Work

Delay and Energy Optimization in Heterogeneous GEO–LEO Satellite Networks: A GNN-Enhanced Game-Theoretic and DRL Approach

Year: 2026

Type: article

Abstract: As 6G mobile communications evolve, Low Earth Orbit (LEO) satellite mobile edge computing (MEC) enables globally seamless computing. However, the high mobility of LEO satellites disrupts service continuity and resource stability. Existing approaches often use oversimplified models that ignore multi-beam interference and dynamic task queueing. To address this, we establish a hierarchical Geostationary Earth Orbit (GEO)–LEO synergistic architecture, where the integration is implemented by utilizing GEO satellites as stability anchors and remote cloud relays, while LEO satellites provide low-latency edge processing. We formulate fine-grained models for two-level beam-centric communication and preemptive dynamic queueing. The resulting joint task offloading and resource allocation problem is a complex mixed-integer nonlinear program (MINLP). To effectively solve this MINLP, we decouple it hierarchically: first determine discrete offloading decisions, then optimize continuous resource allocations based on them, proposing a novel framework termed G2DRL (GNN-enhanced Game-theoretic and deep reinforcement learning). Simulation results demonstrate that G2DRL significantly reduces the weighted sum of system delay and energy, showing superior convergence stability and performance over state-of-the-art DRL baselines.

Source: Future Internet

DOI: 10.3390/fi18060288

Authors: Yiyu Wang, Zhufang Kuang, Mingxiao Lei

Institution: Central South University of Forestry and Technology

Language: English

Topic: Satellite Communication Systems

Subfield: Aerospace Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $1,930