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LEOPath Released: An Open-Source LEO Routing Simulator

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LEOPath logo

We published LEOPath, an open-source, Python-based simulation framework for analyzing and comparing routing algorithms in LEO satellite constellations.

What LEOPath is for #

LEOPath is designed for topology/routing-state generation in time-varying LEO networks. It helps you study how a constellation evolves over time (satellite movement, changing ISLs/GSLs) and how different routing approaches behave.

Some highlights:

  • Realistic constellation modeling using TLEs + SGP4 propagation
  • Dynamic network state (time-varying ISLs and ground-station visibility)
  • Pluggable routing algorithms, including a link-state shortest-path baseline and our 6G-RUPA-inspired topological routing
  • Ground station support and configurable attachment policies
  • Visualization pipeline (Cesium-based 3D views)

What LEOPath is not #

LEOPath intentionally does not simulate a full end-to-end protocol stack or packet-level effects (e.g., TCP behavior, detailed PHY/MAC, queueing). Instead, it focuses on generating forwarding state and network dynamics that you can analyze directly or feed into other tooling.

Quick start #

Install the package:

pip install leopath

Or run simulations via Docker (recommended by the project):

git clone https://github.com/Fundacio-i2CAT/LEOPath.git
cd LEOPath

# Example: run a simulation with a provided config
./run-simulations.sh run -c leopath/config/ether_simple.yaml

# Generate a visualization
./run-simulations.sh visualise -c leopath/config/ether_simple.yaml

Note: the Cesium visualization requires a Cesium Ion access token (see the repository README).

Why this matters for our work #

Our current NTN research needs tooling that is easy to run, easy to extend, and explicit about routing state and control overhead in large constellations. LEOPath is the simulator we use to explore these design points (including topological routing ideas connected to 6G-RUPA).

If you try it out (or want to plug in a new routing algorithm), we would love feedback and contributions via GitHub issues and pull requests.