Spatial-Temporal Graph Representation Learning for Tactical Networks
  Future State Prediction
Liu Junhua Albrethsen Justin Goh Lincoln Yau David Lim Kwan Hui
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.