Machine learning-based discrete event dynamic surrogate model of communication systems for simulating the command, control, and communication system of systems. (August 2019)

• Record Type: Journal Article
• Authors: Kang, Bong Gu; Seo, Kyung-Min; Kim, Tag Gon
• Is Part Of: Simulation. Volume 95:Number 8(2019)
• Page Start: 673
• Page End: 691
• Publication Date: 2019-08
• Subjects: Command -- control -- and communication -- mobile network -- discrete event dynamic system -- probabilistic discrete event systems specification -- nonlinear autoregressive exogenous model; Computer simulation -- Periodicals; 003.3
• DOI: 10.1177/0037549718809890
• Languages: English
• ISSNs: 0037-5497

Abstract

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.