Resource management in space-air-ground integrated vehicular networks: SDN contorl and AI algorithm design

Abstract

With its potential versatility and reliability, the space-air-ground integrated vehicular network (SAGVN) is envisioned as a promising solution to deliver quality vehicular services anywhere at any time. This article proposes a software defined framework for SAGVN to achieve flexible, reliable, and scalable network resource management. First, key applications and research challenges in resource management are identified. Then we propose a hybrid and hierarchical SAGVN control architecture to balance the trade-off between system status acquisition and signaling overhead in different scenarios. Considering the dynamic networking environment with multi-dimensional resources and diverse services, it is challenging to make optimal resource management decisions in real time; thus, artificial intelligence (AI)-based engineering solutions are investigated to facilitate efficient network slicing, mobility management, and cooperative content caching and delivery. A trace-driven case study is presented to demonstrate the effectiveness of the proposed SAGVN framework with AI-based methods in increasing the SAGVN throughput performance.