Capacity reservation optimization for distributed satellite cluster networks. (10th July 2018)

• Record Type: Journal Article
• Authors: Zheng, Yongxing; Zhao, Shanghong; Li, Yongjun; Tan, Qinggui; Xin, Ning; Tian, Kai; Shi, Lei
• Is Part Of: International journal of satellite communications and networking. Volume 37:Number 3(2019)
• Page Start: 127
• Page End: 137
• Publication Date: 2018-07-10
• Subjects: alternating direction method of multipliers -- convex optimization -- distributed satellite cluster -- heuristic algorithm; Artificial satellites in telecommunication -- Periodicals; Digital communications -- Periodicals; 621.3825
• DOI: 10.1002/sat.1262
• Languages: English
• ISSNs: 1542-0973

Abstract

Summary: We investigate the problem of link capacity reservation in distributed satellite cluster networks. In the optimization problem, the objective is to minimize the link reservation cost under the link capacity constraints. The original optimization problem can be formulated as a set of the linear programs. For the small‐scale networks, the general linear program solvers have the capability to solve the problem within an acceptable time. To solve the problem in the large‐scale network, we propose a heuristic method on the basis of the alternating direction method of multipliers, in which the original problem is decoupled to parallel small problems that can be solved simply. In the heuristic, the upper and lower bounds are computed iteratively. When the upper and lower bounds are close enough, then the optimal value can be obtained approximately. The simulation results show that the proposed method is comparatively exact when the stopping criterion is properly chosen. Abstract : We propose an effective method to solve the problem of link capacity reservation in distributed satellite cluster networks based on the ADMM theory and convex optimization method. We decouple the original problem to the parallel smaller problems that can be solved simply and adopt a heuristic scheme to solve them. The approximate optimal solution can be obtained and the computational complexity can be greatly reduced by our method when the stopping criterion is properly chosen.