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E-raamat: Optimization Methods for User Admissions and Radio Resource Allocation for Multicasting over High Altitude Platforms

(University of Pretoria, South Africa and University of Manitoba, Canada), (Memorial University of Newfoundland, Canada)
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Optimization Methods for User Admissions and Radio Resource Allocation for Multicasting over High Altitude PlatformsSuurem pilt
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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 payload to provide wireless communications and broadband services. They are meant to be located in the stratosphere layer of the atmosphere at altitudes in the range 17-22 km and have the ability to fly on demand to temporarily or permanently serve regions with unavailable telecommunications infrastructure.



An important requirement that the book focusses on is the development of an efficient and effective method for resource allocation and user admissions for HAPs, especially when it comes to multicasting. Power, frequency, space (antennas selection) and time (scheduling) are the resources considered in the problem over an orthogonal frequency division multiple access (OFDMA) HAP system.

Due to the strong dependence of the total number of users that could join different multicast groups, on the possible ways we may allocate resources to these groups, it is of significant importance to consider a joint user to session assignments and RRA across the groups. From the service provider's point of view, it would be in its best interest to be able to admit as many higher priority users as possible, while satisfying their quality of service requirements. High priority users could be users subscribed in and paying higher for a service plan that gives them preference of admittance to receive more multicast transmissions, compared to those paying for a lower service plan. Also, the user who tries to join multiple multicast groups (i.e. receive more than one multicast transmission), would have preferences for which one he would favor to receive if resources are not enough to satisfy the QoS requirements.

Technical topics discussed in the book include: - Overview on High Altitude Platforms, their different types and the recent works in this area - Radio Resource Allocation and User Admission Control in HAPs - Multicasting in a Single HAP System: System Model and Mathematical Formulation - Optimization schemes that are designed to enhance the performance of a branch and bound technique by taking into account special mathematical structure in the problem formulation
Preface ix
List of Figures
xiii
List of Tables
xv
List of Abbreviations
xvii
1 Introduction
1(8)
1.1 An Overview on HAPs
2(1)
1.2 Types of HAPs
3(2)
1.3 HAP Radio Regulations
5(1)
1.4 Recent Research Works in HAPs
5(4)
2 Radio Resource Allocation and User Admission Control in HAPs
9(16)
2.1 Differences between RRA in HAP Systems and Terrestrial Cellular Systems
11(2)
2.2 Problem Description, Description and Motivation of the Problem and the Proposed Joint AC-RRA Scheme
13(4)
2.3 Relation between the Research Work Discussed in this Book with the Previous Works
17(2)
2.4 Scope and Research Contribution in this Book
19(6)
3 Multicasting in a Single HAP System: System Model and Mathematical Formulation
25(34)
3.1 System Model
25(6)
3.2 Key Differences in the Fundamental Equations that Describe E-Prob and P-Prob
31(3)
3.3 Formulation of E-Prob
34(7)
3.4 Reducing the Formulation to a Mixed Binary Polynomial Constrained Problem
41(3)
3.5 Reduction of the Formulation to a Mixed Binary Quadratic Constrained Program
44(2)
3.6 Comparison of the Formulation Sizes with the Aid of a Numerical Example
46(10)
3.7
Chapter Conclusion
56(3)
4 Proposed Solution Method: Branching Schemes and a Presolving Linearization-Based Reformulation
59(30)
4.1 A Presolving Linearization for a Particular Quadratic Constraint Set of the Formulation
60(2)
4.2 Branch and Bound-Based Solution Framework
62(3)
4.3 Branching Techniques
65(7)
4.3.1 Random Branching
66(1)
4.3.2 Most Infeasible Branching
66(1)
4.3.3 Pseudocost Branching
67(1)
4.3.4 Strong Branching
67(1)
4.3.5 Hybrid Strong/Pseudocost Branching
68(1)
4.3.6 Reliability Branching
68(1)
4.3.7 Inference Branching
69(1)
4.3.8 Cloud Branching
70(2)
4.4 Computational Experiments and Results
72(15)
4.4.1 Reformulation Linearization at the Presolving Phase
75(6)
4.4.2 Branching Schemes
81(6)
4.5
Chapter Conclusion
87(2)
5 Proposed Solution Method: Cutting Planes, Domain Propagation and Primal Heuristics
89(26)
5.1 Cutting Planes and Cut Separation Process
89(5)
5.2 Domain Propagation
94(4)
5.2.1 Domain Propagation Schemes for Quadratic Constraints
94(1)
5.2.2 Domain Propagation Schemes for Linear Constraints
95(3)
5.3 Primal Heuristics
98(5)
5.3.1 Pseudocost Diving
99(2)
5.3.2 Clique Partition-Based Large Neighborhood Search Heuristic
101(1)
5.3.3 Undercover Heuristic
102(1)
5.4 Computational Experiments
103(9)
5.4.1 Results of the Conducted Experiments
104(8)
5.5
Chapter Conclusion
112(3)
6 Conclusion and Future Work
115(4)
6.1 Conclusion
115(1)
6.2 Future Work
116(3)
Bibliography 119(10)
Index 129(4)
About the Authors 133
Ahmed Ibrahim, Memorial University of Newfoundland, Canada.

Attahiru Alfa, University of Pretoria, South Africa and University of Manitoba, Canada.
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