Fuzzy-CNN Based Multi-Task Routing for Integrated Satellite-Terrestrial Networks

Abstract

The integrated satellite-terrestrial network (ISN) is an emerging framework that complements the ground 5G network with global network access. The satellite layer consists of Low Earth Orbit (LEO) and Geosynchronous (GEO) satellites for long-distance and high-throughput data transmission with low latency. However, traditional ground routing strategies are not appropriate in ISN, considering the dynamic motion and different capabilities of satellites. Moreover, multi-type tasks need to be allocated with optimal inter-satellite transmission links (ISL) to ensure both the load balance of ISN and the quality of experience (QoE) of subscribers. In this paper, we combine the Software Defined Networking (SDN), AI technique, and fuzzy logic to optimize multi-task routing in ISN. The GEO satellites and the ground computing center (GCC) are set as the joint control plane. The load information of ISN at different times is collected by GEO controllers and made into a multi-D matrix. The GCC collects the historical traffic data from GEO controllers for convolutional neural network (CNN) model training and updating. The GEO satellites utilize the trained CNN model to make path allocation decisions and issue flow tables to LEO satellites. Further, considering the CNN judgment may contradict the user QoE, we use fuzzy logic to evaluate task requirements to improve the CNN output for optimal ISN path allocation. The simulation results demonstrate that the fuzzy-CNN based multi-task routing (FCMR) method has better traffic control performance and path finding flexibility under different ISN conditions.