Satellite Network Slice Planning with Handover Trigger and DRL-Based Virtual Network Embedding

Abstract

For satellite network slicing, the end-to-end connectivity should be maintained during the service time of slices under the mobility of low Earth orbit (LEO) satellites. The ground user or station should update the satellite connection at least every 10 minutes, and the routing paths established through inter-satellite links (ISLs) are susceptible to performance degradation as a consequence of fluctuations in relative satellite distances. Therefore, the end-to-end connectivity management of the satellite network slice and its update during the slice service time are crucial issues. In satellite network slice planning (SNSP), the end-to-end connectivity decision is made by solving a virtual network embedding (VNE) problem, and the connectivity is maintained by updating the end-to-end routing path when satellite-ground handover occurs. Hence, an optimal integrated management of VNE and handover is necessary for SNSP. In this paper, we propose an efficient SNSP algorithm leveraging a simple and lightweight deep reinforcement learning (DRL) framework where actions of the learning are to select appropriate embedding methods and optimal pairs of actions and states. Here, a handover trigger (HT) mechanism is developed by introducing an SNSP utility, which is a joint function of end-to-end latency and service available time, so that handover preemptively happens before significant performance degradation. Moreover, dynamic virtual network embedding (VNE) and re-embedding methods are proposed using a deep Q-network (DQN) framework. Extensive simulation results show that the proposed DQN-HT algorithm achieves approximately 36% lower average end-to-end latency compared with benchmarks. © IEEE.