Satellite-Terrestrial Integrated Fog Networks: Architecture, Technologies, and Challenges

TL;DR

The paper introduces satellite-terrestrial integrated fog networks (STIFN) as a 6G paradigm that fuses fog satellite onboard processing with cloud-based terrestrial computing to overcome onboard-resource limits and dynamic ISL conditions. It details a five-plane STIFN architecture and two deployment cases, supported by core technologies—integrated waveform design, onboard resource management, cloud-fog mobility management, and native AI—that enable cross-domain collaboration for integrated communications, sensing, and navigation. The proposed framework emphasizes autonomous networking, edge intelligence, and secure, scalable orchestration across space and ground segments, with practical considerations such as network slicing, programmable data planes, and security. The paper also highlights hardware demonstrations and standardization efforts, underscoring the significance of STIFN in enabling low-latency, high-reliability 6G services through cloud-fog synergy and onboard edge computing.

Abstract

In the evolution of sixth-generation (6G) mobile communication networks, satellite-terrestrial integrated networks emerge as a promising paradigm, characterized by their wide coverage and reliable transmission capabilities. By integrating with cloud-based terrestrial mobile communication networks, the limitations of low Earth orbit (LEO) satellites, such as insufficient onboard computing capabilities and limited inter-satellite link capacity, can be addressed. In addition, to efficiently respond to the diverse integrated tasks of communication, remote sensing, and navigation, LEO constellations need to be capable of autonomous networking. To this end, this article presents a satellite-terrestrial integrated fog network for 6G. Its system architecture and key technologies are introduced to achieve flexible collaboration between fog satellites and terrestrial cloud computing centers. In particular, key techniques with diverse challenges and their corresponding solutions are discussed, including integrated waveform design and resource management based on fog satellite onboard processing, as well as mobility management and native artificial intelligence based on cloud-fog collaboration. Finally, future challenges and open issues are outlined.

Figures (5)

• Figure 1: System architecture of STIFNs.
• Figure 2: Three typical scenarios of FSN-enabled edge-terminal-service integration.
• Figure 3: Two typical deployment cases: a) networking of multiple fog satellite nodes; b) cloud-fog collaboration in STIFNs.
• Figure 4: Service latency performance and computation complexity of Benders decomposition-based branch-and-cut (BDBC) algorithm and time expansion-based decoupled greedy (TEDG) algorithm. The bars indicate average service latency and the lines indicate algorithm execution time.
• Figure 5: Robustness of distributed AI frameworks under different malicious device ratios (Mini-batch size (B), number of local epochs (E), malicious device ratio ($M_{d}$)).