Multi-Stage Survivable Network Slicing With Load Balancing for LEO Mega-Constellation

In the low earth orbit (LEO) mega-constellation, dynamically reconstructing resources to enhance the on-orbit efficiency and flexibility of payloads remains a critical challenge. Network slicing (NS), a core concept in 5G communications, offers a promising solution by deploying end-to-end customized and logically isolated virtual networks over shared infrastructures. However, existing methods overlook the survivability of satellite nodes and the impact of load balancing of satellites and inter-satellite links (ISLs) on subsequent slicing processes. Therefore, this paper proposes a multi-stage survivable NS (MS-SNS) method based on load balancing for LEO mega-constellations. The method adopts a hybrid heuristic-reinforcement learning (RL) framework, decomposing the problem into multiple stages. First, a multi-constraint “load coefficient” indicator is proposed for constellation target area planning and selection using a greedy algorithm. Second, a soft actor-critic (SAC)-based algorithm is proposed for slice mapping within the selected target region. Finally, a survivable NS remapping algorithm is proposed, where a multi-constraint “remapping cost” indicator is proposed to guide the selection of remappable LEO satellite nodes, ensuring service continuity during node failures. Extensive simulations demonstrate the effectiveness of MS-SNS.