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

arXiv:2403.13872v1 Announce Type: new
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 …

abstract arxiv challenges communication connectivity cs.lg cs.si decoder dynamic encoder encoder-decoder environments framework future graph graph representation nature network networks paper prediction representation representation learning spatial state temporal type