Optimization methods for user admissions and radio resource allocation for multicasting over high altitude platforms /

This book focuses on the issue of optimizing radio resource allocation (RRA) and user admission control (AC) for multiple multicasting sessions on a single high altitude platform (HAP) with multiple antennas on-board. HAPs are quasi-stationary aerial platforms that carry a wireless communications pa...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autores principales: Ibrahim, Ahmed (Autor), Alfa, Attahiru (Autor)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Gistrup, Denmark : River Publishers, [2019]
Colección:River Publishers series in communications.
Temas:
Artificial satellites in telecommunication.
Satellites artificiels dans les télécommunications.
communications satellites.
MEDICAL / Hospital Administration & Care
SCIENCE / Energy
Acceso en línea:Texto completo
Tabla de Contenidos:
  • Front Cover; Half Title Page; RIVER PUBLISHERS SERIES IN COMMUNICATIONS; Title Page
  • Optimization Methods for User Admissions and Radio Resource Allocation for Multicasting over High Altitude Platforms; Copyright Page; Contents; Preface; List of Figures; List of Tables; List of Abbreviations; Chapter 1
  • Introduction; 1.1 An Overview on HAPs; 1.2 Types of HAPs; 1.3 HAP Radio Regulations; 1.4 Recent Research Works in HAPs; Chapter 2
  • Radio Resource Allocation and User Admission Control in HAPs; 2.1 Differences between RRA in HAP Systems and Terrestrial Cellular Systems
  • 2.2 Problem Description, Description and Motivation of the Problem and the Proposed Joint AC-RRA Scheme2.3 Relation between the Research Work Discussed in this Book with the Previous Works; 2.4 Scope and Research Contribution in this Book; Chapter 3
  • Multicasting in a Single HAP System:System Model and Mathe maticalFormulation; 3.1 System Model; 3.2 Key Differences in the Fundamental Equations that Describe E-Prob and P-Prob; 3.3 Formulation of E-Prob; 3.4 Reducing the Formulation to a Mixed Binary Polynomial Constrained Problem
  • 3.5 Reduction of the Formulation to a Mixed Binary Quadratic Constrained Program3.6 Comparison of the Formulation Sizes with the Aid of a Numerical Example; 3.7 Chapter Conclusion; Chapter 4
  • Proposed Solution Method: Branching Schemes and a Presolving Linearization-Based Reformulation; 4.1 A Presolving Linearization for a Particular Quadratic Constraint Set of the Formulation; 4.2 Branch and Bound-Based Solution Framework; 4.3 Branching Techniques; 4.3.1 Random Branching; 4.3.2 Most Infeasible Branching; 4.3.3 Pseudocost Branching; 4.3.4 Strong Branching
  • 4.3.5 Hybrid Strong/Pseudocost Branching4.3.6 Reliability Branching; 4.3.7 Inference Branching; 4.3.8 Cloud Branching; 4.4 Computational Experiments and Results; 4.4.1 Reformulation Linearization at the Presolving Phase; 4.4.2 Branching Schemes; 4.5 Chapter Conclusion; Chapter 5
  • Proposed Solution Method: Cutting Planes, Domain Propagation and Primal Heuristics; 5.1 Cutting Planes and Cut Separation Process; 5.2 Domain Propagation; 5.2.1 Domain Propagation Schemes for QuadraticConstraints; 5.2.2 Domain Propagation Schemes for LinearConstraints; 5.3 Primal Heuristics; 5.3.1 Pseudocost Diving
  • 5.3.2 Clique Partition-Based Large Neighborhood Search Heuristic5.3.3 Undercover Heuristic; 5.4 Computational Experiments; 5.4.1 Results of the Conducted Experiments; 5.5 Chapter Conclusion; Chapter 6
  • Conclusion and Future Work; 6.1 Conclusion; 6.2 Future Work; Bibliography; Index; About the Authors; Back Cover

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