Intelligent Sensing, Communication, Computation, and Caching for Satellite-Ground Integrated Networks

Satellite-ground integrated networks (SGINs) are regarded as promising architectures for sensing heterogenous measurements, reducing network congestion and for providing pervasive intelligence services in support of terrestrial users. In SGINs, both low, medium and geostationary orbit based satellites are deployed for achieving global coverage and for supporting communication services for terrestrial users. However, the integration of task sensing, computation, communication and caching functionalities is quite challenging, which leads to low real-time task processing capabilities in dynamically fluctuating complex network environments. Hence, we propose an edge-intelligence-driven collaborative SGIN architecture and construct a framework relying on multiple planes, including the sensing plane, forwarding plane, control plane, intelligence plane and application plane. Furthermore, we construct an integrated sensing, communication, computation, caching, and intelligence service paradigm for SGINs. Moreover, a centralized and distributed integrated learning framework is established for computation offloading and network function virtualization. Our simulation results show that the proposed learning framework is superior to the existing baseline methods in terms of its average transmission rate. Finally, we list a suite of potential research directions and solutions.