Spatial-Temporal Graph Representation Learning for Tactical Networks Future State Prediction
<|reference_start|>Spatial-Temporal Graph Representation Learning for Tactical Networks Future State Prediction: 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.<|reference_end|>
@article{liu2024spatial-temporal,
    title={Spatial-Temporal Graph Representation Learning for Tactical Networks
  Future State Prediction},
    author={Junhua Liu, Justin Albrethsen, Lincoln Goh, David Yau and Kwan Hui Lim},
    journal={arXiv preprint arXiv:2403.13872},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.13872},
    primaryClass={cs.LG cs.SI}
}