# ASTRA: ADMM-Accelerated Topology Reconfiguration for Dynamic Satellite Constellations

arXiv: https://arxiv.org/abs/2609.33993 · HTML: https://mlsanity.com/paper/2609.33993/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 20 of its 53 references that are in the map.
- First posted on arXiv 27 Sep 2026.

## Builds on (most cited first, 20 of 20)

- [Convex optimization](https://mlsanity.com/paper/2106.01946/) (2021)
- [Introduction to Online Convex Optimization](https://mlsanity.com/paper/1909.05207/) (2019)
- [Online Learning: A Modern Introduction Using Convex Optimization](https://mlsanity.com/paper/1912.13213/) (2019)
- [Adaptive Online Learning in Dynamic Environments](https://mlsanity.com/paper/1810.10815/) (2018)
- [Dynamic Regret of Convex and Smooth Functions](https://mlsanity.com/paper/2007.03479/) (2020)
- [Faster Projection-free Online Learning](https://mlsanity.com/paper/2001.11568/) (2020)
- [Adaptive Regret for Control of Time-Varying Dynamics](https://mlsanity.com/paper/2007.04393/) (2020)
- [Online Learning with Inexact Proximal Online Gradient Descent Algorithms](https://mlsanity.com/paper/1806.00202/) (2018)
- [Inter-Plane Inter-Satellite Connectivity in Dense LEO Constellations](https://mlsanity.com/paper/2005.07965/) (2020)
- [Boosting for Online Convex Optimization](https://mlsanity.com/paper/2102.09305/) (2021)
- [Convergence Rates with Inexact Non-expansive Operators](https://mlsanity.com/paper/1404.4837/) (2014)
- [Dynamic Routing for Integrated Satellite-Terrestrial Networks: A Constrained Multi-Agent Reinforcement Learning Approach](https://mlsanity.com/paper/2401.09455/) (2023)
- [Rate-Optimal Online Convex Optimization in Adaptive Linear Control](https://mlsanity.com/paper/2206.01426/) (2022)
- [Projection-Free Online Convex Optimization via Efficient Newton Iterations](https://mlsanity.com/paper/2306.11121/) (2023)
- [Laser Inter-Satellite Link Setup Delay: Quantification, Impact, and Tolerable Value](https://mlsanity.com/paper/2301.05285/) (2023)
- [Time-Dependent Network Topology Optimization for LEO Satellite Constellations](https://mlsanity.com/paper/2501.13280/) (2025)
- [Resilience of Mega-Satellite Constellations: How Node Failures Impact Inter-Satellite Networking Over Time?](https://mlsanity.com/paper/2509.06766/) (2025)
- [Online convex optimization for constrained control of nonlinear systems](https://mlsanity.com/paper/2412.00922/) (2024)
- [LEO Topology Design Under Real-World Deployment Constraints](https://mlsanity.com/paper/2602.07756/) (2026)
- [Online Learning for Dynamic Constellation Topologies](https://mlsanity.com/paper/2603.25954/) (2026)

## Abstract (arXiv)

Dynamic topology reconfiguration is central to the reliability and efficiency of large satellite constellations, yet many existing approaches rely on idealized assumptions such as full constellation deployment or uniform orbital spacing. We present Adaptive Satellite Topology via Regret-Aware learning (ASTRA), a theoretically-grounded framework for dynamic satellite topology reconfiguration that builds on an online learning formulation and makes it computationally practical. ASTRA combines an ADMM-based offline solver with efficient online updates for both online gradient descent and online conditional gradient, yielding markedly cheaper constrained updates than generic optimization pipelines. On the theory side, we show that for a relevant class of entry-wise nonzero utility matrices, the objective is strongly convex, which yields logarithmic static regret for online gradient descent, and we further instantiate known dynamic-regret guarantees under inexact ADMM inner loops. Empirically, ASTRA matches or improves topology quality, presenting a good trade-off with computational time on synthetic constellations, and it remains effective on real Starlink data under partial deployment and non-uniform spacing, where idealized structural assumptions break down. These results position ASTRA as an efficient and theoretically grounded approach to topology reconfiguration in realistic Low Earth Orbit networks.