Satellite Edge Artificial Intelligence with Large Models: Architectures and Technologies

• URL: http://arxiv.org/abs/2504.01676v1
• Date: Wed, 02 Apr 2025 12:25:57 GMT
• Title: Satellite Edge Artificial Intelligence with Large Models: Architectures and Technologies
• Authors: Yuanming Shi, Jingyang Zhu, Chunxiao Jiang, Linling Kuang, Khaled B. Letaief,
• Abstract summary: Extreme weather nowcasting, disaster monitoring, and battlefield surveillance require realtime data processing.<n>Satellite edge large AI model (LAM) involves both the training (i.e., finetuning) and inference phases.<n>LAM deployment in resource-constrained space networks with time-varying topologies.
• Score: 43.24943201324192
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Driven by the growing demand for intelligent remote sensing applications, large artificial intelligence (AI) models pre-trained on large-scale unlabeled datasets and fine-tuned for downstream tasks have significantly improved learning performance for various downstream tasks due to their generalization capabilities. However, many specific downstream tasks, such as extreme weather nowcasting (e.g., downburst and tornado), disaster monitoring, and battlefield surveillance, require real-time data processing. Traditional methods via transferring raw data to ground stations for processing often cause significant issues in terms of latency and trustworthiness. To address these challenges, satellite edge AI provides a paradigm shift from ground-based to on-board data processing by leveraging the integrated communication-and-computation capabilities in space computing power networks (Space-CPN), thereby enhancing the timeliness, effectiveness, and trustworthiness for remote sensing downstream tasks. Moreover, satellite edge large AI model (LAM) involves both the training (i.e., fine-tuning) and inference phases, where a key challenge lies in developing computation task decomposition principles to support scalable LAM deployment in resource-constrained space networks with time-varying topologies. In this article, we first propose a satellite federated fine-tuning architecture to split and deploy the modules of LAM over space and ground networks for efficient LAM fine-tuning. We then introduce a microservice-empowered satellite edge LAM inference architecture that virtualizes LAM components into lightweight microservices tailored for multi-task multimodal inference. Finally, we discuss the future directions for enhancing the efficiency and scalability of satellite edge LAM, including task-oriented communication, brain-inspired computing, and satellite edge AI network optimization.

Related papers

• Satellite Federated Fine-Tuning for Foundation Models in Space Computing Power Networks [42.14666281406915]
  We propose a satellite-ground collaborative federated fine-tuning framework. We introduce tailored communication strategies that integrate both communication and computing resources. Simulation results demonstrate significant reductions in training time with improvements of approximately 33%.
  arXiv  Detail & Related papers  (2025-04-14T16:52:34Z)
• Brain-Inspired Decentralized Satellite Learning in Space Computing Power Networks [42.67808523367945]
  Space Computing Power Networks (Space-CPN) emerges as a promising architecture to coordinate the computing capability of satellites and enable on board data processing.<n>We propose to employ spiking neural networks (SNNs), which is supported by the neuromorphic computing architecture, for on-board data processing.<n>We put forward a decentralized neuromorphic learning framework, where a communication-efficient inter-plane model aggregation method is developed.
  arXiv  Detail & Related papers  (2025-01-27T12:29:47Z)
• A Distance Similarity-based Genetic Optimization Algorithm for Satellite Ground Network Planning Considering Feeding Mode [53.71516191515285]
  The low transmission efficiency of the satellite data relay back mission has become a problem that is currently constraining the construction of the system. We propose a distance similarity-based genetic optimization algorithm (DSGA), which considers the state characteristics between the tasks and introduces a weighted Euclidean distance method to determine the similarity between the tasks.
  arXiv  Detail & Related papers  (2024-08-29T06:57:45Z)
• Hierarchical Learning and Computing over Space-Ground Integrated Networks [40.19542938629252]
  We propose a hierarchical learning and computing framework to provide global aggregation services for locally trained models on ground IoT devices. We formulate a network energy problem for model aggregation, which turns out to be a Directed Steiner Tree (DST) problem. We propose a topologyaware energy-efficient routing (TAEER) algorithm to solve the DST problem by finding a minimum spanning arborescence on a substitute directed graph.
  arXiv  Detail & Related papers  (2024-08-26T09:05:43Z)
• Leveraging Large Language Models for Integrated Satellite-Aerial-Terrestrial Networks: Recent Advances and Future Directions [47.791246017237]
  Integrated satellite, aerial, and terrestrial networks (ISATNs) represent a sophisticated convergence of diverse communication technologies. This paper explores the transformative potential of integrating Large Language Models (LLMs) into ISATNs.
  arXiv  Detail & Related papers  (2024-07-05T15:23:43Z)
• Multi-Objective Optimization for UAV Swarm-Assisted IoT with Virtual Antenna Arrays [55.736718475856726]
  Unmanned aerial vehicle (UAV) network is a promising technology for assisting Internet-of-Things (IoT) Existing UAV-assisted data harvesting and dissemination schemes require UAVs to frequently fly between the IoTs and access points. We introduce collaborative beamforming into IoTs and UAVs simultaneously to achieve energy and time-efficient data harvesting and dissemination.
  arXiv  Detail & Related papers  (2023-08-03T02:49:50Z)
• Olive Branch Learning: A Topology-Aware Federated Learning Framework for Space-Air-Ground Integrated Network [19.059950250921926]
  Training AI models centrally with the assistance of SAGIN faces the challenges of highly constrained network topology, inefficient data transmission, and privacy issues. We first propose a novel topology-aware federated learning framework for the SAGIN, namely Olive Branch Learning (OBL) We extend our OBL framework and CNASA algorithm to adapt to more complex multi-orbit satellite networks.
  arXiv  Detail & Related papers  (2022-12-02T14:51:42Z)
• Artificial Intelligence Techniques for Next-Generation Mega Satellite Networks [37.87439415970645]
  This article introduces the application of AI techniques for integrated terrestrial satellite networks, particularly massive satellite network communications. It details the unique features of massive satellite networks, and the overarching challenges concomitant with their integration into the current communication infrastructure. This entails applying AI for forecasting the highly dynamic radio channel, spectrum sensing and classification, signal detection and demodulation, inter-satellite and satellite access network optimization, and network security.
  arXiv  Detail & Related papers  (2022-06-02T13:56:32Z)
• Machine Learning-Based User Scheduling in Integrated Satellite-HAPS-Ground Networks [82.58968700765783]
  Integrated space-air-ground networks promise to offer a valuable solution space for empowering the sixth generation of communication networks (6G) This paper showcases the prospects of machine learning in the context of user scheduling in integrated space-air-ground communications.
  arXiv  Detail & Related papers  (2022-05-27T13:09:29Z)
• Federated learning for LEO constellations via inter-HAP links [0.0]
  Low Earth Obit (LEO) satellite constellations have seen a sharp increase of deployment in recent years. To apply machine learning (ML) in such applications, the traditional way of downloading satellite data such as imagery to a ground station (GS) is not desirable. We show that existing FL solutions do not fit well in such LEO constellation scenarios because of significant challenges such as excessive convergence delay and unreliable wireless channels.
  arXiv  Detail & Related papers  (2022-05-15T08:22:52Z)
• Artificial Intelligence for Satellite Communication: A Review [91.3755431537592]
  This work provides a general overview of AI, its diverse sub-fields, and its state-of-the-art algorithms. The application of AI to a wide variety of satellite communication aspects have demonstrated excellent potential, including beam-hopping, anti-jamming, network traffic forecasting, channel modeling, telemetry mining, ionospheric scintillation detecting, interference managing, remote sensing, behavior modeling, space-air-ground integrating, and energy managing.
  arXiv  Detail & Related papers  (2021-01-25T13:01:16Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.