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

TL;DR

This paper proposes a digital twin-assisted SAGIMEC framework for low-altitude economy, introducing an online decentralized optimization approach that enhances network management and resource allocation for IoT applications.

Contribution

It develops a novel digital twin-assisted SAGIMEC paradigm and an online decentralized optimization method to improve real-time decision-making and system performance in low-altitude economy networks.

Findings

The proposed ODOA outperforms alternative methods in simulations.

The framework effectively manages heterogeneous SAGIMEC networks.

The approach enhances QoS for IoT sensor devices.

Abstract

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, a joint satellite selection, computation offloading, communication resource allocation, computation resource allocation and UAV trajectory control optimization problem (JSC4OP) is formulated to maximize the quality of service (QoS) of ISDs. Given the complexity of JSC4OP, we propose an online decentralized optimization approach (ODOA) to address the problem. Specifically, JSC4OP 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, while the simulation results demonstrate that the proposed ODOA outperforms other alternative approaches in terms of overall system performance.