1 citations · 1 across the 7 of their papers we have counts for
7 papers
BlockFlex: A Hybrid Resilient Routing Method based on Robust Virtual Overlay for Mega-constellation Networks
Xiangtong Wang
Mega-constellation networks, comprising thousands of interconnected low-Earth-orbit (LEO) satellites, represent a transformative leap in global Internet connectivity. However, thei…
Co-optimizing Topology and Geometry in Mega-Constellation Networks via Structural Motif-Lattice Paradigm
Xiangtong Wang
Mega-constellation networks (MCNs) are currently revolutionizing global internet accessibility by providing ubiquitous connectivity on a planetary scale. While the architectural co…
Monte Carlo Throughput Estimation in Dynamic LEO Satellite Networks
Xiangtong Wang
This study introduces a new framework for analyzing capacity dynamics and throughput performance in Low Earth Orbit satellite networks (LSNs). It focuses on addressing critical gap…
Multi-Protocol Location Forwarding (MPLF) for Space Routing
Xiangtong Wang, Menglong Yang, Songchen Han +1
The structure and routing architecture design is critical for achieving low latency and high capacity in future LEO space networks (SNs). Existing studies mainly focus on topologie…
Space Networking Kit: A Novel Simulation Platform for Emerging LEO Mega-constellations
Xiangtong Wang, Xiaodong Han, Menglong Yang +2
This paper presents SNK, a novel simulation platform designed to evaluate the network performance of constellation systems for global Internet services. SNK offers realtime communi…
Investigating Inter-Satellite Link Spanning Patterns on Networking Performance in Mega-constellations
Xiangtong Wang, Xiaodong Han, Menglong Yang +4
Low Earth orbit (LEO) mega-constellations rely on inter-satellite links (ISLs) to provide global connectivity. We note that in addition to the general constellation parameters, the…
Exploring the Impacts from Datasets to Monocular Depth Estimation (MDE) Models with MineNavi
Xiangtong Wang, Binbin Liang, Menglong Yang +1
Current computer vision tasks based on deep learning require a huge amount of data with annotations for model training or testing, especially in some dense estimation tasks, such a…