Spatial-Temporal Graph Representation Learning for Tactical Networks Future State Prediction
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arXiv
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| Hauptverfasser: | , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866916321627734016 |
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| author | Liu, Junhua Albrethsen, Justin Goh, Lincoln Yau, David Lim, Kwan Hui |
| author_facet | Liu, Junhua Albrethsen, Justin Goh, Lincoln Yau, David Lim, Kwan Hui |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_13872 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Spatial-Temporal Graph Representation Learning for Tactical Networks Future State Prediction Liu, Junhua Albrethsen, Justin Goh, Lincoln Yau, David Lim, Kwan Hui Machine Learning Social and Information Networks 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. |
| title | Spatial-Temporal Graph Representation Learning for Tactical Networks Future State Prediction |
| topic | Machine Learning Social and Information Networks |
| url | https://arxiv.org/abs/2403.13872 |