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SatFlow: Scalable Network Planning for LEO Mega-Constellations

Sheng Cen, Qiying Pan, Yifei Zhu, Bo Li

TL;DR

SatFlow addresses scalable network planning for LEO mega-constellations by jointly optimizing ISL re-establishment, traffic routing, and fine-grained satellite terminal power. It introduces a two-tier framework: SatFlow-L, a distributed Lagrangian-based lower-level optimizer for traffic and power under a fixed topology, and SatFlow-U, a multi-agent reinforcement learning upper-level controller that plans ISL re-establishment over long horizons. The approach leverages temporal graphs, extended monotropic optimization, and asynchronous group-based RL to scale to thousands of satellites, achieving up to $21.0\%$ flow-conservation improvements and up to $89.4\%$ cost reductions over state-of-the-art benchmarks. Experiments on Starlink-like and Kuiper-like constellations demonstrate strong energy efficiency, reduced switching costs, and higher network throughput, indicating practical viability for real-world mega-constellations.

Abstract

Low-earth-orbit (LEO) satellite communication networks have evolved into mega-constellations with hundreds to thousands of satellites inter-connecting with inter-satellite links (ISLs). Network planning, which plans for network resources and architecture to improve the network performance and save operational costs, is crucial for satellite network management. However, due to the large scale of mega-constellations, high dynamics of satellites, and complex distribution of real-world traffic, it is extremely challenging to conduct scalable network planning on mega-constellations with high performance. In this paper, we propose SatFlow, a distributed and hierarchical network planning framework to plan for the network topology, traffic allocation, and fine-grained ISL terminal power allocation for mega-constellations. To tackle the hardness of the original problem, we decompose the grand problem into two hierarchical sub-problems, tackled by two-tier modules. A multi-agent reinforcement learning approach is proposed for the upper-level module so that the overall laser energy consumption and ISL operational costs can be minimized; A distributed alternating step algorithm is proposed for the lower-level module so that the laser energy consumption could be minimized with low time complexity for a given topology. Extensive simulations on various mega-constellations validate SatFlow's scalability on the constellation size, reducing the flow violation ratio by up to 21.0% and reducing the total costs by up to 89.4%, compared with various state-of-the-art benchmarks.

SatFlow: Scalable Network Planning for LEO Mega-Constellations

TL;DR

SatFlow addresses scalable network planning for LEO mega-constellations by jointly optimizing ISL re-establishment, traffic routing, and fine-grained satellite terminal power. It introduces a two-tier framework: SatFlow-L, a distributed Lagrangian-based lower-level optimizer for traffic and power under a fixed topology, and SatFlow-U, a multi-agent reinforcement learning upper-level controller that plans ISL re-establishment over long horizons. The approach leverages temporal graphs, extended monotropic optimization, and asynchronous group-based RL to scale to thousands of satellites, achieving up to flow-conservation improvements and up to cost reductions over state-of-the-art benchmarks. Experiments on Starlink-like and Kuiper-like constellations demonstrate strong energy efficiency, reduced switching costs, and higher network throughput, indicating practical viability for real-world mega-constellations.

Abstract

Low-earth-orbit (LEO) satellite communication networks have evolved into mega-constellations with hundreds to thousands of satellites inter-connecting with inter-satellite links (ISLs). Network planning, which plans for network resources and architecture to improve the network performance and save operational costs, is crucial for satellite network management. However, due to the large scale of mega-constellations, high dynamics of satellites, and complex distribution of real-world traffic, it is extremely challenging to conduct scalable network planning on mega-constellations with high performance. In this paper, we propose SatFlow, a distributed and hierarchical network planning framework to plan for the network topology, traffic allocation, and fine-grained ISL terminal power allocation for mega-constellations. To tackle the hardness of the original problem, we decompose the grand problem into two hierarchical sub-problems, tackled by two-tier modules. A multi-agent reinforcement learning approach is proposed for the upper-level module so that the overall laser energy consumption and ISL operational costs can be minimized; A distributed alternating step algorithm is proposed for the lower-level module so that the laser energy consumption could be minimized with low time complexity for a given topology. Extensive simulations on various mega-constellations validate SatFlow's scalability on the constellation size, reducing the flow violation ratio by up to 21.0% and reducing the total costs by up to 89.4%, compared with various state-of-the-art benchmarks.
Paper Structure (22 sections, 3 theorems, 20 equations, 7 figures, 2 tables, 2 algorithms)

This paper contains 22 sections, 3 theorems, 20 equations, 7 figures, 2 tables, 2 algorithms.

Table of Contents

  1. Introduction
  2. Related Work
  3. Terrestrial Network Planning for Cost Optimization
  4. Network Planning in LEO Mega-constellations
  5. System model and problem formulation
  6. LEO Mega-constellation Architecture and Modeling
  7. Communication Model
  8. ISL Switching Cost Model
  9. Traffic Flow Model
  10. Problem Formulation
  11. Algorithm Design
  12. Lower-level Traffic and Power Allocation Module
  13. Upper-level ISL Re-establishment Module
  14. Performance Evaluation
  15. Experimental Setup
  16. ...and 7 more sections

Key Result

Theorem 1

SatFlow-L converges to the optimal solution $Y^{*}$ of Sub-problem problem_r1.

Figures (7)

  • Figure 1: Data transmission in ISL-enabled LEO constellations.
  • Figure 2: Hierarchical planning framework after the $(s+1)^{th}$ decision for ISL re-establishment. Line boldness for a flow and the number of lightning symbols on an ISL reflect the quantity of reserved data rates and the allocated power, respectively.
  • Figure 3: The upper-level module for ISL re-establishment.
  • Figure 4: Energy consumption comparison of various traffic and power allocation schemes.
  • Figure 5: FVR comparison of various traffic and power allocation schemes.
  • ...and 2 more figures

Theorems & Definitions (3)

  • Theorem 1
  • Theorem 2
  • Theorem 3