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Traditional ground wireless communication networks cannot providehigh-quality services for artificial intelligence (AI) applications such asintelligent transportation systems (ITS) due to deployment, coverage andcapacity issues. The space-air-ground integrated network (SAGIN) has become aresearch focus in the industry. Compared with traditional wirelesscommunication networks, SAGIN is more flexible and reliable, and it has widercoverage and higher quality of seamless connection. However, due to itsinherent heterogeneity, time-varying and self-organizing characteristics, thedeployment and use of SAGIN still faces huge challenges, among which theorchestration of heterogeneous resources is a key issue. Based on virtualnetwork architecture and deep reinforcement learning (DRL), we model SAGIN'sheterogeneous resource orchestration as a multi-domain virtual networkembedding (VNE) problem, and propose a SAGIN cross-domain VNE algorithm. Wemodel the different network segments of SAGIN, and set the network attributesaccording to the actual situation of SAGIN and user needs. In DRL, the agent isacted by a five-layer policy network. We build a feature matrix based onnetwork attributes extracted from SAGIN and use it as the agent trainingenvironment. Through training, the probability of each underlying node beingembedded can be derived. In test phase, we complete the embedding process ofvirtual nodes and links in turn based on this probability. Finally, we verifythe effectiveness of the algorithm from both training and testing.

Space-Air-Ground Integrated Multi-domain Network Resource Orchestration based on Virtual Network Architecture: a DRL Method