <1957->
|
||
| New York, New York : , : Momentum Press Engineering, , [2018] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Spectrum sharing in cognitive radio networks : towards highly connected environments / / Prabhat Thakur, Ghanshyam Singh
| Spectrum sharing in cognitive radio networks : towards highly connected environments / / Prabhat Thakur, Ghanshyam Singh |
| Autore | Thakur Prabhat |
| Pubbl/distr/stampa | Hoboken, NJ : , : Wiley, , [2021] |
| Descrizione fisica | 1 online resource (387 pages) |
| Disciplina | 621.384 |
| Soggetto topico |
Cognitive radio networks
Radio resource management (Wireless communications) |
| ISBN |
1-119-66544-2
1-119-66545-0 1-119-66543-4 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910554873903321 |
|
Thakur Prabhat
|
||
| Hoboken, NJ : , : Wiley, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Spectrum sharing in cognitive radio networks : towards highly connected environments / / Prabhat Thakur, Ghanshyam Singh
| Spectrum sharing in cognitive radio networks : towards highly connected environments / / Prabhat Thakur, Ghanshyam Singh |
| Autore | Thakur Prabhat |
| Pubbl/distr/stampa | Hoboken, NJ : , : Wiley, , [2021] |
| Descrizione fisica | 1 online resource (387 pages) |
| Disciplina | 621.384 |
| Soggetto topico |
Cognitive radio networks
Radio resource management (Wireless communications) |
| ISBN |
1-119-66544-2
1-119-66545-0 1-119-66543-4 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910830790203321 |
|
Thakur Prabhat
|
||
| Hoboken, NJ : , : Wiley, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
