Using Lagrangian Relaxation for Radio Resource Allocation in High Altitude Platforms

In this paper, we study radio resource allocation for multicasting in OFDMA based high altitude platforms (HAPs). We formulate and solve an optimization problem that finds the best allocation of HAP resources such as radio power, sub-channels, and time slots. The problem also finds the best possible...

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Bibliographic Details
Published in:IEEE transactions on wireless communications 2015-10, Vol.14 (10), p.5823-5835
Main Authors: Ibrahim, Ahmed, Alfa, Attahiru S.
Format: Article
Language:English
Subjects:
Algorithms
Continuous Knapsack Problem
High altitude
High Altitude Platforms
Interference
Lagrangian Relaxation
Linear programming
Multicast
Multicasting
OFDM
Optimization
Platforms
Radio
Radio Resource Allocation
Resource allocation
Resource management
Service areas
Signal to noise ratio
Throughput
Wireless communication
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Summary:In this paper, we study radio resource allocation for multicasting in OFDMA based high altitude platforms (HAPs). We formulate and solve an optimization problem that finds the best allocation of HAP resources such as radio power, sub-channels, and time slots. The problem also finds the best possible frequency reuse across the cells that constitute the service area of the HAP. The objective is to maximize the number of user terminals that receive the requested multicast streams in the HAP service area in a given OFDMA frame. A bounding subroutine in a branch and bound algorithm can be obtained by decomposing it into two easier subproblems, due to its high complexity, and solving them iteratively. Subproblem 1 turns out to be a binary integer linear program of no explicitly noticeable structure and therefore Lagrangian relaxation is used to dualize some constraints to get a structure that is easy to solve. Subproblem 2 turns out to be a linear program with a continuous knapsack problem structure. Hence a greedy algorithm is proposed to solve subproblem 2 to optimality. The subgradient method is used to solve for the dual variables in the dual problem to get the tightest bounds.
ISSN:1536-1276
1558-2248
DOI:10.1109/TWC.2015.2443095