Machine learning-based discrete event dynamic surrogate model of communication systems for simulating the command, control, and communication system of systems

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Command and control (C2) and communication are at the heart of successful military operations in network-centric warfare. Interoperable simulation of a C2 system model and a communication (C) system model may be employed to interactively analyze their detailed behaviors. However, such simulation would be inefficient in simulation time for analysis of combat effectiveness of the C2 model against possible input combinations while considering the communication effect in combat operations. This study proposes a discrete event dynamic surrogate model (DEDSM) for the C model, which would be integrated with the C2 model and simulated. The proposed integrated simulation reduces execution time markedly in analysis of combat effectiveness without sacrificing the accuracy reflecting the communication effect. We hypothesize the DEDSM as a probabilistic priority queuing model whose semantics is expressed in a discrete event systems specification model with some characteristic functions unknown. The unknown functions are identified by machine learning with a data set generated by interoperable simulation of the C2 and C models. The case study with the command, control, and communication system of systems first validates the proposed approach through an equivalence test between the interoperable simulation and the proposed one. It then compares the simulation execution times and the number of events exchanged between the two simulations.
Publisher
SAGE PUBLICATIONS LTD
Issue Date
2019-08
Language
English
Article Type
Article
Citation

SIMULATION-TRANSACTIONS OF THE SOCIETY FOR MODELING AND SIMULATION INTERNATIONAL, v.95, no.8, pp.673 - 691

ISSN
0037-5497
DOI
10.1177/0037549718809890
URI
http://hdl.handle.net/10203/263869
Appears in Collection
EE-Journal Papers(저널논문)
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