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Latency-Aware Optimization of Submarine Communication Cable Systems With Trunk-and-Branch Topologies

We provide an optimized design solution, called Lagrangian Fast Marching (LAFM), for the most popular submarine cable system topology (trunk-and-branch), on the undersea surface of the earth modeled by a triangulated 2D manifold in a 3D Euclidean space. Design optimization is formulated as a Steiner minimal tree problem, where each Steiner node models a branching unit (BU). Our objective is to minimize the total cost of the cable system, including both actual cable laying and BU costs as well as a measure of risk associated with location and topography. We minimize this total cost while imposing latency constraints limiting the length of cable between specified pairs of nodes. As most BUs in practice are Y-shaped, Steiner nodes are assumed to have three branches, in accord with the theory of Steiner trees in the Euclidean plane. This paper discusses methodological ideas related to the general problem and provides two algorithms, LAFM-I and LAFM-II, to solve the constrained optimization problem. We have proved that LAFM-I finds the optimal solution for cable systems with one latency constraint. We also show that LAFM-II provides a solution with provable bounds for problems with multiple latency constraints. We find optimal solutions (zero gap between the bounds) for examples with two and four latency constraints. We also demonstrate the superiority of our LAFM method over a simulated annealing (SA) based algorithm, and demonstrate the applicability of LAFM-I, LAFM-II and SA to realistic scenarios with real-world data.

Ketersediaan

Informasi Detail

Judul Seri

JOURNAL OF LIGHTWAVE TECHNOLOGY; Vol.40 No.17 September 1 2022

No. Panggil

-

Penerbit

: .,

Deskripsi Fisik

p. 5825-5841

Bahasa

English

ISBN/ISSN

-

Klasifikasi

NONE

Tipe Isi

-

Tipe Media

-

Tipe Pembawa

-

Edisi

-

Subjek

OPTIMIZATION
LAGRANGIAN
LATENCY CONSTRAINTS
BRANCHING UNITS
COMMUNICATION CABLE SYSTEMS
STEINER MINIMAL TREE
SUBMARINE MANIFOLD

Info Detail Spesifik

DOI: 10.1109/JLT.2022.3186545

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Tianjiao Wang, Zengfu Wang, Bill Moran, Xinyu Wang, Chao Guo, Moshe Zukerman

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