4 papers
Toward Scalable SDN for LEO Mega-Constellations: A Graph Learning Approach
Sivaram Krishnan, Bassel Al Homssi, Zhouyou Gu +3
Terrestrial network limitations drive the integration of non-terrestrial networks (NTNs), notably mega-constellations comprising thousands of low Earth orbit (LEO) satellites. Whil…
Reinforcement Learning for Opportunistic Routing in Software-Defined LEO-Terrestrial Systems
Sivaram Krishnan, Zhouyou Gu, Jihong Park +2
The proliferation of large-scale low Earth orbit (LEO) satellite constellations is driving the need for intelligent routing strategies that can effectively deliver data to terrestr…
Learning Time-Varying Graph Signals via Koopman
Sivaram Krishnan, Jinho Choi, Jihong Park
A wide variety of real-world data, such as sea measurements, e.g., temperatures collected by distributed sensors and multiple unmanned aerial vehicles (UAV) trajectories, can be na…
Koopman-based Prediction of Connectivity for Flying Ad Hoc Networks
Sivaram Krishnan, Jinho Choi, Jihong Park +2
The application of machine learning (ML) to communication systems is expected to play a pivotal role in future artificial intelligence (AI)-based next-generation wireless networks.…