Spatial-Temporal Graph Representation Learning for Tactical Networks Future State Prediction

• URL: http://arxiv.org/abs/2403.13872v3
• Date: Sun, 14 Jul 2024 15:59:14 GMT
• Title: Spatial-Temporal Graph Representation Learning for Tactical Networks Future State Prediction
• Authors: Junhua Liu, Justin Albrethsen, Lincoln Goh, David Yau, Kwan Hui Lim,
• Abstract summary: We introduce the Spatial-Temporal Graph-Decoder (STGED) framework for Tactical Communication Networks. STGED hierarchically utilizes graph-based attention mechanism to spatially encode a series of communication network states. We demonstrate that STGED consistently outperforms baseline models by large margins across different time-steps input.
• Score: 2.0517097336236283
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: Resource allocation in tactical ad-hoc networks presents unique challenges due to their dynamic and multi-hop nature. Accurate prediction of future network connectivity is essential for effective resource allocation in such environments. In this paper, we introduce the Spatial-Temporal Graph Encoder-Decoder (STGED) framework for Tactical Communication Networks that leverages both spatial and temporal features of network states to learn latent tactical behaviors effectively. STGED hierarchically utilizes graph-based attention mechanism to spatially encode a series of communication network states, leverages a recurrent neural network to temporally encode the evolution of states, and a fully-connected feed-forward network to decode the connectivity in the future state. Through extensive experiments, we demonstrate that STGED consistently outperforms baseline models by large margins across different time-steps input, achieving an accuracy of up to 99.2\% for the future state prediction task of tactical communication networks.

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