Digital Twin-Assisted Space-Air-Ground Integrated Networks for Vehicular Edge Computing

Abstract

In this paper, we present a framework that integrates digital twin (DT) technology into space-air-ground integrated networks (SAGINs) to enhance vehicular edge computing (VEC). Our objective is to efficiently offload tasks in ultra-reliable low-latency communications (URLLC)-enabled vehicular networks, focusing on minimizing overall latency for requested tasks by reducing transmission time for task offloading and edge processing requirements. The proposed framework leverages DT-assisted SAGINs to minimize task offloading latency, expand network coverage, and reduce energy consumption. Key components of our framework include partial task offloading, distributed edge computing, latency modeling, energy consumption analysis, mobility, and channel modeling. We formulate a non-convex optimization problem considering various network constraints to achieve the system objective. To solve this optimization problem, we develop a novel multi-agent deep reinforcement learning (DRL) algorithm, enabling intelligent decision-making by individual agents. Through extensive simulations, we validate the effectiveness of our proposed system in advancing VEC by integrating DT technology into SAGINs.