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.

通过使用空间 - 时间图编码器 - 解码器 (STGED) 框架，本研究针对战术通信网络中的资源分配问题提供了准确预测未来网络连通性的解决方案，通过使用网络状态的空间和时间特征，学习潜在的战术行为，实验结果表明 STGED 在不同时间步输入下始终优于基线模型，对战术通信网络未来状态预测任务的准确率达到了高达 99.2%。