Online Convex Optimization for On-Board Routing in High-Throughput Satellites

TL;DR

This paper introduces an online convex optimization approach for real-time internal packet routing in high-throughput satellites, improving capacity and reducing packet loss through iterative, low-overhead optimization.

Contribution

It presents a novel real-time routing and scheduling method using online convex optimization tailored for extremely high-throughput satellite payloads, filling a research gap.

Findings

Reduces packet loss in satellite routing scenarios

Supports real-time rerouting with low computational overhead

Outperforms traditional batch optimization methods

Abstract

The rise in low Earth orbit (LEO) satellite Internet services has led to increasing demand, often exceeding available data rates and compromising the quality of service. While deploying more satellites offers a short-term fix, designing higher-performance satellites with enhanced transmission capabilities provides a more sustainable solution. Achieving the necessary high capacity requires interconnecting multiple modem banks within a satellite payload. However, there is a notable gap in research on internal packet routing within extremely high-throughput satellites. To address this, we propose a real-time optimal flow allocation and priority queue scheduling method using online convex optimization-based model predictive control. We model the problem as a multi-commodity flow instance and employ an online interior-point method to solve the routing and scheduling optimization iteratively. This approach minimizes packet loss and supports real-time rerouting with low computational overhead. Our method is tested in simulation on a next-generation extremely high-throughput satellite model, demonstrating its effectiveness compared to a reference batch optimization and to traditional methods.