arXiv AI
Space Generative AI with Solar Energy Harvesting
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Space Generative AI with Solar Energy Harvesting
Satellites are emerging as promising platforms to extend generative \emph{artificial intelligence} (AI) services to remote areas lacking terrestrial infrastructure. However, deploying space generative...
arXiv AI
Jun 26
SOLAR: AI-Powered Speed-of-Light Performance Analysis
arXiv:2606. 26383v1 Announce Type: cross Abstract: How fast could a deep-learning model run on target hardware, and how far is today's implementation from that limit?
arXiv AI
Oct 26
Heterogeneous Space Data Centers: A Key Paradigm for Future Non-Terrestrial and In-Orbit Services
The paper introduces heterogeneous space data centers (SDCs) that combine large and small nodes to deliver AI‑centric computing and communication services for ground, aerial, and in‑orbit users. It outlines use cases, node sizing, placement strategies, and interconnection topologies, and focuses on dawn‑dusk low‑Earth orbit configurations to analyze solar power, visibility windows, and latency‑aware coverage. The authors evaluate latency, service reliability, and energy requirements for various SDC roles and deployment scales, highlighting trade‑offs among node size, orbit selection, and service portfolio, and offer design guidance and future research directions for AI infrastructure in space.
arXiv AI
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SpaceDiffusion: Over-the-Orbit Diffusion for Space Generate-and-Forward Communications
arXiv:2609.20899v1 Announce Type: cross Abstract: Satellite communications are an essential component of sixth-generation (6G) mobile networks, which provide ubiquitous connectivity for global servic...
arXiv Machine Learning
Oct 12
Goal-Oriented Bundle Management and Flow Control for Deep-Space Communications via Reinforcement Learning
The paper introduces a reinforcement‑learning framework for managing bundles and flow control in deep‑space networks, where relay nodes must schedule and drop data under intermittent connectivity and limited storage. It defines a goal‑oriented loss based on excess age to prioritize mission‑relevant information and formulates relay control as a constrained semi‑Markov decision process solved with masked Lagrangian PPO. Simulations show that the approach improves age‑based utility and inter‑flow fairness compared to traditional schedulers.
arXiv AI
Oct 27
Satellite Intelligence: Distributed Training, Collaborative Inference,and LLM Adaptation in LEO Constellation
arXiv:2610.35282v1 Announce Type: cross Abstract: The proliferation of orbital sensing data and the real-time demands of emerging global services are pushing low Earth orbit (LEO) satellite networks...