# Space Generative AI with Solar Energy Harvesting

arXiv: https://arxiv.org/abs/2609.01062 · HTML: https://mlsanity.com/paper/2609.01062/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 20 of its 46 references that are in the map.
- First posted on arXiv 1 Sep 2026.

## Builds on (most cited first, 20 of 20)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Denoising Diffusion Implicit Models](https://mlsanity.com/paper/2010.02502/) (2020)
- [From Words to Watts: Benchmarking the Energy Costs of Large Language Model Inference](https://mlsanity.com/paper/2310.03003/) (2023)
- [Mobile Edge Intelligence for Large Language Models: A Contemporary Survey](https://mlsanity.com/paper/2407.18921/) (2024)
- [Condition-Aware Neural Network for Controlled Image Generation](https://mlsanity.com/paper/2404.01143/) (2024)
- [TrimCaching: Parameter-sharing Edge Caching for AI Model Downloading](https://mlsanity.com/paper/2404.14204/) (2024)
- [A Survey on Resource Management in Joint Communication and Computing-Embedded SAGIN](https://mlsanity.com/paper/2403.17400/) (2024)
- [Space-ground Fluid AI for 6G Edge Intelligence](https://mlsanity.com/paper/2411.15845/) (2024)
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- [Integrated Sensing, Communication, and Computation for Over-the-Air Federated Edge Learning](https://mlsanity.com/paper/2508.15185/) (2025)
- [Computing-Aware Routing for LEO Satellite Networks: A Transmission and Computation Integration Approach](https://mlsanity.com/paper/2211.08820/) (2022)
- [SpaceMoE: Realizing Distributed Mixture-of-Experts Inference over Space Networks](https://mlsanity.com/paper/2605.00515/) (2026)
- [In-Orbit Processing or Not? Sunlight-Aware Task Scheduling for Energy-Efficient Space Edge Computing Networks](https://mlsanity.com/paper/2407.07337/) (2024)
- [FedMeld: A Model-dispersal Federated Learning Framework for Space-ground Integrated Networks](https://mlsanity.com/paper/2412.17231/) (2024)
- [Energy-Efficient Design of Satellite-Terrestrial Computing in 6G Wireless Networks](https://mlsanity.com/paper/2311.08904/) (2023)
- [3C Resources Joint Allocation for Time-Deterministic Remote Sensing Image Backhaul in the Space-Ground Integrated Network](https://mlsanity.com/paper/2510.09409/) (2025)
- [Toward Communication-Efficient Space Data Centers: Bottlenecks, Architectures, and New Paradigms](https://mlsanity.com/paper/2605.12681/) (2026)

## Abstract (arXiv)

Satellites are emerging as promising platforms to extend generative artificial intelligence (AI) services to remote areas lacking terrestrial infrastructure. However, deploying space generative AI is fundamentally constrained by the limited, time-varying onboard energy supplied by solar energy harvesting (EH). This paper presents a framework for solar-powered space generative AI in which a satellite receives a user prompt, executes a diffusion-based image-generation model, and downlinks the compressed result within a strict time window. We identify the fundamental computation--communication (C2) trade-offs governed by the shared harvested-energy budgets. Specifically, increasing the number of generation steps improves intrinsic image quality but depletes energy and time available for downlink transmission, whereas prioritizing communication guarantees reliable delivery but sacrifices semantic quality. To balance these trade-offs and maximize end-to-end (E2E) generative performance, we exploit the predictable solar-EH dynamics induced by deterministic orbital motion and develop a joint C2 resource-optimization framework using a tractable two-step approach. First, we characterize the maximum downlink throughput for a fixed generation depth under continuous solar EH. This establishes a separation principle that decouples waiting-time selection from optimal transmit-power control. Next, we formulate a joint C2 utility-maximization problem and derive a closed-form, low-complexity step-selection policy in the dominant constant-power regime. Extensive experiments under realistic orbital dynamics demonstrate that the proposed policy dynamically balances generation quality and transmission reliability. This yields significant E2E performance gains over static computation- and communication-centric baselines across diverse solar-EH states.