Tactical Network

Tactical networks are dynamic, multi-hop communication systems facing unique challenges in resource allocation and congestion control. Current research focuses on developing advanced predictive models, such as graph neural networks and reinforcement learning algorithms, to optimize network performance in unpredictable environments. These efforts aim to improve the efficiency and reliability of tactical communication, with applications ranging from military operations to emergency response scenarios where robust and adaptable networks are crucial.

Papers

March 20, 2024

Spatial-Temporal Graph Representation Learning for Tactical Networks Future State Prediction
Liu Junhua, Albrethsen Justin, Goh Lincoln, Yau David, Lim Kwan Hui
Temporal Graph Learning Fully Connected Graph Attention Mechanism Tactical Network

June 27, 2023

Learning to Sail Dynamic Networks: The MARLIN Reinforcement Learning Framework for Congestion Control in Tactical Environments
Raffaele Galliera, Mattia Zaccarini, Alessandro Morelli, Roberto Fronteddu, Filippo Poltronieri, Niranjan Suri, Mauro Tortonesi
Reinforcement Learning LeArning Abstract Dynamic Network Congestion Control Reinforcement Learning Framework Tactical Network

Tactical Network

Papers

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

Learning to Sail Dynamic Networks: The MARLIN Reinforcement Learning Framework for Congestion Control in Tactical Environments