DSNS: The Deep Space Network Simulator

TL;DR

DSNS addresses the need for scalable, realistic simulation of interplanetary and MEG constellation networks. It introduces an event-driven, modular architecture with an abstracted network stack and a Python interface, enabling fast protocol iteration and large-scale testing. The paper demonstrates LTP integration and CCSDS reference scenarios, showing DSNS outperforms existing tools in scalability and fidelity while remaining suitable for rapid experimentation. This tool has practical impact by enabling protocol development and parameter tuning for a potential interplanetary internet, accelerating innovation in space communications.

Abstract

Simulation tools are commonly used in the development and testing of new protocols or new networks. However, as satellite networks start to grow to encompass thousands of nodes, and as companies and space agencies begin to realize the interplanetary internet, existing satellite and network simulation tools have become impractical for use in this context. We therefore present the Deep Space Network Simulator (DSNS): a new network simulator with a focus on large-scale satellite networks. We demonstrate its improved capabilities compared to existing offerings, showcase its flexibility and extensibility through an implementation of existing protocols and the DTN simulation reference scenarios recommended by CCSDS, and evaluate its scalability, showing that it exceeds existing tools while providing better fidelity. DSNS provides concrete usefulness to both standards bodies and satellite operators, enabling fast iteration on protocol development and testing of parameters under highly realistic conditions. By removing roadblocks to research and innovation, we can accelerate the development of upcoming satellite networks and ensure that their communication is both fast and secure.

Figures (8)

• Figure 1: An interplanetary network simulated in and visualized by DSNS, with communication between Earth and Mars constellations via a relay link.
• Figure 2: Overall structure of DSNS and its high-level operation.
• Figure 3: Sequence diagram for LTP message retransmission. Segment 2 is lost in-transit and retransmitted to guarantee the message reaches its destination.
• Figure 4: A selection of results from the reference scenarios, demonstrating delivery rates, message latency, and link saturation over time.
• Figure 5: Time taken to run the Walker constellation scenario in DSNS as the number of nodes in the constellation increases.