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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)
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-9910877331603321
Liu Yuanwei  
Newark : , : John Wiley & Sons, Incorporated, , 2024
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui