2007 3rd International Conference on Testbeds and Research Infrastructure for the Development of Networks and Communities : Lake Buena Vista, FL, 21-23 May 2007 |
Pubbl/distr/stampa | IEEE |
Disciplina | 621.3845 |
Soggetto topico |
Wireless communication systems
Wireless communication systems - Design and construction Mobile communication systems |
ISBN | 1-5090-8750-8 |
Formato | Materiale a stampa |
Livello bibliografico | Monografia |
Lingua di pubblicazione | eng |
Altri titoli varianti |
Visualizing Software for Understanding and Analysis
2007 3rd International Conference on Testbeds and Research Infrastructure for the Development of Networks and Communities |
Record Nr. | UNISA-996215962403316 |
IEEE | ||
Materiale a stampa | ||
Lo trovi qui: Univ. di Salerno | ||
|
2007 3rd International Conference on Testbeds and Research Infrastructure for the Development of Networks and Communities : Lake Buena Vista, FL, 21-23 May 2007 |
Pubbl/distr/stampa | IEEE |
Disciplina | 621.3845 |
Soggetto topico |
Wireless communication systems
Wireless communication systems - Design and construction Mobile communication systems |
ISBN |
9781509087501
1509087508 |
Formato | Materiale a stampa |
Livello bibliografico | Monografia |
Lingua di pubblicazione | eng |
Altri titoli varianti |
Visualizing Software for Understanding and Analysis
2007 3rd International Conference on Testbeds and Research Infrastructure for the Development of Networks and Communities |
Record Nr. | UNINA-9910145107703321 |
IEEE | ||
Materiale a stampa | ||
Lo trovi qui: Univ. Federico II | ||
|
2013 82nd ARFTG Microwave Measurement Conference : Columbus, Ohio, USA, 18-21 November 2013 |
Pubbl/distr/stampa | IEEE |
Disciplina | 621.381/3 |
Soggetto topico |
Microwave measurements
Microwave circuits Millimeter waves Millimeter wave devices Wireless communication systems - Design and construction |
ISBN | 1-4799-2935-2 |
Formato | Materiale a stampa |
Livello bibliografico | Monografia |
Lingua di pubblicazione | eng |
Altri titoli varianti | 82nd ARFTG Microwave Measurement Conference |
Record Nr. | UNISA-996279467303316 |
IEEE | ||
Materiale a stampa | ||
Lo trovi qui: Univ. di Salerno | ||
|
2013 82nd ARFTG Microwave Measurement Conference : Columbus, Ohio, USA, 18-21 November 2013 |
Pubbl/distr/stampa | IEEE |
Disciplina | 621.381/3 |
Soggetto topico |
Microwave measurements
Microwave circuits Millimeter waves Millimeter wave devices Wireless communication systems - Design and construction |
ISBN |
9781479929351
1479929352 |
Formato | Materiale a stampa |
Livello bibliografico | Monografia |
Lingua di pubblicazione | eng |
Altri titoli varianti | 82nd ARFTG Microwave Measurement Conference |
Record Nr. | UNINA-9910135182003321 |
IEEE | ||
Materiale a stampa | ||
Lo trovi qui: Univ. Federico II | ||
|
Analytical modeling of wireless communication systems / / Carla-Fabiana Chiasserini, Marco Gribaudo, Daniele Manini |
Autore | Chiasserini Carla-Fabiana |
Edizione | [1st edition] |
Pubbl/distr/stampa | London, England ; ; Hoboken, New Jersey : , : ISTE : , : Wiley, , 2016 |
Descrizione fisica | 1 online resource (155 p.) |
Disciplina | 621.384 |
Collana | Stochastic Models in Computer Science and Telecommunication Networks Set |
Soggetto topico |
Wireless communication systems
Wireless communication systems - Design and construction |
ISBN |
1-119-30774-0
1-119-30773-2 1-119-30772-4 |
Formato | Materiale a stampa |
Livello bibliografico | Monografia |
Lingua di pubblicazione | eng |
Nota di contenuto |
Cover; Title Page; Copyright; Contents; Preface; Introduction; List of Acronyms; PART 1: Sensor Networks; PART 2: Vehicular Networks; PART 3: Cellular Networks; Bibliography; Index; Other titles from ISTE in Networks and Telecommunications; EULA; 1: Fluid Models and Energy Issues; 2: Hybrid Automata for Transient Delay Analysis; 3: Safety Message Broadcasting; 4: Modeling Information Sharing; 5: Multi-RAT Algorithms; 1.1. The fluid-based approach; 1.2. Network scenario; 1.3. The sensor network model; 1.4. Results; 2.1. Event detection in WSNs; 2.2. Model for single-hop network topologies
2.3. Solution technique2.4. Model for multi-hop network topologies; 2.5. Model validation and exploitation results; 2.6. Discussion; 3.1. System description; 3.2. Dissemination of safety messages; 3.3. Assumptions and notations; 3.4. Model outline; 3.5. Computation of the block probability; 3.6. Computation of the probability of first reception; 3.7. Performance evaluation; 4.1. System scenario; 4.2. Modeling information exchange in IVN; 4.3. Computation of the probability of successful information retrieval; 4.4. Model validation and exploitation; 5.1. RAT network; 5.2. Network model 5.3. Model solution5.4. Performance evaluation; 1.1.1. Sensor density and traffic generation; 1.1.2. Data routing; 1.1.3. Local and relay traffic rates; 1.1.4. Channel contention and data transmission; 1.1.5. Mean packet delivery delay; 1.1.6. Sensor active/sleep behavior; 1.3.1. A minimum energy routing strategy: computing u(r'|r); 1.3.2. Channel contention and data transmission: computing s(r) and PR(r); 1.3.3. Mean packet delivery delay: computing q(r); 1.4.1. Model validation; 1.4.2. Model exploitation; 1.4.3. Model solution complexity and accuracy; 2.1.1. The 802.15.4 MAC protocol 2.2.1. Single message transfer2.2.2. Multiple message transfers; 2.3.1. Time discretization; 2.3.2. Transient solution; 2.3.3. Performance metrics computation; 3.2.1. The spatial differentiation approach; 3.2.2. The safety application; 3.6.1. A Gaussian approximation to the transient system behavior; 3.7.1. The impact of power capture; 3.7.2. The case of occupation probability ρ = 1; 3.7.3. The case of homogeneous occupation probability ρ < 1; 3.7.4. The case of inhomogeneous occupation probability; 3.7.5. The impact of the forwarding policy; 4.2.1. Model description; 5.1.1. Scenario 5.1.2. RAT selection strategy5.2.1. Functional rates; 5.3.1. Analytical approach; 5.3.2. Computation of performance metrics; 5.4.1. Setting and results; 1.3.1.1. Computing єm(r, r')|; 1.3.1.2. Computing FkmE(e|r); 1.3.1.3. Computation of the minimum energy path (equation [1.9]); 1.3.1.4. Computing FmE(e|r); 1.3.1.5. Computing ps; r(r'|e); 1.3.1.6. Computing u(r'|r); 1.3.2.1. Computation of the mean number of transmissions freezing the backoff counter; 1.3.3.1. Computing q(r) for always active sensors; 1.3.3.2. Computing q(r) for active and sleeping sensors; 5.1.1.1. Network scenario 5.2.1.1. Rates derivation |
Record Nr. | UNINA-9910135012303321 |
Chiasserini Carla-Fabiana | ||
London, England ; ; Hoboken, New Jersey : , : ISTE : , : Wiley, , 2016 | ||
Materiale a stampa | ||
Lo trovi qui: Univ. Federico II | ||
|
Analytical modeling of wireless communication systems / / Carla-Fabiana Chiasserini, Marco Gribaudo, Daniele Manini |
Autore | Chiasserini Carla-Fabiana |
Edizione | [1st edition] |
Pubbl/distr/stampa | London, England ; ; Hoboken, New Jersey : , : ISTE : , : Wiley, , 2016 |
Descrizione fisica | 1 online resource (155 p.) |
Disciplina | 621.384 |
Collana | Stochastic Models in Computer Science and Telecommunication Networks Set |
Soggetto topico |
Wireless communication systems
Wireless communication systems - Design and construction |
ISBN |
1-119-30774-0
1-119-30773-2 1-119-30772-4 |
Formato | Materiale a stampa |
Livello bibliografico | Monografia |
Lingua di pubblicazione | eng |
Nota di contenuto |
Cover; Title Page; Copyright; Contents; Preface; Introduction; List of Acronyms; PART 1: Sensor Networks; PART 2: Vehicular Networks; PART 3: Cellular Networks; Bibliography; Index; Other titles from ISTE in Networks and Telecommunications; EULA; 1: Fluid Models and Energy Issues; 2: Hybrid Automata for Transient Delay Analysis; 3: Safety Message Broadcasting; 4: Modeling Information Sharing; 5: Multi-RAT Algorithms; 1.1. The fluid-based approach; 1.2. Network scenario; 1.3. The sensor network model; 1.4. Results; 2.1. Event detection in WSNs; 2.2. Model for single-hop network topologies
2.3. Solution technique2.4. Model for multi-hop network topologies; 2.5. Model validation and exploitation results; 2.6. Discussion; 3.1. System description; 3.2. Dissemination of safety messages; 3.3. Assumptions and notations; 3.4. Model outline; 3.5. Computation of the block probability; 3.6. Computation of the probability of first reception; 3.7. Performance evaluation; 4.1. System scenario; 4.2. Modeling information exchange in IVN; 4.3. Computation of the probability of successful information retrieval; 4.4. Model validation and exploitation; 5.1. RAT network; 5.2. Network model 5.3. Model solution5.4. Performance evaluation; 1.1.1. Sensor density and traffic generation; 1.1.2. Data routing; 1.1.3. Local and relay traffic rates; 1.1.4. Channel contention and data transmission; 1.1.5. Mean packet delivery delay; 1.1.6. Sensor active/sleep behavior; 1.3.1. A minimum energy routing strategy: computing u(r'|r); 1.3.2. Channel contention and data transmission: computing s(r) and PR(r); 1.3.3. Mean packet delivery delay: computing q(r); 1.4.1. Model validation; 1.4.2. Model exploitation; 1.4.3. Model solution complexity and accuracy; 2.1.1. The 802.15.4 MAC protocol 2.2.1. Single message transfer2.2.2. Multiple message transfers; 2.3.1. Time discretization; 2.3.2. Transient solution; 2.3.3. Performance metrics computation; 3.2.1. The spatial differentiation approach; 3.2.2. The safety application; 3.6.1. A Gaussian approximation to the transient system behavior; 3.7.1. The impact of power capture; 3.7.2. The case of occupation probability ρ = 1; 3.7.3. The case of homogeneous occupation probability ρ < 1; 3.7.4. The case of inhomogeneous occupation probability; 3.7.5. The impact of the forwarding policy; 4.2.1. Model description; 5.1.1. Scenario 5.1.2. RAT selection strategy5.2.1. Functional rates; 5.3.1. Analytical approach; 5.3.2. Computation of performance metrics; 5.4.1. Setting and results; 1.3.1.1. Computing єm(r, r')|; 1.3.1.2. Computing FkmE(e|r); 1.3.1.3. Computation of the minimum energy path (equation [1.9]); 1.3.1.4. Computing FmE(e|r); 1.3.1.5. Computing ps; r(r'|e); 1.3.1.6. Computing u(r'|r); 1.3.2.1. Computation of the mean number of transmissions freezing the backoff counter; 1.3.3.1. Computing q(r) for always active sensors; 1.3.3.2. Computing q(r) for active and sleeping sensors; 5.1.1.1. Network scenario 5.2.1.1. Rates derivation |
Record Nr. | UNINA-9910821465503321 |
Chiasserini Carla-Fabiana | ||
London, England ; ; Hoboken, New Jersey : , : ISTE : , : Wiley, , 2016 | ||
Materiale a stampa | ||
Lo trovi qui: Univ. Federico II | ||
|
Artificial intelligence for 6G / / Haesik Kim |
Autore | Kim Haesik |
Pubbl/distr/stampa | Cham, Switzerland : , : Springer International Publishing, , [2022] |
Descrizione fisica | 1 online resource (534 pages) |
Disciplina | 621.384 |
Soggetto topico |
Wireless communication systems - Design and construction
Wireless communication systems - Economic aspects |
ISBN |
9783030950415
9783030950408 |
Formato | Materiale a stampa |
Livello bibliografico | Monografia |
Lingua di pubblicazione | eng |
Nota di contenuto |
Intro -- Preface -- Contents -- Abbreviations -- Part I Artificial Intelligence Techniques -- 1 Historical Sketch of Artificial Intelligence -- 1.1 Introduction to Artificial Intelligence -- 1.2 History of Artificial Intelligence -- References -- 2 Artificial Intelligence Ecosystem, Techniques, and Use Cases -- 2.1 Artificial Intelligence Ecosystem -- 2.2 Hardware and Software of Artificial Intelligence -- 2.3 Artificial Intelligence Techniques and Selection -- 2.4 Artificial Intelligence Workflow and Use Cases -- References -- 3 Unsupervised Learning -- 3.1 Types and Performance Metrics of Unsupervised Learning -- 3.2 Clustering Algorithms -- 3.2.1 Hierarchical Clustering -- 3.2.2 Partitional Clustering -- 3.3 Association Rule Mining -- 3.4 Dimensionality Reduction -- 3.5 Problems -- References -- 4 Supervised Learning -- 4.1 Supervised Learning Workflow, Metrics, and Ensemble Methods -- 4.2 Classification of Supervised Learning -- 4.2.1 Decision Tree -- 4.2.2 K-Nearest Neighbours -- 4.2.3 Support Vector Machine -- 4.3 Regression of Supervised Learning -- 4.3.1 Linear Regression -- 4.3.2 Gradient Descent Algorithms -- 4.3.3 Logistic Regression -- 4.4 Problems -- References -- 5 Reinforcement Learning -- 5.1 Introduction to Reinforcement Learning and Markov Decision Process -- 5.2 Model-Based Approaches -- 5.2.1 Policy Iteration -- 5.2.2 Value Iteration -- 5.3 Model-Free Approaches -- 5.3.1 Monte Carlo Methods -- 5.3.2 Temporal difference learning methods -- 5.4 Problems -- References -- 6 Deep Learning -- 6.1 Introduction to Deep Learning -- 6.2 Deep Neural Network -- 6.3 Convolutional Neural Network -- 6.4 Recurrent Neural Network -- 6.5 Problems -- References -- Part II AI-Enabled Communications and Networks Techniques for 6G -- 7 6G Wireless Communications and Networks Systems -- 7.1 6G Wireless Communications and Networks.
7.1.1 6G Use Cases and Requirements -- 7.1.2 6G Timeline, Technical Requirements, and Technical Challenges -- 7.1.3 6G Key Enabling Techniques -- 7.2 AI-Enabled 6G Wireless Communications and Networks -- 7.2.1 AI and ML Contributions to Physical Layers -- 7.2.2 AI and ML Contribution to Data Link and Network Layers and Open Research Challenges -- 7.3 Problems -- References -- 8 AI-Enabled Physical Layer -- 8.1 Design Approaches of AI-Enabled Physical Layer -- 8.2 End-To-End Physical Layer Redesign with Autoencoder -- 8.3 Wireless Channel Models -- 8.4 Signal Detection and Modulation -- 8.5 Channel Estimation -- 8.6 Error Control Coding -- 8.7 MIMO -- 8.8 Problems -- References -- 9 AI-Enabled Data Link Layer -- 9.1 Design Approaches of AI-Enabled Data Link Layer -- 9.2 Radio Resource Allocation and Scheduling -- 9.2.1 Resource Allocation Problems in Wireless Networks and Convex Optimization -- 9.2.2 Resource Allocation Models and Performance Measure -- 9.2.3 Utility Functions and Fairness of Resource Allocation -- 9.2.4 Resource Allocation Using AI Techniques -- 9.3 Handover Using AI Techniques -- 9.4 Problems -- References -- 10 AI-Enabled Network Layer -- 10.1 Design Approaches of AI-Enabled Network Layer -- 10.2 Cellular Systems and Networking -- 10.2.1 Evolution of Cellular Networks -- 10.2.2 Concept of Cellular Systems -- 10.2.3 Cell Planning -- 10.3 Network Traffic Prediction -- 10.3.1 Classic Network Traffic Prediction -- 10.3.2 AI-Enabled Network Traffic Prediction -- 10.4 Problems -- References -- Index. |
Record Nr. | UNINA-9910558492703321 |
Kim Haesik | ||
Cham, Switzerland : , : Springer International Publishing, , [2022] | ||
Materiale a stampa | ||
Lo trovi qui: Univ. Federico II | ||
|
Channel-adaptive technologies and cross-layer designs for wireless systems with multiple antennas [[electronic resource] ] : theory and applications / / Vincent K.N. Lau, Yu Kwong Ricky Kwok |
Autore | Lau Vincent K. N |
Pubbl/distr/stampa | Hoboken, N.J., : John Wiley, c2006 |
Descrizione fisica | 1 online resource (544 p.) |
Disciplina |
621.3845/6
621.38456 |
Altri autori (Persone) | KwokYu-Kwong Ricky |
Collana | Wiley series in telecommunications and signal processing |
Soggetto topico | Wireless communication systems - Design and construction |
Soggetto genere / forma | Electronic books. |
ISBN |
1-280-34358-3
9786610343584 0-470-36032-1 0-471-77406-5 0-471-77405-7 |
Formato | Materiale a stampa |
Livello bibliografico | Monografia |
Lingua di pubblicazione | eng |
Nota di contenuto |
Channel-Adaptive Technologies and Cross-Layer Designs for Wireless Systems with Multiple Antennas; CONTENTS; List of Figures; List of Tables; Preface; Acknowledgments; PART 1 THEORY; Chapter 1 Basic Concepts in Wireless Communications; 1.1 Overview; 1.2 Wireless Channel Models; 1.2.1 AWGN Channel Model; 1.2.2 Linear Time-Varying Deterministic Spatial Channel; 1.2.3 The Random Channels; 1.2.4 Frequency-Flat Fading Channels; 1.2.5 Frequency-Selective Fading Channels; 1.3 Equivalence of Continuous-Time and Discrete-Time Models; 1.3.1 Concepts of Signal Space; 1.3.2 Sufficient Statistics
1.3.3 Discrete-Time Signal Model-Flat Fading1.3.4 Discrete-Time Channel Model-Frequency-Selective Fading; 1.4 Fundamentals of Information Theory; 1.4.1 Entropy and Mutual Information; 1.4.2 Shannon's Channel Coding Theorem; 1.4.3 Examples of Channel Capacity; 1.5 Summary; Exercises; Chapter 2 MIMO Link with Perfect Channel State Information; 2.1 Overview; 2.2 Mathematical Model of the MIMO Link; 2.2.1 Probabilistic Channels with States; 2.2.2 General Transmission and CSI Feedback Model; 2.2.3 Adaptive-Channel Encoding and Decoding; 2.2.4 Transmit Power Constraint 2.2.5 Causal Feedback Constraint2.3 Ergodic and Outage Channel Capacity; 2.3.1 Ergodic Capacity; 2.3.2 Outage Capacity; 2.4 Channel Capacity with No CSIT and No CSIR; 2.4.1 Fast Flat Fading MIMO Channels; 2.4.2 Block Fading Channels; 2.5 Channel Capacity with Perfect CSIR; 2.5.1 Block Fading Channels; 2.5.2 Fast Flat Fading MIMO Channels; 2.5.3 Effect of Antenna Correlation on Ergodic MIMO Capacity; 2.5.4 Slow Flat Fading MIMO Channels; 2.6 Channel Capacity with Perfect CSIT Only; 2.6.1 Discrete Block Fading Channels; 2.6.2 Discrete Channel with Three States 2.6.3 Fast Flat Fading MIMO Channels2.6.4 Slow Flat Fading MIMO Channels; 2.7 Channel Capacity with Perfect CSIR and Perfect CSIT; 2.7.1 Fast Flat Fading MIMO Channels; 2.7.2 Slow Flat Fading MIMO Channels; 2.8 Summary; Exercises; Chapter 3 MIMO Link with Imperfect Channel State Information; 3.1 Overview; 3.2 Effect of Imperfect CSI Estimation; 3.2.1 CSI Estimation for MIMO Channels; 3.2.2 Capacity Bounds of MIMO Link; 3.3 Effect of Limited Feedback-Optimizing for SNR; 3.3.1 Introduction to Optimizing Effective SNR; 3.3.2 Grassmannian Line Packing 3.3.3 Grassmannian Precoding for MIMO Systems-Spatial Diversity3.3.4 Grassmannian Precoding for MIMO Systems-Spatial Multiplexing; 3.4 Effect of Limited Feedback-Optimizing for Ergodic Capacity; 3.4.1 Channel Capacity with Partial CSIT; 3.4.2 Coding Theorem with Partial CSIT; 3.4.3 Equivalence with Vector Quantization Problem; 3.4.4 Fast Flat Fading MIMO Channels; 3.4.5 Lloyd's Algorithm; 3.4.6 Approximate Closed-Form Solution for Step 1; 3.4.7 Complexity of the Online Adaptation Strategy; 3.4.8 MMSE-SIC Receiver Structure; 3.4.9 Numerical Results and Discussion; 3.5 Summary; Exercises Chapter 4 Spacetime Coding and Layered Spacetime Coding for MIMO with Perfect Channel State Information |
Record Nr. | UNINA-9910143564603321 |
Lau Vincent K. N | ||
Hoboken, N.J., : John Wiley, c2006 | ||
Materiale a stampa | ||
Lo trovi qui: Univ. Federico II | ||
|
Channel-adaptive technologies and cross-layer designs for wireless systems with multiple antennas [[electronic resource] ] : theory and applications / / Vincent K.N. Lau, Yu Kwong Ricky Kwok |
Autore | Lau Vincent K. N |
Pubbl/distr/stampa | Hoboken, N.J., : John Wiley, c2006 |
Descrizione fisica | 1 online resource (544 p.) |
Disciplina |
621.3845/6
621.38456 |
Altri autori (Persone) | KwokYu-Kwong Ricky |
Collana | Wiley series in telecommunications and signal processing |
Soggetto topico | Wireless communication systems - Design and construction |
ISBN |
1-280-34358-3
9786610343584 0-470-36032-1 0-471-77406-5 0-471-77405-7 |
Formato | Materiale a stampa |
Livello bibliografico | Monografia |
Lingua di pubblicazione | eng |
Nota di contenuto |
Channel-Adaptive Technologies and Cross-Layer Designs for Wireless Systems with Multiple Antennas; CONTENTS; List of Figures; List of Tables; Preface; Acknowledgments; PART 1 THEORY; Chapter 1 Basic Concepts in Wireless Communications; 1.1 Overview; 1.2 Wireless Channel Models; 1.2.1 AWGN Channel Model; 1.2.2 Linear Time-Varying Deterministic Spatial Channel; 1.2.3 The Random Channels; 1.2.4 Frequency-Flat Fading Channels; 1.2.5 Frequency-Selective Fading Channels; 1.3 Equivalence of Continuous-Time and Discrete-Time Models; 1.3.1 Concepts of Signal Space; 1.3.2 Sufficient Statistics
1.3.3 Discrete-Time Signal Model-Flat Fading1.3.4 Discrete-Time Channel Model-Frequency-Selective Fading; 1.4 Fundamentals of Information Theory; 1.4.1 Entropy and Mutual Information; 1.4.2 Shannon's Channel Coding Theorem; 1.4.3 Examples of Channel Capacity; 1.5 Summary; Exercises; Chapter 2 MIMO Link with Perfect Channel State Information; 2.1 Overview; 2.2 Mathematical Model of the MIMO Link; 2.2.1 Probabilistic Channels with States; 2.2.2 General Transmission and CSI Feedback Model; 2.2.3 Adaptive-Channel Encoding and Decoding; 2.2.4 Transmit Power Constraint 2.2.5 Causal Feedback Constraint2.3 Ergodic and Outage Channel Capacity; 2.3.1 Ergodic Capacity; 2.3.2 Outage Capacity; 2.4 Channel Capacity with No CSIT and No CSIR; 2.4.1 Fast Flat Fading MIMO Channels; 2.4.2 Block Fading Channels; 2.5 Channel Capacity with Perfect CSIR; 2.5.1 Block Fading Channels; 2.5.2 Fast Flat Fading MIMO Channels; 2.5.3 Effect of Antenna Correlation on Ergodic MIMO Capacity; 2.5.4 Slow Flat Fading MIMO Channels; 2.6 Channel Capacity with Perfect CSIT Only; 2.6.1 Discrete Block Fading Channels; 2.6.2 Discrete Channel with Three States 2.6.3 Fast Flat Fading MIMO Channels2.6.4 Slow Flat Fading MIMO Channels; 2.7 Channel Capacity with Perfect CSIR and Perfect CSIT; 2.7.1 Fast Flat Fading MIMO Channels; 2.7.2 Slow Flat Fading MIMO Channels; 2.8 Summary; Exercises; Chapter 3 MIMO Link with Imperfect Channel State Information; 3.1 Overview; 3.2 Effect of Imperfect CSI Estimation; 3.2.1 CSI Estimation for MIMO Channels; 3.2.2 Capacity Bounds of MIMO Link; 3.3 Effect of Limited Feedback-Optimizing for SNR; 3.3.1 Introduction to Optimizing Effective SNR; 3.3.2 Grassmannian Line Packing 3.3.3 Grassmannian Precoding for MIMO Systems-Spatial Diversity3.3.4 Grassmannian Precoding for MIMO Systems-Spatial Multiplexing; 3.4 Effect of Limited Feedback-Optimizing for Ergodic Capacity; 3.4.1 Channel Capacity with Partial CSIT; 3.4.2 Coding Theorem with Partial CSIT; 3.4.3 Equivalence with Vector Quantization Problem; 3.4.4 Fast Flat Fading MIMO Channels; 3.4.5 Lloyd's Algorithm; 3.4.6 Approximate Closed-Form Solution for Step 1; 3.4.7 Complexity of the Online Adaptation Strategy; 3.4.8 MMSE-SIC Receiver Structure; 3.4.9 Numerical Results and Discussion; 3.5 Summary; Exercises Chapter 4 Spacetime Coding and Layered Spacetime Coding for MIMO with Perfect Channel State Information |
Record Nr. | UNINA-9910830046903321 |
Lau Vincent K. N | ||
Hoboken, N.J., : John Wiley, c2006 | ||
Materiale a stampa | ||
Lo trovi qui: Univ. Federico II | ||
|
Channel-adaptive technologies and cross-layer designs for wireless systems with multiple antennas : theory and applications / / Vincent K.N. Lau, Yu Kwong Ricky Kwok |
Autore | Lau Vincent K. N |
Pubbl/distr/stampa | Hoboken, N.J., : John Wiley, c2006 |
Descrizione fisica | 1 online resource (544 p.) |
Disciplina | 621.3845/6 |
Altri autori (Persone) | KwokYu-Kwong Ricky |
Collana | Wiley series in telecommunications and signal processing |
Soggetto topico | Wireless communication systems - Design and construction |
ISBN |
1-280-34358-3
9786610343584 0-470-36032-1 0-471-77406-5 0-471-77405-7 |
Formato | Materiale a stampa |
Livello bibliografico | Monografia |
Lingua di pubblicazione | eng |
Nota di contenuto |
Channel-Adaptive Technologies and Cross-Layer Designs for Wireless Systems with Multiple Antennas; CONTENTS; List of Figures; List of Tables; Preface; Acknowledgments; PART 1 THEORY; Chapter 1 Basic Concepts in Wireless Communications; 1.1 Overview; 1.2 Wireless Channel Models; 1.2.1 AWGN Channel Model; 1.2.2 Linear Time-Varying Deterministic Spatial Channel; 1.2.3 The Random Channels; 1.2.4 Frequency-Flat Fading Channels; 1.2.5 Frequency-Selective Fading Channels; 1.3 Equivalence of Continuous-Time and Discrete-Time Models; 1.3.1 Concepts of Signal Space; 1.3.2 Sufficient Statistics
1.3.3 Discrete-Time Signal Model-Flat Fading1.3.4 Discrete-Time Channel Model-Frequency-Selective Fading; 1.4 Fundamentals of Information Theory; 1.4.1 Entropy and Mutual Information; 1.4.2 Shannon's Channel Coding Theorem; 1.4.3 Examples of Channel Capacity; 1.5 Summary; Exercises; Chapter 2 MIMO Link with Perfect Channel State Information; 2.1 Overview; 2.2 Mathematical Model of the MIMO Link; 2.2.1 Probabilistic Channels with States; 2.2.2 General Transmission and CSI Feedback Model; 2.2.3 Adaptive-Channel Encoding and Decoding; 2.2.4 Transmit Power Constraint 2.2.5 Causal Feedback Constraint2.3 Ergodic and Outage Channel Capacity; 2.3.1 Ergodic Capacity; 2.3.2 Outage Capacity; 2.4 Channel Capacity with No CSIT and No CSIR; 2.4.1 Fast Flat Fading MIMO Channels; 2.4.2 Block Fading Channels; 2.5 Channel Capacity with Perfect CSIR; 2.5.1 Block Fading Channels; 2.5.2 Fast Flat Fading MIMO Channels; 2.5.3 Effect of Antenna Correlation on Ergodic MIMO Capacity; 2.5.4 Slow Flat Fading MIMO Channels; 2.6 Channel Capacity with Perfect CSIT Only; 2.6.1 Discrete Block Fading Channels; 2.6.2 Discrete Channel with Three States 2.6.3 Fast Flat Fading MIMO Channels2.6.4 Slow Flat Fading MIMO Channels; 2.7 Channel Capacity with Perfect CSIR and Perfect CSIT; 2.7.1 Fast Flat Fading MIMO Channels; 2.7.2 Slow Flat Fading MIMO Channels; 2.8 Summary; Exercises; Chapter 3 MIMO Link with Imperfect Channel State Information; 3.1 Overview; 3.2 Effect of Imperfect CSI Estimation; 3.2.1 CSI Estimation for MIMO Channels; 3.2.2 Capacity Bounds of MIMO Link; 3.3 Effect of Limited Feedback-Optimizing for SNR; 3.3.1 Introduction to Optimizing Effective SNR; 3.3.2 Grassmannian Line Packing 3.3.3 Grassmannian Precoding for MIMO Systems-Spatial Diversity3.3.4 Grassmannian Precoding for MIMO Systems-Spatial Multiplexing; 3.4 Effect of Limited Feedback-Optimizing for Ergodic Capacity; 3.4.1 Channel Capacity with Partial CSIT; 3.4.2 Coding Theorem with Partial CSIT; 3.4.3 Equivalence with Vector Quantization Problem; 3.4.4 Fast Flat Fading MIMO Channels; 3.4.5 Lloyd's Algorithm; 3.4.6 Approximate Closed-Form Solution for Step 1; 3.4.7 Complexity of the Online Adaptation Strategy; 3.4.8 MMSE-SIC Receiver Structure; 3.4.9 Numerical Results and Discussion; 3.5 Summary; Exercises Chapter 4 Spacetime Coding and Layered Spacetime Coding for MIMO with Perfect Channel State Information |
Record Nr. | UNINA-9910876511103321 |
Lau Vincent K. N | ||
Hoboken, N.J., : John Wiley, c2006 | ||
Materiale a stampa | ||
Lo trovi qui: Univ. Federico II | ||
|