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Biblioteca

MARC Lista (tabellare)
5G enabling technologies and wireless networking / / Michael Mackay
5G enabling technologies and wireless networking / / Michael Mackay
Autore Mackay Michael
Pubbl/distr/stampa Basel : , : MDPI - Multidisciplinary Digital Publishing Institute, , [2023]
Descrizione fisica 1 online resource (150 pages)
Disciplina 621.384
Soggetto topico Wireless communication systems
Wireless communication systems - Automatic control
ISBN 3-0365-6807-7
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910683381303321
Mackay Michael  
Basel : , : MDPI - Multidisciplinary Digital Publishing Institute, , [2023]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
5G NR : the next generation wireless access technology / / Erik Dahlman, Stefan Parkvall and Johan Skold
5G NR : the next generation wireless access technology / / Erik Dahlman, Stefan Parkvall and Johan Skold
Autore Dahlman Erik
Edizione [1st edition]
Pubbl/distr/stampa London : , : Academic Press, , [2018]
Descrizione fisica 1 online resource (469 pages)
Disciplina 003.7
Soggetto topico Wireless communication systems - Automatic control
ISBN 0-12-814324-X
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto What is 5G? -- 5G standardization -- Spectrum for 5G -- LTE : an overview -- NR overview -- Radio-interface architecture -- Overall transmission structure -- Channel sounding -- Transport-channel processing -- Physical-layer control signaling -- Multi-antenna transmission -- Beam management -- Retransmission protocols -- Scheduling -- Uplink power and timing control -- Initial access -- LTE/NR interworking and coexistence -- RF characteristics -- RF technologies at mm-wave frequencies -- Beyond the first release of 5G.
Record Nr. UNINA-9910583484403321
Dahlman Erik  
London : , : Academic Press, , [2018]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Broadband communications, computing, and control for ubiquitous intelligence / / Lin Cai, Brian L. Mark, and Jianping Pan, editors
Broadband communications, computing, and control for ubiquitous intelligence / / Lin Cai, Brian L. Mark, and Jianping Pan, editors
Pubbl/distr/stampa Cham, Switzerland : , : Springer International Publishing, , [2022]
Descrizione fisica 1 online resource (353 pages)
Disciplina 621.384
Collana Wireless Networks
Soggetto topico Wireless communication systems - Automatic control
Wireless communication systems
ISBN 3-030-98064-2
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Preface -- Contents -- Contributors -- 1 Tribute to Professor Jon W. Mark -- Personal Stories -- Greeting Messages from Alumni -- Part I Broadband Communications for Ubiquitous Connectivity -- 2 Network Slicing for 5G Networks and Beyond -- 2.1 Introduction to 5G Communication Networks -- 2.2 Network Slicing -- 2.2.1 Network Slicing in 5G Wireless Networks -- 2.2.1.1 Dynamic Radio Resource Slicing Framework -- 2.2.2 Network Slicing in 5G Core Networks -- 2.2.2.1 Joint Computing and Transmission Resource Slicing -- 2.2.3 AI-Assisted Network Slicing in Beyond 5G Networks -- 2.2.3.1 Beyond 5G Networks -- 2.2.3.2 AI-Assisted Network Slicing -- 2.3 Case Study -- 2.4 Conclusion -- References -- 3 Responsive Regulation of Dynamic UAV Communication Networks Based on Deep Reinforcement Learning -- 3.1 Introduction -- 3.2 Related Works -- 3.3 System Model and Problem Formulation -- 3.3.1 Network Environment -- 3.3.2 Spectrum Access -- 3.3.3 Energy-Related Considerations -- 3.3.4 Problem Formulation -- 3.4 Preliminaries -- 3.5 Learning Algorithm Design for Proactive Self-Regulation Strategy -- 3.5.1 State Space -- 3.5.1.1 Case of UAV Quit -- 3.5.1.2 Case of UAV Join-In -- 3.5.2 Action Definition -- 3.5.3 Reward Function Design -- 3.5.4 State Transition Definition -- 3.5.4.1 Case of UAV Quit -- 3.5.4.2 Case of UAV Join-In -- 3.5.5 Training Tune-Ups -- 3.5.5.1 Tune-Ups for Neural Network Training -- 3.5.5.2 Tune-Ups for RL Training -- 3.5.6 Parallel Computing -- 3.6 Proactive Self-Regulation with Dynamic User Distribution -- 3.7 Numerical Results -- 3.7.1 Simulation Setup -- 3.7.2 Simulation Results -- 3.7.2.1 Case Without UAV or User Dynamics -- 3.7.2.2 Case of UAV Quit -- 3.7.2.3 Case of UAV Join-In -- 3.7.2.4 Case of UAV and User Dynamics -- 3.8 Conclusions -- References.
4 Utility-Based Dynamic Resource Allocation in IEEE 802.11ax Networks: A Genetic Algorithm Approach -- 4.1 Introduction -- 4.2 Related Works -- 4.3 Background on OFDMA and RU Allocation in IEEE 802.11ax -- 4.4 System Model -- 4.5 Utility-Based Dynamic Resource Allocation Scheme -- 4.5.1 Optimal Resource Allocation Problem Formulation -- 4.5.2 Genetic Algorithm -- 4.6 Simulation Results -- 4.6.1 UDRA vs. Exhaustive Search -- 4.6.2 Network-Wise Throughputs and Fairness Indexes -- 4.7 Conclusion -- References -- 5 Intelligentized Radio Access Network for Joint Optimization of User Association and Power Allocation -- 5.1 Introduction -- 5.2 Related Work -- 5.3 Main Contribution -- 5.4 System Model -- 5.5 Problem Formulation -- 5.6 DQL Framework -- 5.6.1 DQN -- 5.6.2 Design the DQN -- 5.6.2.1 Actions -- 5.6.2.2 Reward -- 5.7 Results and Discussions -- 5.7.1 Training and Testing Results -- 5.7.2 UE Performance -- 5.7.3 Robustness -- 5.7.4 Scalability -- 5.7.5 Closer Look at DQN -- 5.8 Summary -- References -- 6 Routing Algorithms for Heterogeneous Vehicular Networks -- 6.1 Introduction -- 6.2 Background -- 6.2.1 Unicast Routing Algorithms -- 6.2.2 Broadcast Routing Algorithms -- 6.2.3 Geocast Routing Algorithms -- 6.2.4 Related Work in Routing Algorithms -- 6.3 Machine Learning-Based Routing Algorithm for IoV with Mobility Prediction -- 6.3.1 Network Model -- 6.3.2 Statistical Mobility Model -- 6.3.2.1 Inter-Arrival Time Distribution -- 6.3.2.2 Inter-Vehicle Spacing Distribution -- 6.3.3 Channel Model -- 6.3.4 ANN Model -- 6.4 Performance Evaluation -- 6.5 Conclusion -- References -- 7 Teaching from Home: Computer and Communication Network Perspectives -- 7.1 Introduction -- 7.2 Related Work -- 7.3 Network Technologies Involved -- 7.3.1 Host Computers -- 7.3.1.1 Desktop, Laptop, or Tablet? -- 7.3.1.2 Windows, Mac OS, or Linux?.
7.3.1.3 Other Necessary Peripherals -- 7.3.2 Home Networks -- 7.3.2.1 Ethernet Structured Wiring -- 7.3.2.2 No-New-Wires Home Backbone -- 7.3.2.3 Wireless Home Network -- 7.3.3 Internet Access -- 7.3.3.1 Fiber, Cellular, or Satellite? -- 7.3.3.2 Telephone Service Providers -- 7.3.3.3 Television Service Providers -- 7.4 Improvement for Online Teaching -- 7.4.1 WiFi Interference Avoidance -- 7.4.1.1 A Better (Al)located WiFi AP -- 7.4.1.2 Wired Interconnected WiFi APs -- 7.4.1.3 Wireless Interconnected WiFi APs -- 7.4.2 WAN Reliability Augmentation -- 7.4.2.1 DSL vs. Cable Modem -- 7.4.2.2 Primary vs. Backup -- 7.4.2.3 Load Balancing -- 7.4.3 Recommendations on Teaching from Home -- 7.5 Further Discussion -- 7.6 Conclusions -- References -- Part II Caching, Computing, and Control for Ubiquitous Intelligence -- 8 State Transition Field: A New Framework for Mobile Dynamic Caching -- 8.1 Introduction -- 8.2 State Transition Field -- 8.2.1 Content Request and Replacement -- 8.2.2 Cache State -- 8.2.3 State and Content Caching Probabilities -- 8.2.4 General Cache State Transition Model -- 8.2.5 State Transition Field -- 8.2.6 Discussions on the Steady State and the Convergence -- 8.3 State Transition Field with Time-Varying Content Popularity -- 8.3.1 General Replacement Model -- 8.3.2 Instantaneous STF: The General Case -- 8.3.3 Impact of STF on Instantaneous Cache Hit Probability -- 8.4 Dynamic Probabilistic Caching with Time-Varying Content Popularity -- 8.4.1 The Content Replacement Markov Chain -- 8.4.2 Generating the State Transition Matrix -- 8.4.3 Discussion on Scalability -- 8.5 Numerical Results -- 8.5.1 State Transition Field with Time-Invariant Content Popularity -- 8.5.2 State Transition Field with Time-Varying Content Popularity -- 8.5.3 Dynamic Probabilistic Caching with Time-Varying Content Popularity -- 8.6 Summary -- References.
9 Deep Reinforcement Learning for Mobile EdgeComputing Systems -- 9.1 Introduction -- 9.2 Overview of Deep Reinforcement Learning -- 9.2.1 DRL Problem Formulation -- 9.2.2 Determine the Optimal Policy with Deep Learning -- 9.2.3 Existing DRL Algorithms -- 9.3 Case Study: Deep Q-Learning for Task Offloading in MEC -- 9.3.1 System Model -- 9.3.1.1 Task Model -- 9.3.1.2 Task Offloading Decision -- 9.3.1.3 Local Processing Model -- 9.3.1.4 Edge Node Offloading Model -- 9.3.2 Task Offloading Problem -- 9.3.2.1 State -- 9.3.2.2 Action -- 9.3.2.3 Cost -- 9.3.2.4 Problem Formulation -- 9.3.3 Deep Q-Learning-Based Algorithm -- 9.3.3.1 Neural Network -- 9.3.3.2 Algorithm Design -- 9.3.4 Performance Evaluation -- 9.3.4.1 Algorithm Convergence -- 9.3.4.2 Method Comparison -- 9.4 Challenges and Future Directions -- 9.5 Conclusion -- References -- 10 Mobile Computation Offloading with Hard TaskCompletion Times -- 10.1 Introduction -- 10.2 Continuous Offloading -- 10.2.1 System Description and Problem Formulation -- 10.2.1.1 Local Execution -- 10.2.1.2 Remote Execution -- 10.2.2 Markovian Channel and the Time-Dilated Absorbing Markov Model -- 10.2.3 Offline Bound -- 10.2.4 OnOpt (Online Optimal) Algorithm -- 10.3 Multi-part Offloading -- 10.3.1 Problem Formulation -- 10.3.2 Offline Bound -- 10.3.3 The Time-Dilated Absorbing Markov Model -- 10.3.4 Optimal Algorithm for K-Part Offloading -- 10.4 Numerical Results -- 10.4.1 Simulation Set 1 -- 10.4.2 Simulation Set 2 -- 10.5 Summary -- References -- 11 Online Incentive Mechanism Design in Edge Computing -- 11.1 Introduction -- 11.2 Mechanism Design and Auction -- 11.3 Primal-Dual-Based Online Incentive Mechanism -- 11.3.1 Primal-Dual-Based Method for Linear Systems -- 11.3.2 Primal-Dual-Based Method for Nonlinear Systems -- 11.4 Application of Primal-Dual Online Incentive Mechanism Design in Edge Computing.
11.4.1 System Model Descriptions -- 11.4.1.1 System Model -- 11.4.1.2 Problem Formulation -- 11.4.2 The Design of OMAP -- 11.4.2.1 Problem Reformulation -- 11.4.2.2 OMAP -- 11.4.3 Performance Analyses -- 11.4.4 Numerical Simulations -- 11.5 Summary -- References -- 12 Collaborative Deep Neural Network Inference via Mobile Edge Computing -- 12.1 Introduction -- 12.2 Background -- 12.2.1 DNN Inference -- 12.2.2 Mobile Edge Computing -- 12.2.3 Machine Learning -- 12.3 Collaborative DNN Inference via Device-Edge Orchestration -- 12.3.1 Collaborative DNN Inference Framework -- 12.3.2 Service Delay and Accuracy Analysis of Collaborative DNN Inference -- 12.3.2.1 Inference Delay Analysis -- 12.3.2.2 Inference Accuracy Analysis -- 12.3.3 Joint Sampling Rate Selection and Resource Allocation Problem -- 12.3.3.1 Constrained Markov Decision Process -- 12.3.4 Deep RL-Based Solution -- 12.3.4.1 Markov Decision Process Transformation (Step 1) -- 12.3.4.2 Optimization Subroutine for Resource Allocation (Step 3) -- 12.3.4.3 Deep RL-Based Algorithm (Step 2) -- 12.4 Performance Evaluation -- 12.4.1 Experiment Setup -- 12.4.2 Convergence Performance -- 12.4.3 Impact of Task Arrival Rate -- 12.4.4 Impact of Optimization Subroutine -- 12.5 Conclusion -- References -- 13 Automated Data-Driven System for Compliance Monitoring -- 13.1 Introduction -- 13.1.1 Radio Spectrum Management -- 13.1.2 Spectrum Monitoring for Compliance -- 13.1.3 Chapter Contributions and Organization -- 13.2 Automated Data-driven System -- 13.3 Data Sources -- 13.3.1 Spectrum Measurements -- 13.3.2 Spectrum Management Records -- 13.4 Signal Identification -- 13.4.1 Mode Analysis -- 13.4.2 Mode-Sensor Matching -- 13.4.3 License-Measurements Association -- 13.5 Violation Identification -- 13.5.1 Detecting Violations -- 13.5.2 Characterizing Violations -- 13.5.2.1 Confidence Indicators.
13.5.2.2 Behavioral Indicators.
Record Nr. UNISA-996485669803316
Cham, Switzerland : , : Springer International Publishing, , [2022]
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui
Broadband communications, computing, and control for ubiquitous intelligence / / Lin Cai, Brian L. Mark, and Jianping Pan, editors
Broadband communications, computing, and control for ubiquitous intelligence / / Lin Cai, Brian L. Mark, and Jianping Pan, editors
Pubbl/distr/stampa Cham, Switzerland : , : Springer International Publishing, , [2022]
Descrizione fisica 1 online resource (353 pages)
Disciplina 621.384
Collana Wireless Networks
Soggetto topico Wireless communication systems - Automatic control
Wireless communication systems
ISBN 3-030-98064-2
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Preface -- Contents -- Contributors -- 1 Tribute to Professor Jon W. Mark -- Personal Stories -- Greeting Messages from Alumni -- Part I Broadband Communications for Ubiquitous Connectivity -- 2 Network Slicing for 5G Networks and Beyond -- 2.1 Introduction to 5G Communication Networks -- 2.2 Network Slicing -- 2.2.1 Network Slicing in 5G Wireless Networks -- 2.2.1.1 Dynamic Radio Resource Slicing Framework -- 2.2.2 Network Slicing in 5G Core Networks -- 2.2.2.1 Joint Computing and Transmission Resource Slicing -- 2.2.3 AI-Assisted Network Slicing in Beyond 5G Networks -- 2.2.3.1 Beyond 5G Networks -- 2.2.3.2 AI-Assisted Network Slicing -- 2.3 Case Study -- 2.4 Conclusion -- References -- 3 Responsive Regulation of Dynamic UAV Communication Networks Based on Deep Reinforcement Learning -- 3.1 Introduction -- 3.2 Related Works -- 3.3 System Model and Problem Formulation -- 3.3.1 Network Environment -- 3.3.2 Spectrum Access -- 3.3.3 Energy-Related Considerations -- 3.3.4 Problem Formulation -- 3.4 Preliminaries -- 3.5 Learning Algorithm Design for Proactive Self-Regulation Strategy -- 3.5.1 State Space -- 3.5.1.1 Case of UAV Quit -- 3.5.1.2 Case of UAV Join-In -- 3.5.2 Action Definition -- 3.5.3 Reward Function Design -- 3.5.4 State Transition Definition -- 3.5.4.1 Case of UAV Quit -- 3.5.4.2 Case of UAV Join-In -- 3.5.5 Training Tune-Ups -- 3.5.5.1 Tune-Ups for Neural Network Training -- 3.5.5.2 Tune-Ups for RL Training -- 3.5.6 Parallel Computing -- 3.6 Proactive Self-Regulation with Dynamic User Distribution -- 3.7 Numerical Results -- 3.7.1 Simulation Setup -- 3.7.2 Simulation Results -- 3.7.2.1 Case Without UAV or User Dynamics -- 3.7.2.2 Case of UAV Quit -- 3.7.2.3 Case of UAV Join-In -- 3.7.2.4 Case of UAV and User Dynamics -- 3.8 Conclusions -- References.
4 Utility-Based Dynamic Resource Allocation in IEEE 802.11ax Networks: A Genetic Algorithm Approach -- 4.1 Introduction -- 4.2 Related Works -- 4.3 Background on OFDMA and RU Allocation in IEEE 802.11ax -- 4.4 System Model -- 4.5 Utility-Based Dynamic Resource Allocation Scheme -- 4.5.1 Optimal Resource Allocation Problem Formulation -- 4.5.2 Genetic Algorithm -- 4.6 Simulation Results -- 4.6.1 UDRA vs. Exhaustive Search -- 4.6.2 Network-Wise Throughputs and Fairness Indexes -- 4.7 Conclusion -- References -- 5 Intelligentized Radio Access Network for Joint Optimization of User Association and Power Allocation -- 5.1 Introduction -- 5.2 Related Work -- 5.3 Main Contribution -- 5.4 System Model -- 5.5 Problem Formulation -- 5.6 DQL Framework -- 5.6.1 DQN -- 5.6.2 Design the DQN -- 5.6.2.1 Actions -- 5.6.2.2 Reward -- 5.7 Results and Discussions -- 5.7.1 Training and Testing Results -- 5.7.2 UE Performance -- 5.7.3 Robustness -- 5.7.4 Scalability -- 5.7.5 Closer Look at DQN -- 5.8 Summary -- References -- 6 Routing Algorithms for Heterogeneous Vehicular Networks -- 6.1 Introduction -- 6.2 Background -- 6.2.1 Unicast Routing Algorithms -- 6.2.2 Broadcast Routing Algorithms -- 6.2.3 Geocast Routing Algorithms -- 6.2.4 Related Work in Routing Algorithms -- 6.3 Machine Learning-Based Routing Algorithm for IoV with Mobility Prediction -- 6.3.1 Network Model -- 6.3.2 Statistical Mobility Model -- 6.3.2.1 Inter-Arrival Time Distribution -- 6.3.2.2 Inter-Vehicle Spacing Distribution -- 6.3.3 Channel Model -- 6.3.4 ANN Model -- 6.4 Performance Evaluation -- 6.5 Conclusion -- References -- 7 Teaching from Home: Computer and Communication Network Perspectives -- 7.1 Introduction -- 7.2 Related Work -- 7.3 Network Technologies Involved -- 7.3.1 Host Computers -- 7.3.1.1 Desktop, Laptop, or Tablet? -- 7.3.1.2 Windows, Mac OS, or Linux?.
7.3.1.3 Other Necessary Peripherals -- 7.3.2 Home Networks -- 7.3.2.1 Ethernet Structured Wiring -- 7.3.2.2 No-New-Wires Home Backbone -- 7.3.2.3 Wireless Home Network -- 7.3.3 Internet Access -- 7.3.3.1 Fiber, Cellular, or Satellite? -- 7.3.3.2 Telephone Service Providers -- 7.3.3.3 Television Service Providers -- 7.4 Improvement for Online Teaching -- 7.4.1 WiFi Interference Avoidance -- 7.4.1.1 A Better (Al)located WiFi AP -- 7.4.1.2 Wired Interconnected WiFi APs -- 7.4.1.3 Wireless Interconnected WiFi APs -- 7.4.2 WAN Reliability Augmentation -- 7.4.2.1 DSL vs. Cable Modem -- 7.4.2.2 Primary vs. Backup -- 7.4.2.3 Load Balancing -- 7.4.3 Recommendations on Teaching from Home -- 7.5 Further Discussion -- 7.6 Conclusions -- References -- Part II Caching, Computing, and Control for Ubiquitous Intelligence -- 8 State Transition Field: A New Framework for Mobile Dynamic Caching -- 8.1 Introduction -- 8.2 State Transition Field -- 8.2.1 Content Request and Replacement -- 8.2.2 Cache State -- 8.2.3 State and Content Caching Probabilities -- 8.2.4 General Cache State Transition Model -- 8.2.5 State Transition Field -- 8.2.6 Discussions on the Steady State and the Convergence -- 8.3 State Transition Field with Time-Varying Content Popularity -- 8.3.1 General Replacement Model -- 8.3.2 Instantaneous STF: The General Case -- 8.3.3 Impact of STF on Instantaneous Cache Hit Probability -- 8.4 Dynamic Probabilistic Caching with Time-Varying Content Popularity -- 8.4.1 The Content Replacement Markov Chain -- 8.4.2 Generating the State Transition Matrix -- 8.4.3 Discussion on Scalability -- 8.5 Numerical Results -- 8.5.1 State Transition Field with Time-Invariant Content Popularity -- 8.5.2 State Transition Field with Time-Varying Content Popularity -- 8.5.3 Dynamic Probabilistic Caching with Time-Varying Content Popularity -- 8.6 Summary -- References.
9 Deep Reinforcement Learning for Mobile EdgeComputing Systems -- 9.1 Introduction -- 9.2 Overview of Deep Reinforcement Learning -- 9.2.1 DRL Problem Formulation -- 9.2.2 Determine the Optimal Policy with Deep Learning -- 9.2.3 Existing DRL Algorithms -- 9.3 Case Study: Deep Q-Learning for Task Offloading in MEC -- 9.3.1 System Model -- 9.3.1.1 Task Model -- 9.3.1.2 Task Offloading Decision -- 9.3.1.3 Local Processing Model -- 9.3.1.4 Edge Node Offloading Model -- 9.3.2 Task Offloading Problem -- 9.3.2.1 State -- 9.3.2.2 Action -- 9.3.2.3 Cost -- 9.3.2.4 Problem Formulation -- 9.3.3 Deep Q-Learning-Based Algorithm -- 9.3.3.1 Neural Network -- 9.3.3.2 Algorithm Design -- 9.3.4 Performance Evaluation -- 9.3.4.1 Algorithm Convergence -- 9.3.4.2 Method Comparison -- 9.4 Challenges and Future Directions -- 9.5 Conclusion -- References -- 10 Mobile Computation Offloading with Hard TaskCompletion Times -- 10.1 Introduction -- 10.2 Continuous Offloading -- 10.2.1 System Description and Problem Formulation -- 10.2.1.1 Local Execution -- 10.2.1.2 Remote Execution -- 10.2.2 Markovian Channel and the Time-Dilated Absorbing Markov Model -- 10.2.3 Offline Bound -- 10.2.4 OnOpt (Online Optimal) Algorithm -- 10.3 Multi-part Offloading -- 10.3.1 Problem Formulation -- 10.3.2 Offline Bound -- 10.3.3 The Time-Dilated Absorbing Markov Model -- 10.3.4 Optimal Algorithm for K-Part Offloading -- 10.4 Numerical Results -- 10.4.1 Simulation Set 1 -- 10.4.2 Simulation Set 2 -- 10.5 Summary -- References -- 11 Online Incentive Mechanism Design in Edge Computing -- 11.1 Introduction -- 11.2 Mechanism Design and Auction -- 11.3 Primal-Dual-Based Online Incentive Mechanism -- 11.3.1 Primal-Dual-Based Method for Linear Systems -- 11.3.2 Primal-Dual-Based Method for Nonlinear Systems -- 11.4 Application of Primal-Dual Online Incentive Mechanism Design in Edge Computing.
11.4.1 System Model Descriptions -- 11.4.1.1 System Model -- 11.4.1.2 Problem Formulation -- 11.4.2 The Design of OMAP -- 11.4.2.1 Problem Reformulation -- 11.4.2.2 OMAP -- 11.4.3 Performance Analyses -- 11.4.4 Numerical Simulations -- 11.5 Summary -- References -- 12 Collaborative Deep Neural Network Inference via Mobile Edge Computing -- 12.1 Introduction -- 12.2 Background -- 12.2.1 DNN Inference -- 12.2.2 Mobile Edge Computing -- 12.2.3 Machine Learning -- 12.3 Collaborative DNN Inference via Device-Edge Orchestration -- 12.3.1 Collaborative DNN Inference Framework -- 12.3.2 Service Delay and Accuracy Analysis of Collaborative DNN Inference -- 12.3.2.1 Inference Delay Analysis -- 12.3.2.2 Inference Accuracy Analysis -- 12.3.3 Joint Sampling Rate Selection and Resource Allocation Problem -- 12.3.3.1 Constrained Markov Decision Process -- 12.3.4 Deep RL-Based Solution -- 12.3.4.1 Markov Decision Process Transformation (Step 1) -- 12.3.4.2 Optimization Subroutine for Resource Allocation (Step 3) -- 12.3.4.3 Deep RL-Based Algorithm (Step 2) -- 12.4 Performance Evaluation -- 12.4.1 Experiment Setup -- 12.4.2 Convergence Performance -- 12.4.3 Impact of Task Arrival Rate -- 12.4.4 Impact of Optimization Subroutine -- 12.5 Conclusion -- References -- 13 Automated Data-Driven System for Compliance Monitoring -- 13.1 Introduction -- 13.1.1 Radio Spectrum Management -- 13.1.2 Spectrum Monitoring for Compliance -- 13.1.3 Chapter Contributions and Organization -- 13.2 Automated Data-driven System -- 13.3 Data Sources -- 13.3.1 Spectrum Measurements -- 13.3.2 Spectrum Management Records -- 13.4 Signal Identification -- 13.4.1 Mode Analysis -- 13.4.2 Mode-Sensor Matching -- 13.4.3 License-Measurements Association -- 13.5 Violation Identification -- 13.5.1 Detecting Violations -- 13.5.2 Characterizing Violations -- 13.5.2.1 Confidence Indicators.
13.5.2.2 Behavioral Indicators.
Record Nr. UNINA-9910586599503321
Cham, Switzerland : , : Springer International Publishing, , [2022]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Cooperation and integration in 6G heterogeneous networks : resource allocation and networking / / Jun Du and Chunxiao Jiang
Cooperation and integration in 6G heterogeneous networks : resource allocation and networking / / Jun Du and Chunxiao Jiang
Autore Du Jun
Pubbl/distr/stampa Singapore : , : Springer, , [2022]
Descrizione fisica 1 online resource (461 pages)
Disciplina 004.11
Collana Wireless Networks
Soggetto topico Heterogeneous computing
Wireless communication systems - Automatic control
ISBN 981-19-7648-1
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Foreword -- Contents -- About the Authors -- Part I Introduction -- 1 Introduction of 6G Heterogeneous Networks -- 1.1 Heterogeneous Architecture of 6G Networks -- 1.2 Challenges of Heterogeneous Resource Allocation -- 1.2.1 Heterogeneous Resource Modeling and Performance Evaluation -- 1.2.2 Task Adaptation and Resource Efficiency -- 1.2.3 Interference Control and Secure Communications -- 1.3 Mathematic Tools for Resource Allocation -- 1.3.1 Information Economics Theory -- 1.3.2 Machine Learning and Artificial Intelligence -- References -- Part II Cooperative Transmission in Heterogeneous Networks -- 2 Introduction of Cooperative Transmission in Heterogeneous Networks -- 3 Traffic Offloading in Heterogeneous Networks -- 3.1 Introduction -- 3.2 Architecture of SDWN -- 3.3 Contract Formulation for Traffic Offloading -- 3.3.1 Transmission Model Formulation -- 3.3.2 Economic Models Formulation -- 3.4 Contract Design for Traffic Offloading -- 3.4.1 Contract Design with Information Asymmetry -- 3.4.1.1 Individual Rationality (IR) -- 3.4.1.2 Incentive Compatibility (IC) -- 3.4.2 Contract Design Without Information Asymmetry -- 3.4.3 Contract Design by Linear Pricing -- 3.5 Conditions for Contract Feasibility -- 3.6 Simulation Results -- 3.7 Conclusion -- References -- 4 Cooperative Resource Allocation in Heterogeneous Space-Based Networks -- 4.1 Introduction -- 4.2 Related Works -- 4.3 System Model -- 4.3.1 ON/OFF Model -- 4.3.1.1 ISL Connection Status -- 4.3.1.2 Satellite-Ground Station Link Connection Status -- 4.3.2 Physical Channel Model -- 4.4 Cooperative Resource Allocation Protocol -- 4.4.1 GEO Relay -- 4.4.2 LEO Relay -- 4.5 Stability Analysis -- 4.5.1 GEO Relay -- 4.5.2 LEO Relay -- 4.5.3 Multiple Users Case -- 4.6 Simulation Results -- 4.7 Conclusion -- 4.8 Proof of Lemma 4.1 -- 4.9 Proof of Lemma 4.2 -- References.
Part III Cooperative Transmission in IntegratedSatellite-Terrestrial Networks -- 5 Introduction of Cooperative Transmission in Integrated Satellite-Terrestrial Networks -- 6 Traffic Offloading in Satellite-Terrestrial Networks -- 6.1 Introduction -- 6.2 Related Works -- 6.3 Architecture of SDN -- 6.3.1 Service Plane -- 6.3.2 Control Plane -- 6.3.2.1 Information Collection -- 6.3.2.2 Strategy Distribution -- 6.3.3 Management Plane -- 6.4 System Model of Traffic Offloading in H-STN -- 6.4.1 Fully-Loaded Transmission -- 6.4.2 Satellite's Transmission Rate Through Each Channel -- 6.4.2.1 Transmission Rates Under Interference -- 6.4.2.2 Transmission Rates Under Non-Interference -- 6.4.3 BSs' Cooperative and Competitive Modes -- 6.4.3.1 Cooperative Mode -- 6.4.3.2 Competitive Mode -- 6.5 Second-Price Auction Based Traffic Offloading Mechanism Design -- 6.5.1 Second-Price Auction -- 6.5.2 Auction Operation -- 6.5.3 Outcomes of Auction-Based Traffic Offloading -- 6.6 Satellite's Equilibrium Bidding Strategies -- 6.6.1 Bidding Strategy for ps: [/EMC pdfmark [/objdef Equ /Subtype /Span /ActualText (upper R Subscript thr Baseline element of left parenthesis mu Subscript min Baseline comma mu Subscript max Baseline right bracket) /StPNE pdfmark [/StBMC pdfmarkRthr( μmin,μmax ]ps: [/EMC pdfmark [/StPop pdfmark [/StBMC pdfmark -- 6.6.2 Bidding Strategy for ps: [/EMC pdfmark [/objdef Equ /Subtype /Span /ActualText (upper R Subscript thr Baseline element of left parenthesis mu Subscript max Baseline comma left parenthesis 1 plus StartFraction 1 minus beta Over upper N EndFraction right parenthesis mu Subscript max Baseline right parenthesis) /StPNE pdfmark [/StBMC pdfmarkRthr ( μmax, (1+1-βN)μmax)ps: [/EMC pdfmark [/StPop pdfmark [/StBMC pdfmark.
6.6.3 Bidding Strategy for ps: [/EMC pdfmark [/objdef Equ /Subtype /Span /ActualText (upper R Subscript thr Baseline element of left bracket left parenthesis 1 plus StartFraction 1 minus beta Over upper N EndFraction right parenthesis mu Subscript max Baseline comma plus normal infinity right parenthesis) /StPNE pdfmark [/StBMC pdfmarkRthr[ ( 1+1-βN )μmax,+∞)ps: [/EMC pdfmark [/StPop pdfmark [/StBMC pdfmark -- 6.6.4 Bidding Strategy for ps: [/EMC pdfmark [/objdef Equ /Subtype /Span /ActualText (upper R Subscript thr Baseline element of left bracket 0 comma mu Subscript min Baseline right bracket) /StPNE pdfmark [/StBMC pdfmarkRthr[ 0,μmin ]ps: [/EMC pdfmark [/StPop pdfmark [/StBMC pdfmark -- 6.7 Expected Utility Analysis for MNO -- 6.7.1 Utility Analysis for ps: [/EMC pdfmark [/objdef Equ /Subtype /Span /ActualText (upper R Subscript thr Baseline element of left parenthesis mu Subscript min Baseline comma mu Subscript max Baseline right bracket) /StPNE pdfmark [/StBMC pdfmarkRthr( μmin,μmax ]ps: [/EMC pdfmark [/StPop pdfmark [/StBMC pdfmark -- 6.7.2 Utility Analysis for ps: [/EMC pdfmark [/objdef Equ /Subtype /Span /ActualText (upper R Subscript thr Baseline element of left parenthesis mu Subscript max Baseline comma left parenthesis 1 plus StartFraction 1 minus beta Over upper N EndFraction right parenthesis mu Subscript max Baseline right parenthesis) /StPNE pdfmark [/StBMC pdfmarkRthr ( μmax, (1+1-βN)μmax)ps: [/EMC pdfmark [/StPop pdfmark [/StBMC pdfmark.
6.7.3 Utility Analysis for ps: [/EMC pdfmark [/objdef Equ /Subtype /Span /ActualText (upper R Subscript thr Baseline element of left bracket left parenthesis 1 plus StartFraction 1 minus beta Over upper N EndFraction right parenthesis mu Subscript max Baseline comma plus normal infinity right parenthesis) /StPNE pdfmark [/StBMC pdfmarkRthr[ ( 1+1-βN )μmax,+∞)ps: [/EMC pdfmark [/StPop pdfmark [/StBMC pdfmark -- 6.7.4 Utility Analysis for ps: [/EMC pdfmark [/objdef Equ /Subtype /Span /ActualText (upper R Subscript thr Baseline element of left bracket 0 comma mu Subscript min Baseline right bracket) /StPNE pdfmark [/StBMC pdfmarkRthr[ 0,μmin ]ps: [/EMC pdfmark [/StPop pdfmark [/StBMC pdfmark -- 6.8 Simulation Results -- 6.8.1 Beam Group's Strategy of the Satellite -- 6.8.2 Expected Utility of the MNO -- 6.9 Conclusion -- 6.10 Proof of Lemma 6.1 -- 6.11 Proof of Theorem 6.1 -- 6.11.1 ps: [/EMC pdfmark [/objdef Equ /Subtype /Span /ActualText (mu Subscript n Baseline element of left bracket upper R Subscript thr Baseline comma mu Subscript max Baseline right bracket) /StPNE pdfmark [/StBMC pdfmarkμn[ Rthr,μmax ]ps: [/EMC pdfmark [/StPop pdfmark [/StBMC pdfmark -- 6.11.1.1 Case 1 -- 6.11.1.2 Case 2 -- 6.11.2 ps: [/EMC pdfmark [/objdef Equ /Subtype /Span /ActualText (mu Subscript n Baseline element of left parenthesis ModifyingAbove mu With tilde Subscript a Baseline left parenthesis upper R Subscript thr Baseline right parenthesis comma upper R Subscript thr Baseline right parenthesis) /StPNE pdfmark [/StBMC pdfmarkμn( μ̃a( Rthr ),Rthr )ps: [/EMC pdfmark [/StPop pdfmark [/StBMC pdfmark.
6.11.2.1 ps: [/EMC pdfmark [/objdef Equ /Subtype /Span /ActualText (upper R Subscript thr) /StPNE pdfmark [/StBMC pdfmarkRthrps: [/EMC pdfmark [/StPop pdfmark [/StBMC pdfmark vs ps: [/EMC pdfmark [/objdef Equ /Subtype /Span /ActualText (empty set) /StPNE pdfmark [/StBMC pdfmarkps: [/EMC pdfmark [/StPop pdfmark [/StBMC pdfmark -- 6.11.2.2 ps: [/EMC pdfmark [/objdef Equ /Subtype /Span /ActualText (upper R Subscript thr) /StPNE pdfmark [/StBMC pdfmarkRthrps: [/EMC pdfmark [/StPop pdfmark [/StBMC pdfmark vs ps: [/EMC pdfmark [/objdef Equ /Subtype /Span /ActualText (ModifyingAbove mu With caret element of left bracket upper R Subscript thr Baseline comma plus normal infinity right parenthesis) /StPNE pdfmark [/StBMC pdfmark[ Rthr,+∞)ps: [/EMC pdfmark [/StPop pdfmark [/StBMC pdfmark -- 6.11.3 ps: [/EMC pdfmark [/objdef Equ /Subtype /Span /ActualText (mu Subscript n Baseline equals ModifyingAbove mu With tilde Subscript a Baseline left parenthesis upper R Subscript thr Baseline right parenthesis) /StPNE pdfmark [/StBMC pdfmarkμn=μ̃a( Rthr )ps: [/EMC pdfmark [/StPop pdfmark [/StBMC pdfmark -- 6.11.4 ps: [/EMC pdfmark [/objdef Equ /Subtype /Span /ActualText (mu Subscript n Baseline element of left bracket mu Subscript min Baseline comma ModifyingAbove mu With tilde Subscript a Baseline left parenthesis upper R Subscript thr Baseline right parenthesis right parenthesis) /StPNE pdfmark [/StBMC pdfmarkμn[ μmin,μ̃a( Rthr ) )ps: [/EMC pdfmark [/StPop pdfmark [/StBMC pdfmark -- 6.12 Proof of Theorem 6.3 -- References -- 7 Cooperative Beamforming for Secure Satellite-Terrestrial Transmission -- 7.1 Introduction -- 7.2 Related Works -- 7.2.1 Satellite Terrestrial Networks -- 7.2.2 Physical Layer Security -- 7.3 System Model -- 7.3.1 Channel Model -- 7.3.2 Received Signal Model -- 7.3.3 Signal-to-Interference Plus Noise Ratio -- 7.3.4 Achievable Secrecy Rate.
7.4 Secure Transmission Beamforming Schemes for Satellite Terrestrial Networks.
Record Nr. UNISA-996546843403316
Du Jun  
Singapore : , : Springer, , [2022]
Materiale a stampa
Lo trovi qui: Univ. di Salerno
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Cooperation and integration in 6G heterogeneous networks : resource allocation and networking / / Jun Du and Chunxiao Jiang
Cooperation and integration in 6G heterogeneous networks : resource allocation and networking / / Jun Du and Chunxiao Jiang
Autore Du Jun
Pubbl/distr/stampa Singapore : , : Springer, , [2022]
Descrizione fisica 1 online resource (461 pages)
Disciplina 004.11
Collana Wireless Networks
Soggetto topico Heterogeneous computing
Wireless communication systems - Automatic control
ISBN 981-19-7648-1
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Foreword -- Contents -- About the Authors -- Part I Introduction -- 1 Introduction of 6G Heterogeneous Networks -- 1.1 Heterogeneous Architecture of 6G Networks -- 1.2 Challenges of Heterogeneous Resource Allocation -- 1.2.1 Heterogeneous Resource Modeling and Performance Evaluation -- 1.2.2 Task Adaptation and Resource Efficiency -- 1.2.3 Interference Control and Secure Communications -- 1.3 Mathematic Tools for Resource Allocation -- 1.3.1 Information Economics Theory -- 1.3.2 Machine Learning and Artificial Intelligence -- References -- Part II Cooperative Transmission in Heterogeneous Networks -- 2 Introduction of Cooperative Transmission in Heterogeneous Networks -- 3 Traffic Offloading in Heterogeneous Networks -- 3.1 Introduction -- 3.2 Architecture of SDWN -- 3.3 Contract Formulation for Traffic Offloading -- 3.3.1 Transmission Model Formulation -- 3.3.2 Economic Models Formulation -- 3.4 Contract Design for Traffic Offloading -- 3.4.1 Contract Design with Information Asymmetry -- 3.4.1.1 Individual Rationality (IR) -- 3.4.1.2 Incentive Compatibility (IC) -- 3.4.2 Contract Design Without Information Asymmetry -- 3.4.3 Contract Design by Linear Pricing -- 3.5 Conditions for Contract Feasibility -- 3.6 Simulation Results -- 3.7 Conclusion -- References -- 4 Cooperative Resource Allocation in Heterogeneous Space-Based Networks -- 4.1 Introduction -- 4.2 Related Works -- 4.3 System Model -- 4.3.1 ON/OFF Model -- 4.3.1.1 ISL Connection Status -- 4.3.1.2 Satellite-Ground Station Link Connection Status -- 4.3.2 Physical Channel Model -- 4.4 Cooperative Resource Allocation Protocol -- 4.4.1 GEO Relay -- 4.4.2 LEO Relay -- 4.5 Stability Analysis -- 4.5.1 GEO Relay -- 4.5.2 LEO Relay -- 4.5.3 Multiple Users Case -- 4.6 Simulation Results -- 4.7 Conclusion -- 4.8 Proof of Lemma 4.1 -- 4.9 Proof of Lemma 4.2 -- References.
Part III Cooperative Transmission in IntegratedSatellite-Terrestrial Networks -- 5 Introduction of Cooperative Transmission in Integrated Satellite-Terrestrial Networks -- 6 Traffic Offloading in Satellite-Terrestrial Networks -- 6.1 Introduction -- 6.2 Related Works -- 6.3 Architecture of SDN -- 6.3.1 Service Plane -- 6.3.2 Control Plane -- 6.3.2.1 Information Collection -- 6.3.2.2 Strategy Distribution -- 6.3.3 Management Plane -- 6.4 System Model of Traffic Offloading in H-STN -- 6.4.1 Fully-Loaded Transmission -- 6.4.2 Satellite's Transmission Rate Through Each Channel -- 6.4.2.1 Transmission Rates Under Interference -- 6.4.2.2 Transmission Rates Under Non-Interference -- 6.4.3 BSs' Cooperative and Competitive Modes -- 6.4.3.1 Cooperative Mode -- 6.4.3.2 Competitive Mode -- 6.5 Second-Price Auction Based Traffic Offloading Mechanism Design -- 6.5.1 Second-Price Auction -- 6.5.2 Auction Operation -- 6.5.3 Outcomes of Auction-Based Traffic Offloading -- 6.6 Satellite's Equilibrium Bidding Strategies -- 6.6.1 Bidding Strategy for ps: [/EMC pdfmark [/objdef Equ /Subtype /Span /ActualText (upper R Subscript thr Baseline element of left parenthesis mu Subscript min Baseline comma mu Subscript max Baseline right bracket) /StPNE pdfmark [/StBMC pdfmarkRthr( μmin,μmax ]ps: [/EMC pdfmark [/StPop pdfmark [/StBMC pdfmark -- 6.6.2 Bidding Strategy for ps: [/EMC pdfmark [/objdef Equ /Subtype /Span /ActualText (upper R Subscript thr Baseline element of left parenthesis mu Subscript max Baseline comma left parenthesis 1 plus StartFraction 1 minus beta Over upper N EndFraction right parenthesis mu Subscript max Baseline right parenthesis) /StPNE pdfmark [/StBMC pdfmarkRthr ( μmax, (1+1-βN)μmax)ps: [/EMC pdfmark [/StPop pdfmark [/StBMC pdfmark.
6.6.3 Bidding Strategy for ps: [/EMC pdfmark [/objdef Equ /Subtype /Span /ActualText (upper R Subscript thr Baseline element of left bracket left parenthesis 1 plus StartFraction 1 minus beta Over upper N EndFraction right parenthesis mu Subscript max Baseline comma plus normal infinity right parenthesis) /StPNE pdfmark [/StBMC pdfmarkRthr[ ( 1+1-βN )μmax,+∞)ps: [/EMC pdfmark [/StPop pdfmark [/StBMC pdfmark -- 6.6.4 Bidding Strategy for ps: [/EMC pdfmark [/objdef Equ /Subtype /Span /ActualText (upper R Subscript thr Baseline element of left bracket 0 comma mu Subscript min Baseline right bracket) /StPNE pdfmark [/StBMC pdfmarkRthr[ 0,μmin ]ps: [/EMC pdfmark [/StPop pdfmark [/StBMC pdfmark -- 6.7 Expected Utility Analysis for MNO -- 6.7.1 Utility Analysis for ps: [/EMC pdfmark [/objdef Equ /Subtype /Span /ActualText (upper R Subscript thr Baseline element of left parenthesis mu Subscript min Baseline comma mu Subscript max Baseline right bracket) /StPNE pdfmark [/StBMC pdfmarkRthr( μmin,μmax ]ps: [/EMC pdfmark [/StPop pdfmark [/StBMC pdfmark -- 6.7.2 Utility Analysis for ps: [/EMC pdfmark [/objdef Equ /Subtype /Span /ActualText (upper R Subscript thr Baseline element of left parenthesis mu Subscript max Baseline comma left parenthesis 1 plus StartFraction 1 minus beta Over upper N EndFraction right parenthesis mu Subscript max Baseline right parenthesis) /StPNE pdfmark [/StBMC pdfmarkRthr ( μmax, (1+1-βN)μmax)ps: [/EMC pdfmark [/StPop pdfmark [/StBMC pdfmark.
6.7.3 Utility Analysis for ps: [/EMC pdfmark [/objdef Equ /Subtype /Span /ActualText (upper R Subscript thr Baseline element of left bracket left parenthesis 1 plus StartFraction 1 minus beta Over upper N EndFraction right parenthesis mu Subscript max Baseline comma plus normal infinity right parenthesis) /StPNE pdfmark [/StBMC pdfmarkRthr[ ( 1+1-βN )μmax,+∞)ps: [/EMC pdfmark [/StPop pdfmark [/StBMC pdfmark -- 6.7.4 Utility Analysis for ps: [/EMC pdfmark [/objdef Equ /Subtype /Span /ActualText (upper R Subscript thr Baseline element of left bracket 0 comma mu Subscript min Baseline right bracket) /StPNE pdfmark [/StBMC pdfmarkRthr[ 0,μmin ]ps: [/EMC pdfmark [/StPop pdfmark [/StBMC pdfmark -- 6.8 Simulation Results -- 6.8.1 Beam Group's Strategy of the Satellite -- 6.8.2 Expected Utility of the MNO -- 6.9 Conclusion -- 6.10 Proof of Lemma 6.1 -- 6.11 Proof of Theorem 6.1 -- 6.11.1 ps: [/EMC pdfmark [/objdef Equ /Subtype /Span /ActualText (mu Subscript n Baseline element of left bracket upper R Subscript thr Baseline comma mu Subscript max Baseline right bracket) /StPNE pdfmark [/StBMC pdfmarkμn[ Rthr,μmax ]ps: [/EMC pdfmark [/StPop pdfmark [/StBMC pdfmark -- 6.11.1.1 Case 1 -- 6.11.1.2 Case 2 -- 6.11.2 ps: [/EMC pdfmark [/objdef Equ /Subtype /Span /ActualText (mu Subscript n Baseline element of left parenthesis ModifyingAbove mu With tilde Subscript a Baseline left parenthesis upper R Subscript thr Baseline right parenthesis comma upper R Subscript thr Baseline right parenthesis) /StPNE pdfmark [/StBMC pdfmarkμn( μ̃a( Rthr ),Rthr )ps: [/EMC pdfmark [/StPop pdfmark [/StBMC pdfmark.
6.11.2.1 ps: [/EMC pdfmark [/objdef Equ /Subtype /Span /ActualText (upper R Subscript thr) /StPNE pdfmark [/StBMC pdfmarkRthrps: [/EMC pdfmark [/StPop pdfmark [/StBMC pdfmark vs ps: [/EMC pdfmark [/objdef Equ /Subtype /Span /ActualText (empty set) /StPNE pdfmark [/StBMC pdfmarkps: [/EMC pdfmark [/StPop pdfmark [/StBMC pdfmark -- 6.11.2.2 ps: [/EMC pdfmark [/objdef Equ /Subtype /Span /ActualText (upper R Subscript thr) /StPNE pdfmark [/StBMC pdfmarkRthrps: [/EMC pdfmark [/StPop pdfmark [/StBMC pdfmark vs ps: [/EMC pdfmark [/objdef Equ /Subtype /Span /ActualText (ModifyingAbove mu With caret element of left bracket upper R Subscript thr Baseline comma plus normal infinity right parenthesis) /StPNE pdfmark [/StBMC pdfmark[ Rthr,+∞)ps: [/EMC pdfmark [/StPop pdfmark [/StBMC pdfmark -- 6.11.3 ps: [/EMC pdfmark [/objdef Equ /Subtype /Span /ActualText (mu Subscript n Baseline equals ModifyingAbove mu With tilde Subscript a Baseline left parenthesis upper R Subscript thr Baseline right parenthesis) /StPNE pdfmark [/StBMC pdfmarkμn=μ̃a( Rthr )ps: [/EMC pdfmark [/StPop pdfmark [/StBMC pdfmark -- 6.11.4 ps: [/EMC pdfmark [/objdef Equ /Subtype /Span /ActualText (mu Subscript n Baseline element of left bracket mu Subscript min Baseline comma ModifyingAbove mu With tilde Subscript a Baseline left parenthesis upper R Subscript thr Baseline right parenthesis right parenthesis) /StPNE pdfmark [/StBMC pdfmarkμn[ μmin,μ̃a( Rthr ) )ps: [/EMC pdfmark [/StPop pdfmark [/StBMC pdfmark -- 6.12 Proof of Theorem 6.3 -- References -- 7 Cooperative Beamforming for Secure Satellite-Terrestrial Transmission -- 7.1 Introduction -- 7.2 Related Works -- 7.2.1 Satellite Terrestrial Networks -- 7.2.2 Physical Layer Security -- 7.3 System Model -- 7.3.1 Channel Model -- 7.3.2 Received Signal Model -- 7.3.3 Signal-to-Interference Plus Noise Ratio -- 7.3.4 Achievable Secrecy Rate.
7.4 Secure Transmission Beamforming Schemes for Satellite Terrestrial Networks.
Record Nr. UNINA-9910634042403321
Du Jun  
Singapore : , : Springer, , [2022]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
IEC/IEEE 62704-2:2017 . Part 2 : IEEE/IEC International Standard -- Determining the peak spatial-average specific absorption rate (SAR) in the human body from wireless communications devices, 30 MHz to 6 GHz : Specific requirements for finite difference time domain / / Institute of Electrical and Electronics Engineers
IEC/IEEE 62704-2:2017 . Part 2 : IEEE/IEC International Standard -- Determining the peak spatial-average specific absorption rate (SAR) in the human body from wireless communications devices, 30 MHz to 6 GHz : Specific requirements for finite difference time domain / / Institute of Electrical and Electronics Engineers
Pubbl/distr/stampa Piscataway, NJ : , : IEEE, , 2017
Descrizione fisica 1 online resource
Disciplina 621.384
Soggetto topico Wireless communication systems - Automatic control
ISBN 1-5044-4116-8
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Altri titoli varianti 62704-2-2017 - IEEE/IEC International Standard -- Determining the peak spatial-average specific absorption rate
IEC/IEEE 62704-2
Record Nr. UNINA-9910211248303321
Piscataway, NJ : , : IEEE, , 2017
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
IEC/IEEE 62704-2:2017 . Part 2 : IEEE/IEC International Standard -- Determining the peak spatial-average specific absorption rate (SAR) in the human body from wireless communications devices, 30 MHz to 6 GHz : Specific requirements for finite difference time domain / / Institute of Electrical and Electronics Engineers
IEC/IEEE 62704-2:2017 . Part 2 : IEEE/IEC International Standard -- Determining the peak spatial-average specific absorption rate (SAR) in the human body from wireless communications devices, 30 MHz to 6 GHz : Specific requirements for finite difference time domain / / Institute of Electrical and Electronics Engineers
Pubbl/distr/stampa Piscataway, NJ : , : IEEE, , 2017
Descrizione fisica 1 online resource
Disciplina 621.384
Soggetto topico Wireless communication systems - Automatic control
ISBN 1-5044-4116-8
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Altri titoli varianti 62704-2-2017 - IEEE/IEC International Standard -- Determining the peak spatial-average specific absorption rate
IEC/IEEE 62704-2
Record Nr. UNISA-996280587803316
Piscataway, NJ : , : IEEE, , 2017
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui
IEEE Std C62.55-2017 : IEEE Guide for Surge Protection of DC Power Feeds to Remote Radio Heads / / Institute of Electrical and Electronics Engineers
IEEE Std C62.55-2017 : IEEE Guide for Surge Protection of DC Power Feeds to Remote Radio Heads / / Institute of Electrical and Electronics Engineers
Pubbl/distr/stampa Piscataway, NJ : , : IEEE, , 2017
Descrizione fisica 1 online resource
Disciplina 621.384
Soggetto topico Wireless communication systems - Automatic control
ISBN 1-5044-3800-0
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Altri titoli varianti IEEE Std C62.55-2017
Record Nr. UNISA-996278306103316
Piscataway, NJ : , : IEEE, , 2017
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui
IEEE Std C62.55-2017 : IEEE Guide for Surge Protection of DC Power Feeds to Remote Radio Heads / / Institute of Electrical and Electronics Engineers
IEEE Std C62.55-2017 : IEEE Guide for Surge Protection of DC Power Feeds to Remote Radio Heads / / Institute of Electrical and Electronics Engineers
Pubbl/distr/stampa Piscataway, NJ : , : IEEE, , 2017
Descrizione fisica 1 online resource
Disciplina 621.384
Soggetto topico Wireless communication systems - Automatic control
ISBN 1-5044-3800-0
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Altri titoli varianti IEEE Std C62.55-2017
Record Nr. UNINA-9910213051303321
Piscataway, NJ : , : IEEE, , 2017
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui