Digital Twin-Assisted Space-Air-Ground Integrated Multi-Access Edge Computing for Low-Altitude Economy: An Online Decentralized Optimization Approach

The emergence of space-air-ground integrated multi-access edge computing (SAGIMEC) networks opens a significant opportunity for the rapidly growing low altitude economy (LAE), facilitating the development of various applications by offering efficient communication and computing services. However, the heterogeneous nature of SAGIMEC networks, coupled with the stringent computational and communication requirements of diverse applications in the LAE, introduces considerable challenges in integrating SAGIMEC into the LAE. In this work, we first present a digital twin-assisted SAGIMEC paradigm for LAE, where digital twin enables reliable network monitoring and management, while SAGIMEC provides efficient computing offloading services for Internet of Things sensor devices (ISDs). Then, ajointsatellite selection,computation offloading,communication resource allocation,computation resource allocation and uncrewed aerial vehicle (UAV) trajectorycontrol optimization problem ($\text{JSC}^{4}\text{OP}$) is formulated to maximize the quality of service (QoS) of ISDs. Given the complexity of$\text{JSC}^{4}\text{OP}$, we propose an online decentralized optimization approach (ODOA) to address the problem. Specifically,$\text{JSC}^{4}\text{OP}$is first transformed into a real-time decision-making optimization problem (RDOP) by leveraging Lyapunov optimization. Then, to solve the RDOP, we introduce an online learning-based latency prediction method to predict the uncertain system environment and a game theoretic decision-making method to make real-time decisions. Finally, theoretical analysis confirms the effectiveness of the ODOA. b Simulation results show that the proposed ODOA outperforms various benchmark approaches and improves the QoS of ISDs by at least 14.5% compared to deep reinforcement learning (DRL)-based approaches, thereby validating the superiority of the proposed approach.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.