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Area of Science:

  • Spacecraft Engineering
  • Communication Networks
  • Operations Research

Background:

  • Nanosatellites in Low Earth Orbit (LEO) offer versatile platforms for various missions.
  • Limited downlink capacity due to bandwidth and low ground station duty cycles (often <5%) restricts data throughput.
  • Maximizing "good-put" (unique messages successfully delivered) is crucial in cooperative nanosatellite networks.

Purpose of the Study:

  • To develop and evaluate novel algorithms for scheduling nanosatellite passes.
  • To maximize good-put in heterogeneous cooperative nanosatellite networks under variable conditions.
  • To enhance throughput, fairness, and resilience in nanosatellite communication systems.

Main Methods:

  • Cooperative Reception Algorithm: Utilizes Shapley value analysis to prioritize passes based on estimated station contribution (signal quality, geography, history).
  • Pair-utility optimization: Refines assignments via pairwise comparisons of reception probabilities between neighboring stations.
  • Weighted bidding algorithm: Allocates passes based on a price-per-message model to maximize expected rewards.

Main Results:

  • All three proposed algorithms significantly outperform conventional scheduling strategies.
  • The Shapley-based algorithm demonstrated the largest improvements in good-put.
  • The algorithms effectively handle realistic traffic and link variability.

Conclusions:

  • The developed algorithms provide a practical toolkit for optimizing nanosatellite communication.
  • These methods improve data throughput, network fairness, and system resilience.
  • The findings are applicable to both commercial and non-commercial networks like SatNOGS and TinyGS.