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MARC Lista (tabellare)
Heterogeneous Reconfigurable Processors for Real-Time Baseband Processing : From Algorithm to Architecture / / by Chenxin Zhang, Liang Liu, Viktor Öwall
Heterogeneous Reconfigurable Processors for Real-Time Baseband Processing : From Algorithm to Architecture / / by Chenxin Zhang, Liang Liu, Viktor Öwall
Autore Zhang Chenxin
Edizione [1st ed. 2016.]
Pubbl/distr/stampa Cham : , : Springer International Publishing : , : Imprint : Springer, , 2016
Descrizione fisica 1 online resource (203 p.)
Disciplina 620
Soggetto topico Electronic circuits
Microprocessors
Electronics
Microelectronics
Circuits and Systems
Processor Architectures
Electronics and Microelectronics, Instrumentation
ISBN 3-319-24004-8
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Introduction -- Digital Hardware Platforms -- Digital Baseband Processing -- The Reconfigurable Cell Array -- Multi-standard Digital Front-End Processing -- Multi-task MIMO Signal Processing -- Future Multi-user MIMO systems – A Discussion -- Conclusion.-.
Record Nr. UNINA-9910254244803321
Zhang Chenxin  
Cham : , : Springer International Publishing : , : Imprint : Springer, , 2016
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Multimedia sensor networks / / Huadong Ma, Liang Liu, Hong Luo
Multimedia sensor networks / / Huadong Ma, Liang Liu, Hong Luo
Autore Ma Huadong
Pubbl/distr/stampa Singapore : , : Springer, , [2021]
Descrizione fisica 1 online resource (xii, 249 pages) : illustrations
Disciplina 681.2
Collana Advances in Computer Science and Technology
Soggetto topico Sensor networks
Multisensor data fusion
ISBN 981-16-0107-0
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Preface -- Contents -- 1 Introduction to Multimedia Sensor Networks -- 1.1 Basic Concepts -- 1.2 Conceptual Architecture -- 1.2.1 Sensing Layer -- 1.2.2 Transmission Layer -- 1.2.3 Processing Layer -- 1.3 Main Research Topics of Multimedia Sensor Networks -- 2 Directional Sensing Models and Coverage Control -- 2.1 Introduction -- 2.2 Directional Sensor Networks -- 2.2.1 Motivation -- 2.2.2 Coverage Problem with Directional Sensing -- 2.2.2.1 Directional Sensing Model -- 2.2.2.2 Coverage Probability with Directional Sensing Model -- 2.2.3 Coverage Enhancing with Rotatable Directional Sensing Model -- 2.2.3.1 Rotatable Directional Sensing Model -- 2.2.3.2 The Problem of Area Coverage Enhancing -- 2.2.4 Potential Field Based Coverage-Enhancing Method -- 2.2.4.1 Sensing Centroid -- 2.2.4.2 Potential Field Force -- 2.2.4.3 Control Laws -- 2.2.5 Simulation Results -- 2.2.5.1 Case Study -- 2.2.5.2 Performance Evaluation -- 2.3 Three Dimensional Directional Sensor Networks -- 2.3.1 Motivation -- 2.3.2 The 3D Directional Sensing Model -- 2.3.3 Area Coverage-Enhancing Method -- 2.3.3.1 Problem Formulation -- 2.3.3.2 Virtual Force Analysis Based Coverage-Enhancing -- 2.3.3.3 Coverage Optimization Approach -- 2.3.4 Case Study and Performance Evaluations -- 2.4 Directional K-Coverage for Target Recognition -- 2.4.1 Motivation -- 2.4.2 Collaborative Face Orientation Detection -- 2.4.3 Problem Description -- 2.4.3.1 Effective Sensing in Video Surveillance -- 2.4.3.2 Directional K-Coverage (DKC) Problem -- 2.4.4 Analysis of Directional K-Coverage -- 2.4.5 Experimental Results -- 2.5 L-Coverage for Target Localization -- 2.5.1 Motivation -- 2.5.2 Localization-Oriented Sensing Model -- 2.5.3 Bayesian Estimation Based L-Coverage -- 2.5.3.1 L-Coverage Concept -- 2.5.3.2 L-Coverage Illustrations.
2.5.4 L-Coverage Probability in Randomly Deployed Camera Sensor Networks -- 2.5.5 Simulation Experiments -- 2.6 Exposure-Path Prevention for Intrusion Detection in Multimedia Sensor Networks -- 2.6.1 Motivation -- 2.6.2 System Models and Problem Formulation -- 2.6.2.1 Sensors Deploying Model -- 2.6.2.2 Continuum Percolation Model-Based Problem Formulation -- 2.6.3 Bond Percolation Model for Coverage -- 2.6.4 Critical Density for Exposure Path -- 2.6.4.1 Critical Density of Omnidirectional Sensors -- 2.6.4.2 Critical Density of Directional Sensors -- 2.6.5 Dependence Among Neighboring Edges -- 2.6.6 Simulation Evaluations -- 2.6.6.1 Omnidirectional Sensor Networks -- 2.6.6.2 Directional Sensor Networks -- References -- 3 Data Fusion Based Transmission in Multimedia Sensor Networks -- 3.1 Introduction -- 3.2 Adaptive Data Fusion for Energy Efficient Routing in Multimedia Sensor Networks -- 3.2.1 Motivation -- 3.2.2 Measurement of Image Fusion Cost -- 3.2.2.1 Measurement Model for Data Aggregation -- 3.2.2.2 Image Fusion -- 3.2.3 System Model and Problem Formulation -- 3.2.3.1 Network Model -- 3.2.3.2 Problem Formulation -- 3.2.4 Minimum Fusion Steiner Tree -- 3.2.4.1 MFST Algorithm -- 3.2.4.2 3-D Binary Tree Structure -- 3.2.5 Design and Analysis of AFST -- 3.2.5.1 Binary Fusion Steiner Tree (BFST) -- 3.2.5.2 Adaptive Fusion Steiner Tree (AFST) -- 3.2.6 Experimental Study -- 3.2.6.1 Simulation Environment -- 3.2.6.2 Impact of Correlation Coefficient -- 3.2.6.3 Impact of Unit Fusion Cost -- 3.3 Physarum Optimization: A Biology-Inspired Algorithm for the Steiner Tree Problem in Networks -- 3.3.1 Motivation -- 3.3.2 Biology-Inspired Optimization and Physarum Computing -- 3.3.3 Problem Formulation and Physarum Model -- 3.3.3.1 Steiner Tree Problem -- 3.3.3.2 Mathematical Model for Physarum -- 3.3.4 Physarum Optimization for Steiner Tree Problem.
3.3.4.1 Initial Pressures of Vertices -- 3.3.4.2 Main Process of Physarum Optimization -- 3.3.4.3 Convergence of Physarum Optimization -- 3.3.4.4 Algorithms of Physarum Optimization -- 3.4 A Trust-Based Framework for Fault-Tolerant Data Aggregation in Multimedia Sensor Networks -- 3.4.1 Motivation -- 3.4.2 System Model -- 3.4.2.1 Multi-Layer Trustworthy Aggregation Architecture -- 3.4.2.2 Source Model -- 3.4.2.3 Trust Model -- 3.4.3 Trust-Based Framework for Fault-Tolerant Data Aggregation -- 3.4.3.1 Self Data Trust Opinion of Sensor Node -- 3.4.3.2 Peer Node Trust Opinion -- 3.4.3.3 Trust Transfer and Peer Data Trust Opinion -- 3.4.3.4 Trust Combination and Self Data Trust Opinion of Aggregator -- 3.4.3.5 Trust-Based and Fault-Tolerant Data Aggregation Algorithm -- 3.4.4 Experimental and Simulation Studies -- 3.4.4.1 Continuous Audio Stream -- 3.4.4.2 Discrete Data -- References -- 4 In-Network Processing for Multimedia Sensor Networks -- 4.1 Introduction -- 4.2 Correlation Based Image Processing in Multimedia Sensor Networks -- 4.2.1 Motivation -- 4.2.2 Sensing Correlation -- 4.2.3 Image Processing Based on Correlation -- 4.2.3.1 Allocating the Sensing Task -- 4.2.3.2 Image Capturing -- 4.2.3.3 Image Delivering -- 4.2.3.4 Image Fusion -- 4.2.4 Experimetal Results -- 4.3 Dynamic Node Collaboration for Mobile Target Tracking in Multimedia Sensor Networks -- 4.3.1 Motivation -- 4.3.2 Related Works -- 4.3.3 System Models and Description -- 4.3.3.1 Motion Model of the Target -- 4.3.3.2 Sensing Model of Camera Sensors -- 4.3.3.3 Target Tracking by Sequential Monte Carlo Method -- 4.3.3.4 The Dynamic Node Collaboration Scheme -- 4.3.4 Election of the Cluster Heads -- 4.3.5 Selection of the Cluster Members -- 4.3.5.1 Utility Function -- 4.3.5.2 Cost Function -- 4.3.5.3 The Cluster Members Selection Algorithm -- 4.3.6 Simulation Results.
4.4 Distributed Target Classification in Multimedia Sensor Networks -- 4.4.1 Motivation -- 4.4.2 Related Works -- 4.4.3 Procedure of Target Classification in Multimedia Sensor Networks -- 4.4.3.1 Target Detection -- 4.4.3.2 Feature Extraction -- 4.4.3.3 Classification -- 4.4.4 Binary Classification Tree Based Framework -- 4.4.4.1 Generation of the Binary Classification Tree -- 4.4.4.2 Division of the Binary Classification Tree -- 4.4.4.3 Selection of Multimedia Sensor Nodes -- 4.4.5 Case Study and Simulations -- 4.5 Decomposition-Fusion: A Cooperative Computing Mode for Multimedia Sensor Networks -- 4.5.1 Motivation -- 4.5.2 Typical Paradigms of Transmission-Processing for MSNs -- 4.5.3 Decomposition-Fusion Cooperative Computing Framework -- 4.5.3.1 Task Decomposition -- 4.5.3.2 Target Detection -- 4.5.3.3 Selection of Candidates -- 4.5.3.4 Selection of Cooperators -- 4.5.3.5 Interim Results Fusion -- References -- 5 Multimedia Sensor Network Supported IoT Service -- 5.1 Introduction -- 5.2 Searching in IoT -- 5.2.1 Motivation -- 5.2.2 Concept of IoT Search -- 5.2.3 Characters of Searching in IoT -- 5.2.4 Challenges of Searching in IoT -- 5.2.5 The Progressive Search Paradigm -- 5.2.5.1 Coarse-to-Fine Search Strategy -- 5.2.5.2 Near-to-Distant Search Strategy -- 5.2.5.3 Low-to-High Permission Search Strategy -- 5.2.6 Progressive IoT Search in the Multimedia Sensors Based Urban Sensing Network -- 5.3 PROVID: Progressive and Multi-modal Vehicle Re-identification for Large-Scale Urban Surveillance -- 5.3.1 Motivation -- 5.3.2 Related Work -- 5.3.3 Overview of the PROVID Framework -- 5.3.4 Vehicle Filtering by Appearance -- 5.3.4.1 Multi-level Vehicle Representation -- 5.3.4.2 The Null-Space-Based FACT Model -- 5.3.5 License Plate Verification Based on Siamese Neural Network -- 5.3.6 Spatiotemporal Relation-Based Vehicle Re-ranking -- 5.3.7 Applications.
5.3.7.1 Application I: Suspect Vehicle Search -- 5.3.7.2 Application II: Cross-Camera Vehicle Tracking -- 5.3.8 Experiments -- 5.3.8.1 Dataset -- 5.3.8.2 Experimental Settings -- 5.3.8.3 Evaluation of Appearance-Based Vehicle Re-Id -- 5.3.8.4 Evaluation of Plate Verification -- 5.3.8.5 Evaluation of Progressive Vehicle Re-Id -- 5.3.8.6 Time Cost of the PROVID Framework -- References -- 6 Prospect of Future Research -- 6.1 Human-Like Perception -- 6.2 Intelligent Networking and Transmission -- 6.3 Intelligent Services.
Record Nr. UNISA-996464502303316
Ma Huadong  
Singapore : , : Springer, , [2021]
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui
Multimedia sensor networks / / Huadong Ma, Liang Liu, Hong Luo
Multimedia sensor networks / / Huadong Ma, Liang Liu, Hong Luo
Autore Ma Huadong
Pubbl/distr/stampa Singapore : , : Springer, , [2021]
Descrizione fisica 1 online resource (xii, 249 pages) : illustrations
Disciplina 681.2
Collana Advances in Computer Science and Technology
Soggetto topico Sensor networks
Multisensor data fusion
ISBN 981-16-0107-0
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Preface -- Contents -- 1 Introduction to Multimedia Sensor Networks -- 1.1 Basic Concepts -- 1.2 Conceptual Architecture -- 1.2.1 Sensing Layer -- 1.2.2 Transmission Layer -- 1.2.3 Processing Layer -- 1.3 Main Research Topics of Multimedia Sensor Networks -- 2 Directional Sensing Models and Coverage Control -- 2.1 Introduction -- 2.2 Directional Sensor Networks -- 2.2.1 Motivation -- 2.2.2 Coverage Problem with Directional Sensing -- 2.2.2.1 Directional Sensing Model -- 2.2.2.2 Coverage Probability with Directional Sensing Model -- 2.2.3 Coverage Enhancing with Rotatable Directional Sensing Model -- 2.2.3.1 Rotatable Directional Sensing Model -- 2.2.3.2 The Problem of Area Coverage Enhancing -- 2.2.4 Potential Field Based Coverage-Enhancing Method -- 2.2.4.1 Sensing Centroid -- 2.2.4.2 Potential Field Force -- 2.2.4.3 Control Laws -- 2.2.5 Simulation Results -- 2.2.5.1 Case Study -- 2.2.5.2 Performance Evaluation -- 2.3 Three Dimensional Directional Sensor Networks -- 2.3.1 Motivation -- 2.3.2 The 3D Directional Sensing Model -- 2.3.3 Area Coverage-Enhancing Method -- 2.3.3.1 Problem Formulation -- 2.3.3.2 Virtual Force Analysis Based Coverage-Enhancing -- 2.3.3.3 Coverage Optimization Approach -- 2.3.4 Case Study and Performance Evaluations -- 2.4 Directional K-Coverage for Target Recognition -- 2.4.1 Motivation -- 2.4.2 Collaborative Face Orientation Detection -- 2.4.3 Problem Description -- 2.4.3.1 Effective Sensing in Video Surveillance -- 2.4.3.2 Directional K-Coverage (DKC) Problem -- 2.4.4 Analysis of Directional K-Coverage -- 2.4.5 Experimental Results -- 2.5 L-Coverage for Target Localization -- 2.5.1 Motivation -- 2.5.2 Localization-Oriented Sensing Model -- 2.5.3 Bayesian Estimation Based L-Coverage -- 2.5.3.1 L-Coverage Concept -- 2.5.3.2 L-Coverage Illustrations.
2.5.4 L-Coverage Probability in Randomly Deployed Camera Sensor Networks -- 2.5.5 Simulation Experiments -- 2.6 Exposure-Path Prevention for Intrusion Detection in Multimedia Sensor Networks -- 2.6.1 Motivation -- 2.6.2 System Models and Problem Formulation -- 2.6.2.1 Sensors Deploying Model -- 2.6.2.2 Continuum Percolation Model-Based Problem Formulation -- 2.6.3 Bond Percolation Model for Coverage -- 2.6.4 Critical Density for Exposure Path -- 2.6.4.1 Critical Density of Omnidirectional Sensors -- 2.6.4.2 Critical Density of Directional Sensors -- 2.6.5 Dependence Among Neighboring Edges -- 2.6.6 Simulation Evaluations -- 2.6.6.1 Omnidirectional Sensor Networks -- 2.6.6.2 Directional Sensor Networks -- References -- 3 Data Fusion Based Transmission in Multimedia Sensor Networks -- 3.1 Introduction -- 3.2 Adaptive Data Fusion for Energy Efficient Routing in Multimedia Sensor Networks -- 3.2.1 Motivation -- 3.2.2 Measurement of Image Fusion Cost -- 3.2.2.1 Measurement Model for Data Aggregation -- 3.2.2.2 Image Fusion -- 3.2.3 System Model and Problem Formulation -- 3.2.3.1 Network Model -- 3.2.3.2 Problem Formulation -- 3.2.4 Minimum Fusion Steiner Tree -- 3.2.4.1 MFST Algorithm -- 3.2.4.2 3-D Binary Tree Structure -- 3.2.5 Design and Analysis of AFST -- 3.2.5.1 Binary Fusion Steiner Tree (BFST) -- 3.2.5.2 Adaptive Fusion Steiner Tree (AFST) -- 3.2.6 Experimental Study -- 3.2.6.1 Simulation Environment -- 3.2.6.2 Impact of Correlation Coefficient -- 3.2.6.3 Impact of Unit Fusion Cost -- 3.3 Physarum Optimization: A Biology-Inspired Algorithm for the Steiner Tree Problem in Networks -- 3.3.1 Motivation -- 3.3.2 Biology-Inspired Optimization and Physarum Computing -- 3.3.3 Problem Formulation and Physarum Model -- 3.3.3.1 Steiner Tree Problem -- 3.3.3.2 Mathematical Model for Physarum -- 3.3.4 Physarum Optimization for Steiner Tree Problem.
3.3.4.1 Initial Pressures of Vertices -- 3.3.4.2 Main Process of Physarum Optimization -- 3.3.4.3 Convergence of Physarum Optimization -- 3.3.4.4 Algorithms of Physarum Optimization -- 3.4 A Trust-Based Framework for Fault-Tolerant Data Aggregation in Multimedia Sensor Networks -- 3.4.1 Motivation -- 3.4.2 System Model -- 3.4.2.1 Multi-Layer Trustworthy Aggregation Architecture -- 3.4.2.2 Source Model -- 3.4.2.3 Trust Model -- 3.4.3 Trust-Based Framework for Fault-Tolerant Data Aggregation -- 3.4.3.1 Self Data Trust Opinion of Sensor Node -- 3.4.3.2 Peer Node Trust Opinion -- 3.4.3.3 Trust Transfer and Peer Data Trust Opinion -- 3.4.3.4 Trust Combination and Self Data Trust Opinion of Aggregator -- 3.4.3.5 Trust-Based and Fault-Tolerant Data Aggregation Algorithm -- 3.4.4 Experimental and Simulation Studies -- 3.4.4.1 Continuous Audio Stream -- 3.4.4.2 Discrete Data -- References -- 4 In-Network Processing for Multimedia Sensor Networks -- 4.1 Introduction -- 4.2 Correlation Based Image Processing in Multimedia Sensor Networks -- 4.2.1 Motivation -- 4.2.2 Sensing Correlation -- 4.2.3 Image Processing Based on Correlation -- 4.2.3.1 Allocating the Sensing Task -- 4.2.3.2 Image Capturing -- 4.2.3.3 Image Delivering -- 4.2.3.4 Image Fusion -- 4.2.4 Experimetal Results -- 4.3 Dynamic Node Collaboration for Mobile Target Tracking in Multimedia Sensor Networks -- 4.3.1 Motivation -- 4.3.2 Related Works -- 4.3.3 System Models and Description -- 4.3.3.1 Motion Model of the Target -- 4.3.3.2 Sensing Model of Camera Sensors -- 4.3.3.3 Target Tracking by Sequential Monte Carlo Method -- 4.3.3.4 The Dynamic Node Collaboration Scheme -- 4.3.4 Election of the Cluster Heads -- 4.3.5 Selection of the Cluster Members -- 4.3.5.1 Utility Function -- 4.3.5.2 Cost Function -- 4.3.5.3 The Cluster Members Selection Algorithm -- 4.3.6 Simulation Results.
4.4 Distributed Target Classification in Multimedia Sensor Networks -- 4.4.1 Motivation -- 4.4.2 Related Works -- 4.4.3 Procedure of Target Classification in Multimedia Sensor Networks -- 4.4.3.1 Target Detection -- 4.4.3.2 Feature Extraction -- 4.4.3.3 Classification -- 4.4.4 Binary Classification Tree Based Framework -- 4.4.4.1 Generation of the Binary Classification Tree -- 4.4.4.2 Division of the Binary Classification Tree -- 4.4.4.3 Selection of Multimedia Sensor Nodes -- 4.4.5 Case Study and Simulations -- 4.5 Decomposition-Fusion: A Cooperative Computing Mode for Multimedia Sensor Networks -- 4.5.1 Motivation -- 4.5.2 Typical Paradigms of Transmission-Processing for MSNs -- 4.5.3 Decomposition-Fusion Cooperative Computing Framework -- 4.5.3.1 Task Decomposition -- 4.5.3.2 Target Detection -- 4.5.3.3 Selection of Candidates -- 4.5.3.4 Selection of Cooperators -- 4.5.3.5 Interim Results Fusion -- References -- 5 Multimedia Sensor Network Supported IoT Service -- 5.1 Introduction -- 5.2 Searching in IoT -- 5.2.1 Motivation -- 5.2.2 Concept of IoT Search -- 5.2.3 Characters of Searching in IoT -- 5.2.4 Challenges of Searching in IoT -- 5.2.5 The Progressive Search Paradigm -- 5.2.5.1 Coarse-to-Fine Search Strategy -- 5.2.5.2 Near-to-Distant Search Strategy -- 5.2.5.3 Low-to-High Permission Search Strategy -- 5.2.6 Progressive IoT Search in the Multimedia Sensors Based Urban Sensing Network -- 5.3 PROVID: Progressive and Multi-modal Vehicle Re-identification for Large-Scale Urban Surveillance -- 5.3.1 Motivation -- 5.3.2 Related Work -- 5.3.3 Overview of the PROVID Framework -- 5.3.4 Vehicle Filtering by Appearance -- 5.3.4.1 Multi-level Vehicle Representation -- 5.3.4.2 The Null-Space-Based FACT Model -- 5.3.5 License Plate Verification Based on Siamese Neural Network -- 5.3.6 Spatiotemporal Relation-Based Vehicle Re-ranking -- 5.3.7 Applications.
5.3.7.1 Application I: Suspect Vehicle Search -- 5.3.7.2 Application II: Cross-Camera Vehicle Tracking -- 5.3.8 Experiments -- 5.3.8.1 Dataset -- 5.3.8.2 Experimental Settings -- 5.3.8.3 Evaluation of Appearance-Based Vehicle Re-Id -- 5.3.8.4 Evaluation of Plate Verification -- 5.3.8.5 Evaluation of Progressive Vehicle Re-Id -- 5.3.8.6 Time Cost of the PROVID Framework -- References -- 6 Prospect of Future Research -- 6.1 Human-Like Perception -- 6.2 Intelligent Networking and Transmission -- 6.3 Intelligent Services.
Record Nr. UNINA-9910488693803321
Ma Huadong  
Singapore : , : Springer, , [2021]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Next Generation Multiple Access
Next Generation Multiple Access
Autore Liu Yuanwei
Edizione [1st ed.]
Pubbl/distr/stampa Newark : , : John Wiley & Sons, Incorporated, , 2024
Descrizione fisica 1 online resource (624 pages)
Altri autori (Persone) LiuLiang
DingZhiguo
ShenXuemin
ISBN 1-394-18052-7
1-394-18050-0
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Cover -- Title Page -- Copyright -- Contents -- About the Editors -- List of Contributors -- Preface -- Acknowledgments -- Chapter 1 Next Generation Multiple Access Toward 6G -- 1.1 The Road to NGMA -- 1.2 Non‐Orthogonal Multiple Access -- 1.3 Massive Access -- 1.4 Book Outline -- Part I Evolution of NOMA Towards NGMA -- Chapter 2 Modulation Techniques for NGMA/NOMA -- 2.1 Introduction -- 2.2 Space‐Domain IM for NGMA -- 2.2.1 SM‐Based NOMA -- 2.2.1.1 Multi‐RF Schemes -- 2.2.1.2 Single‐RF Schemes -- 2.2.1.3 Recent Developments in SM‐NOMA -- 2.2.2 RSM‐Based NOMA -- 2.2.3 SM‐Aided SCMA -- 2.3 Frequency‐Domain IM for NGMA -- 2.3.1 NOMA with Frequency‐Domain IM -- 2.3.1.1 OFDM‐IM NOMA -- 2.3.1.2 DM‐OFDM NOMA -- 2.3.2 C‐NOMA with Frequency‐Domain IM -- 2.3.2.1 Broadcast Phase -- 2.3.2.2 Cooperative Phase -- 2.4 Code‐Domain IM for NGMA -- 2.4.1 CIM‐SCMA -- 2.4.2 CIM‐MC‐CDMA -- 2.5 Power‐Domain IM for NGMA -- 2.5.1 Transmission Model -- 2.5.1.1 Two‐User Case -- 2.5.1.2 Multiuser Case -- 2.5.2 Signal Decoding -- 2.5.3 Performance Analysis -- 2.6 Summary -- References -- Chapter 3 NOMA Transmission Design with Practical Modulations -- 3.1 Introduction -- 3.2 Fundamentals -- 3.2.1 Multichannel Downlink NOMA -- 3.2.2 Practical Modulations in NOMA -- 3.3 Effective Throughput Analysis -- 3.3.1 Effective Throughput of the Single‐User Channels -- 3.3.2 Effective Throughput of the Two‐User Channels -- 3.4 NOMA Transmission Design -- 3.4.1 Problem Formulation -- 3.4.2 Power Allocation -- 3.4.2.1 Power Allocation within Channels -- 3.4.2.2 Power Budget Allocation Among Channels -- 3.4.3 Joint Resource Allocation -- 3.5 Numerical Results -- 3.6 Conclusion -- References -- Chapter 4 Optimal Resource Allocation for NGMA -- 4.1 Introduction -- 4.2 Single‐Cell Single‐Carrier NOMA -- 4.2.1 Total Power Minimization Problem -- 4.2.2 Sum‐Rate Maximization Problem.
4.2.3 Energy‐Efficiency Maximization Problem -- 4.2.4 Key Features and Implementation Issues -- 4.2.4.1 CSI Insensitivity -- 4.2.4.2 Rate Fairness -- 4.3 Single‐Cell Multicarrier NOMA -- 4.3.1 Total Power Minimization Problem -- 4.3.2 Sum‐Rate Maximization Problem -- 4.3.3 Energy‐Efficiency Maximization Problem -- 4.3.4 Key Features and Implementation Issues -- 4.4 Multi‐cell NOMA with Single‐Cell Processing -- 4.4.1 Dynamic Decoding Order -- 4.4.1.1 Optimal JSPA for Total Power Minimization Problem -- 4.4.1.2 Optimal JSPA for Sum‐Rate Maximization Problem -- 4.4.1.3 Optimal JSPA for EE Maximization Problem -- 4.4.2 Static Decoding Order -- 4.4.2.1 Optimal FRPA for Total Power Minimization Problem -- 4.4.2.2 Optimal FRPA for Sum‐Rate Maximization Problem -- 4.4.2.3 Optimal FRPA for EE Maximization Problem -- 4.4.2.4 Optimal JRPA for Total Power Minimization Problem -- 4.4.2.5 Suboptimal JRPA for Sum‐Rate Maximization Problem -- 4.4.2.6 Suboptimal JRPA for EE Maximization Problem -- 4.5 Numerical Results -- 4.5.1 Approximated Optimal Powers -- 4.5.2 SC‐NOMA versus FDMA-NOMA versus FDMA -- 4.5.3 Multi‐cell NOMA: JSPA versus JRPA versus FRPA -- 4.6 Conclusions -- Acknowledgments -- References -- Chapter 5 Cooperative NOMA -- 5.1 Introduction -- 5.2 System Model for D2MD‐CNOMA -- 5.2.1 System Configuration -- 5.2.2 Channel Model -- 5.3 Adaptive Aggregate Transmission -- 5.3.1 First Phase -- 5.3.2 Second Phase -- 5.4 Performance Analysis -- 5.4.1 Outage Probability -- 5.4.2 Ergodic Sum Capacity -- 5.5 Numerical Results and Discussion -- 5.5.1 Outage Probability -- 5.5.2 Ergodic Sum Capacity -- 5.A.1 Proof of Theorem 5.1 -- References -- Chapter 6 Multi‐scale‐NOMA: An Effective Support to Future Communication-Positioning Integration System -- 6.1 Introduction -- 6.2 Positioning in Cellular Networks -- 6.3 MS‐NOMA Architecture -- 6.4 Interference Analysis.
6.4.1 Single‐Cell Network -- 6.4.1.1 Interference of Positioning to Communication -- 6.4.1.2 Interference of Communication to Positioning -- 6.4.2 Multicell Networks -- 6.4.2.1 Interference of Positioning to Communication -- 6.4.2.2 Interference of Communication to Positioning -- 6.5 Resource Allocation -- 6.5.1 The Constraints -- 6.5.1.1 The BER Threshold Under QoS Constraint -- 6.5.1.2 The Total Power Limitation -- 6.5.1.3 The Elimination of Near‐Far Effect -- 6.5.2 The Proposed Joint Power Allocation Model -- 6.5.3 The Positioning-Communication Joint Power Allocation Scheme -- 6.5.4 Remarks -- 6.6 Performance Evaluation -- 6.6.1 Communication Performance -- 6.6.2 Ranging Performance -- 6.6.3 Resource Consumption of Positioning -- 6.6.3.1 Achievable Positioning Measurement Frequency -- 6.6.3.2 The Resource Element Consumption -- 6.6.3.3 The Power Consumption -- 6.6.4 Positioning Performance -- 6.6.4.1 Comparison by Using CP4A and the Traditional Method -- 6.6.4.2 Comparision Between MS‐NOMA and PRS -- References -- Chapter 7 NOMA‐Aware Wireless Content Caching Networks -- 7.1 Introduction -- 7.2 System Model -- 7.2.1 System Description -- 7.2.2 Content Request Model -- 7.2.3 Random System State -- 7.2.4 System Latency Under Each Random State -- 7.2.5 System's Average Latency -- 7.3 Algorithm Design -- 7.3.1 User Pairing and Power Control Optimization -- 7.3.2 Cache Placement -- 7.3.3 Recommendation Algorithm -- 7.3.4 Joint Optimization Algorithm and Property Analysis -- 7.4 Numerical Simulation -- 7.4.1 Convergence Performance -- 7.4.2 System's Average Latency -- 7.4.3 Cache Hit Ratio -- 7.5 Conclusion -- References -- Chapter 8 NOMA Empowered Multi‐Access Edge Computing and Edge Intelligence -- 8.1 Introduction -- 8.2 Literature Review -- 8.3 System Model and Formulation -- 8.3.1 Modeling of Two‐Sided Dual Offloading.
8.3.2 Overall Latency Minimization -- 8.4 Algorithms for Optimal Offloading -- 8.5 Numerical Results -- 8.6 Conclusion -- Acknowledgments -- References -- Chapter 9 Exploiting Non‐orthogonal Multiple Access in Integrated Sensing and Communications -- 9.1 Introduction -- 9.2 Developing Trends and Fundamental Models of ISAC -- 9.2.1 ISAC: From Orthogonality to Non‐orthogonality -- 9.2.2 Downlink ISAC -- 9.2.3 Uplink ISAC -- 9.3 Novel NOMA Designs in Downlink and Uplink ISAC -- 9.3.1 NOMA‐Empowered Downlink ISAC Design -- 9.3.2 Semi‐NOMA‐Based Uplink ISAC Design -- 9.4 Case Study: System Model and Problem Formulation -- 9.4.1 System Model -- 9.4.1.1 Communication Model -- 9.4.1.2 Sensing Model -- 9.4.2 Problem Formulation -- 9.5 Case Study: Proposed Solutions -- 9.6 Case Study: Numerical Results -- 9.6.1 Convergence of Algorithm 9.1 -- 9.6.2 Baseline -- 9.6.3 Transmit Beampattern -- 9.7 Conclusions -- References -- Part II Massive Access for NGMA -- Chapter 10 Capacity of Many‐Access Channels -- 10.1 Introduction -- 10.2 The Many‐Access Channel Model -- 10.3 Capacity of the MnAC -- 10.3.1 The Equal‐Power Case -- 10.3.2 Heterogeneous Powers and Fading -- 10.4 Energy Efficiency of the MnAC -- 10.4.1 Minimum Energy per Bit for Given PUPE -- 10.4.2 Capacity per Unit‐Energy Under Different Error Criteria -- 10.5 Discussion and Open Problems -- 10.5.1 Scaling Regime -- 10.5.2 Some Practical Issues -- Acknowledgments -- References -- Chapter 11 Random Access Techniques for Machine‐Type Communication -- 11.1 Fundamentals of Random Access -- 11.1.1 Coordinated Versus Uncoordinated Transmissions -- 11.1.2 Random Access Techniques -- 11.1.2.1 ALOHA Protocols -- 11.1.2.2 CSMA -- 11.1.3 Re‐transmission Strategies -- 11.2 A Game Theoretic View -- 11.2.1 A Model -- 11.2.2 Fictitious Play -- 11.3 Random Access Protocols for MTC -- 11.3.1 4‐Step Random Access.
11.3.2 2‐Step Random Access -- 11.3.3 Analysis of 2‐Step Random Access -- 11.3.4 Fast Retrial -- 11.4 Variants of 2‐Step Random Access -- 11.4.1 2‐Step Random Access with MIMO -- 11.4.2 Sequential Transmission of Multiple Preambles -- 11.4.3 Simultaneous Transmission of Multiple Preambles -- 11.4.4 Preambles for Exploration -- 11.5 Application of NOMA to Random Access -- 11.5.1 Power‐Domain NOMA -- 11.5.2 S‐ALOHA with NOMA -- 11.5.3 A Generalization with Multiple Channels -- 11.5.4 NOMA‐ALOHA Game -- 11.6 Low‐Latency Access for MTC -- 11.6.1 Long Propagation Delay -- 11.6.2 Repetition Diversity -- 11.6.3 Channel Coding‐Based Random Access -- References -- Chapter 12 Grant‐Free Random Access via Compressed Sensing: Algorithm and Performance -- 12.1 Introduction -- 12.2 Joint Device Detection, Channel Estimation, and Data Decoding with Collision Resolution for MIMO Massive Unsourced Random Access -- 12.2.1 System Model and Encoding Scheme -- 12.2.1.1 System Model -- 12.2.1.2 Encoding Scheme -- 12.2.2 Collision Resolution Protocol -- 12.2.3 Decoding Scheme -- 12.2.3.1 Joint DAD‐CE Algorithm -- 12.2.3.2 MIMO‐LDPC‐SIC Decoder -- 12.2.4 Experimental Results -- 12.3 Exploiting Angular Domain Sparsity for Grant‐Free Random Access: A Hybrid AMP Approach -- 12.3.1 Sparse Modeling of Massive Access -- 12.3.2 Recovery Algorithm -- 12.3.2.1 Application to Unsourced Random Access -- 12.3.3 Experimental Results -- 12.4 LEO Satellite‐Enabled Grant‐Free Random Access -- 12.4.1 System Model -- 12.4.1.1 Channel Model -- 12.4.1.2 Signal Modulation -- 12.4.1.3 Problem Formulation -- 12.4.2 Pattern Coupled SBL Framework -- 12.4.2.1 The Pattern‐Coupled Hierarchical Prior -- 12.4.2.2 SBL Framework -- 12.4.3 Experimental Results -- 12.5 Concluding Remarks -- Acknowledgments -- References -- Chapter 13 Algorithm Unrolling for Massive Connectivity in IoT Networks.
13.1 Introduction.
Record Nr. UNINA-9910830889403321
Liu Yuanwei  
Newark : , : John Wiley & Sons, Incorporated, , 2024
Materiale a stampa
Lo trovi qui: Univ. Federico II
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Next Generation Multiple Access
Next Generation Multiple Access
Autore Liu Yuanwei
Edizione [1st ed.]
Pubbl/distr/stampa Newark : , : John Wiley & Sons, Incorporated, , 2024
Descrizione fisica 1 online resource (624 pages)
Disciplina 004.62
Altri autori (Persone) LiuLiang
DingZhiguo
ShenXuemin
Soggetto topico 6G mobile communication systems
Multiple access protocols (Computer network protocols)
ISBN 9781394180523
1394180527
9781394180509
1394180500
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Cover -- Title Page -- Copyright -- Contents -- About the Editors -- List of Contributors -- Preface -- Acknowledgments -- Chapter 1 Next Generation Multiple Access Toward 6G -- 1.1 The Road to NGMA -- 1.2 Non‐Orthogonal Multiple Access -- 1.3 Massive Access -- 1.4 Book Outline -- Part I Evolution of NOMA Towards NGMA -- Chapter 2 Modulation Techniques for NGMA/NOMA -- 2.1 Introduction -- 2.2 Space‐Domain IM for NGMA -- 2.2.1 SM‐Based NOMA -- 2.2.1.1 Multi‐RF Schemes -- 2.2.1.2 Single‐RF Schemes -- 2.2.1.3 Recent Developments in SM‐NOMA -- 2.2.2 RSM‐Based NOMA -- 2.2.3 SM‐Aided SCMA -- 2.3 Frequency‐Domain IM for NGMA -- 2.3.1 NOMA with Frequency‐Domain IM -- 2.3.1.1 OFDM‐IM NOMA -- 2.3.1.2 DM‐OFDM NOMA -- 2.3.2 C‐NOMA with Frequency‐Domain IM -- 2.3.2.1 Broadcast Phase -- 2.3.2.2 Cooperative Phase -- 2.4 Code‐Domain IM for NGMA -- 2.4.1 CIM‐SCMA -- 2.4.2 CIM‐MC‐CDMA -- 2.5 Power‐Domain IM for NGMA -- 2.5.1 Transmission Model -- 2.5.1.1 Two‐User Case -- 2.5.1.2 Multiuser Case -- 2.5.2 Signal Decoding -- 2.5.3 Performance Analysis -- 2.6 Summary -- References -- Chapter 3 NOMA Transmission Design with Practical Modulations -- 3.1 Introduction -- 3.2 Fundamentals -- 3.2.1 Multichannel Downlink NOMA -- 3.2.2 Practical Modulations in NOMA -- 3.3 Effective Throughput Analysis -- 3.3.1 Effective Throughput of the Single‐User Channels -- 3.3.2 Effective Throughput of the Two‐User Channels -- 3.4 NOMA Transmission Design -- 3.4.1 Problem Formulation -- 3.4.2 Power Allocation -- 3.4.2.1 Power Allocation within Channels -- 3.4.2.2 Power Budget Allocation Among Channels -- 3.4.3 Joint Resource Allocation -- 3.5 Numerical Results -- 3.6 Conclusion -- References -- Chapter 4 Optimal Resource Allocation for NGMA -- 4.1 Introduction -- 4.2 Single‐Cell Single‐Carrier NOMA -- 4.2.1 Total Power Minimization Problem -- 4.2.2 Sum‐Rate Maximization Problem.
4.2.3 Energy‐Efficiency Maximization Problem -- 4.2.4 Key Features and Implementation Issues -- 4.2.4.1 CSI Insensitivity -- 4.2.4.2 Rate Fairness -- 4.3 Single‐Cell Multicarrier NOMA -- 4.3.1 Total Power Minimization Problem -- 4.3.2 Sum‐Rate Maximization Problem -- 4.3.3 Energy‐Efficiency Maximization Problem -- 4.3.4 Key Features and Implementation Issues -- 4.4 Multi‐cell NOMA with Single‐Cell Processing -- 4.4.1 Dynamic Decoding Order -- 4.4.1.1 Optimal JSPA for Total Power Minimization Problem -- 4.4.1.2 Optimal JSPA for Sum‐Rate Maximization Problem -- 4.4.1.3 Optimal JSPA for EE Maximization Problem -- 4.4.2 Static Decoding Order -- 4.4.2.1 Optimal FRPA for Total Power Minimization Problem -- 4.4.2.2 Optimal FRPA for Sum‐Rate Maximization Problem -- 4.4.2.3 Optimal FRPA for EE Maximization Problem -- 4.4.2.4 Optimal JRPA for Total Power Minimization Problem -- 4.4.2.5 Suboptimal JRPA for Sum‐Rate Maximization Problem -- 4.4.2.6 Suboptimal JRPA for EE Maximization Problem -- 4.5 Numerical Results -- 4.5.1 Approximated Optimal Powers -- 4.5.2 SC‐NOMA versus FDMA-NOMA versus FDMA -- 4.5.3 Multi‐cell NOMA: JSPA versus JRPA versus FRPA -- 4.6 Conclusions -- Acknowledgments -- References -- Chapter 5 Cooperative NOMA -- 5.1 Introduction -- 5.2 System Model for D2MD‐CNOMA -- 5.2.1 System Configuration -- 5.2.2 Channel Model -- 5.3 Adaptive Aggregate Transmission -- 5.3.1 First Phase -- 5.3.2 Second Phase -- 5.4 Performance Analysis -- 5.4.1 Outage Probability -- 5.4.2 Ergodic Sum Capacity -- 5.5 Numerical Results and Discussion -- 5.5.1 Outage Probability -- 5.5.2 Ergodic Sum Capacity -- 5.A.1 Proof of Theorem 5.1 -- References -- Chapter 6 Multi‐scale‐NOMA: An Effective Support to Future Communication-Positioning Integration System -- 6.1 Introduction -- 6.2 Positioning in Cellular Networks -- 6.3 MS‐NOMA Architecture -- 6.4 Interference Analysis.
6.4.1 Single‐Cell Network -- 6.4.1.1 Interference of Positioning to Communication -- 6.4.1.2 Interference of Communication to Positioning -- 6.4.2 Multicell Networks -- 6.4.2.1 Interference of Positioning to Communication -- 6.4.2.2 Interference of Communication to Positioning -- 6.5 Resource Allocation -- 6.5.1 The Constraints -- 6.5.1.1 The BER Threshold Under QoS Constraint -- 6.5.1.2 The Total Power Limitation -- 6.5.1.3 The Elimination of Near‐Far Effect -- 6.5.2 The Proposed Joint Power Allocation Model -- 6.5.3 The Positioning-Communication Joint Power Allocation Scheme -- 6.5.4 Remarks -- 6.6 Performance Evaluation -- 6.6.1 Communication Performance -- 6.6.2 Ranging Performance -- 6.6.3 Resource Consumption of Positioning -- 6.6.3.1 Achievable Positioning Measurement Frequency -- 6.6.3.2 The Resource Element Consumption -- 6.6.3.3 The Power Consumption -- 6.6.4 Positioning Performance -- 6.6.4.1 Comparison by Using CP4A and the Traditional Method -- 6.6.4.2 Comparision Between MS‐NOMA and PRS -- References -- Chapter 7 NOMA‐Aware Wireless Content Caching Networks -- 7.1 Introduction -- 7.2 System Model -- 7.2.1 System Description -- 7.2.2 Content Request Model -- 7.2.3 Random System State -- 7.2.4 System Latency Under Each Random State -- 7.2.5 System's Average Latency -- 7.3 Algorithm Design -- 7.3.1 User Pairing and Power Control Optimization -- 7.3.2 Cache Placement -- 7.3.3 Recommendation Algorithm -- 7.3.4 Joint Optimization Algorithm and Property Analysis -- 7.4 Numerical Simulation -- 7.4.1 Convergence Performance -- 7.4.2 System's Average Latency -- 7.4.3 Cache Hit Ratio -- 7.5 Conclusion -- References -- Chapter 8 NOMA Empowered Multi‐Access Edge Computing and Edge Intelligence -- 8.1 Introduction -- 8.2 Literature Review -- 8.3 System Model and Formulation -- 8.3.1 Modeling of Two‐Sided Dual Offloading.
8.3.2 Overall Latency Minimization -- 8.4 Algorithms for Optimal Offloading -- 8.5 Numerical Results -- 8.6 Conclusion -- Acknowledgments -- References -- Chapter 9 Exploiting Non‐orthogonal Multiple Access in Integrated Sensing and Communications -- 9.1 Introduction -- 9.2 Developing Trends and Fundamental Models of ISAC -- 9.2.1 ISAC: From Orthogonality to Non‐orthogonality -- 9.2.2 Downlink ISAC -- 9.2.3 Uplink ISAC -- 9.3 Novel NOMA Designs in Downlink and Uplink ISAC -- 9.3.1 NOMA‐Empowered Downlink ISAC Design -- 9.3.2 Semi‐NOMA‐Based Uplink ISAC Design -- 9.4 Case Study: System Model and Problem Formulation -- 9.4.1 System Model -- 9.4.1.1 Communication Model -- 9.4.1.2 Sensing Model -- 9.4.2 Problem Formulation -- 9.5 Case Study: Proposed Solutions -- 9.6 Case Study: Numerical Results -- 9.6.1 Convergence of Algorithm 9.1 -- 9.6.2 Baseline -- 9.6.3 Transmit Beampattern -- 9.7 Conclusions -- References -- Part II Massive Access for NGMA -- Chapter 10 Capacity of Many‐Access Channels -- 10.1 Introduction -- 10.2 The Many‐Access Channel Model -- 10.3 Capacity of the MnAC -- 10.3.1 The Equal‐Power Case -- 10.3.2 Heterogeneous Powers and Fading -- 10.4 Energy Efficiency of the MnAC -- 10.4.1 Minimum Energy per Bit for Given PUPE -- 10.4.2 Capacity per Unit‐Energy Under Different Error Criteria -- 10.5 Discussion and Open Problems -- 10.5.1 Scaling Regime -- 10.5.2 Some Practical Issues -- Acknowledgments -- References -- Chapter 11 Random Access Techniques for Machine‐Type Communication -- 11.1 Fundamentals of Random Access -- 11.1.1 Coordinated Versus Uncoordinated Transmissions -- 11.1.2 Random Access Techniques -- 11.1.2.1 ALOHA Protocols -- 11.1.2.2 CSMA -- 11.1.3 Re‐transmission Strategies -- 11.2 A Game Theoretic View -- 11.2.1 A Model -- 11.2.2 Fictitious Play -- 11.3 Random Access Protocols for MTC -- 11.3.1 4‐Step Random Access.
11.3.2 2‐Step Random Access -- 11.3.3 Analysis of 2‐Step Random Access -- 11.3.4 Fast Retrial -- 11.4 Variants of 2‐Step Random Access -- 11.4.1 2‐Step Random Access with MIMO -- 11.4.2 Sequential Transmission of Multiple Preambles -- 11.4.3 Simultaneous Transmission of Multiple Preambles -- 11.4.4 Preambles for Exploration -- 11.5 Application of NOMA to Random Access -- 11.5.1 Power‐Domain NOMA -- 11.5.2 S‐ALOHA with NOMA -- 11.5.3 A Generalization with Multiple Channels -- 11.5.4 NOMA‐ALOHA Game -- 11.6 Low‐Latency Access for MTC -- 11.6.1 Long Propagation Delay -- 11.6.2 Repetition Diversity -- 11.6.3 Channel Coding‐Based Random Access -- References -- Chapter 12 Grant‐Free Random Access via Compressed Sensing: Algorithm and Performance -- 12.1 Introduction -- 12.2 Joint Device Detection, Channel Estimation, and Data Decoding with Collision Resolution for MIMO Massive Unsourced Random Access -- 12.2.1 System Model and Encoding Scheme -- 12.2.1.1 System Model -- 12.2.1.2 Encoding Scheme -- 12.2.2 Collision Resolution Protocol -- 12.2.3 Decoding Scheme -- 12.2.3.1 Joint DAD‐CE Algorithm -- 12.2.3.2 MIMO‐LDPC‐SIC Decoder -- 12.2.4 Experimental Results -- 12.3 Exploiting Angular Domain Sparsity for Grant‐Free Random Access: A Hybrid AMP Approach -- 12.3.1 Sparse Modeling of Massive Access -- 12.3.2 Recovery Algorithm -- 12.3.2.1 Application to Unsourced Random Access -- 12.3.3 Experimental Results -- 12.4 LEO Satellite‐Enabled Grant‐Free Random Access -- 12.4.1 System Model -- 12.4.1.1 Channel Model -- 12.4.1.2 Signal Modulation -- 12.4.1.3 Problem Formulation -- 12.4.2 Pattern Coupled SBL Framework -- 12.4.2.1 The Pattern‐Coupled Hierarchical Prior -- 12.4.2.2 SBL Framework -- 12.4.3 Experimental Results -- 12.5 Concluding Remarks -- Acknowledgments -- References -- Chapter 13 Algorithm Unrolling for Massive Connectivity in IoT Networks.
13.1 Introduction.
Record Nr. UNINA-9911020068103321
Liu Yuanwei  
Newark : , : John Wiley & Sons, Incorporated, , 2024
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui