Deep Reinforcement Learning-Based Computation Computational Offloading for Space–Air–Ground Integrated Vehicle Networks

Abstract

In remote or disaster areas, where terrestrial networks are difficult to cover and Terrestrial Edge Computing (TEC) infrastructures are unavailable, solving the computation computational offloading for Internet of Vehicles (IoV) scenarios is challenging. Current terrestrial networks have high data rates, great connectivity, and low delay, but global coverage is limited. Space–Air–Ground Integrated Networks (SAGIN) can improve the coverage limitations of terrestrial networks and enhance disaster resistance. However, the rising complexity and heterogeneity of networks make it difficult to find a robust and intelligent computational offload strategy. Therefore, joint scheduling of space, air, and ground resources is needed to meet the growing demand for services. In light of this, we propose an integrated network framework for Space-Air Auxiliary Vehicle Computation (SA-AVC) and build a system model to support various IoV services in remote areas. Our model aims to maximize delay and fair utility and increase the utilization of satellites and Autonomous aerial vehicles (AAVs). To this end, we propose a Deep Reinforcement Learning algorithm to achieve real-time computational computational offloading decisions. We utilize the Rank-based Prioritization method in Prioritized Experience Replay (PER) to optimize our algorithm. We designed simulation experiments for validation and the results show that our proposed algorithm reduces the average system delay by 17.84%, 58.09%, and 58.32%, and the average variance of the task completion delay will be reduced by 29.41%, 48.74%, and 49.58% compared to the Deep Q Network (DQN), Q-learning and RandomChoose algorithms.