Towards Routing and Edge Computing in Satellite-Terrestrial Networks: A Column Generation Approach

TL;DR

This work addresses the challenge of timely, scalable computing in satellite-terrestrial networks by introducing a three-layer edge computing protocol that allows local satellite processing, inter-satellite offloading, and ground-station processing. It formulates the routing and offloading task as a linear program aimed at maximizing computing capacity under hop-based latency and capacity constraints, and tackles the resulting combinatorial explosion with a column generation approach that iteratively activates only a subset of routes. The method demonstrates a 60% improvement in computing capacity over a single-layer baseline while reducing problem size by up to 92% through selective route activation, highlighting the practical potential of adaptive, multi-hop offloading in STNs with laser ISLs. The combination of three-layer MEC and column generation offers a scalable, high-performance framework for next-generation satellite networks and 6G-era edge computing.

Abstract

Edge computing that enables satellites to process raw data locally is expected to bring further timeliness and flexibility to satellite-terrestrial networks (STNs). In this letter, we propose a three-layer edge computing protocol, where raw data collected by the satellites can be processed locally, or transmitted to other satellites or the ground station via multi-hop routing for further processing. The overall computing capacity of the proposed framework is maximized by determining the offloading strategy and routing formation, subject to channel capacity and hop constraints. Given that the problem scale grows exponentially with the number of satellites and maximum-allowed hops, the column generation approach is employed to obtain the global optimal solution by activating only a subset of variables. Numerical results reveal that the proposed three-layer computing protocol, when tolerating a 5-hop routing latency, achieves a 60% improvement in computation capacity compared to the single-layer local computing configuration.

Figures (3)

• Figure 1: Illustration of the STN.
• Figure 2: Computed data volume under different data distribution.
• Figure 3: Data volume computed on different layers.