cs.ITSep 17, 2026

SpaceDiffusion: Over-the-Orbit Diffusion for Space Generate-and-Forward Communications

Authors: Jianhao Huang, Zhanwei Wang, Khaled B. Letaief, Kaibin Huang

Organizations: Department of Electrical and Computer Engineering, The University of Hong Kong (HKU), Hong Kong SAR, China · Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology (HKUST), Hong Kong SAR, China

Read PDF
Ask AI
Cite

Copy citation

DOINot available for this paper

Abstract

Satellite communications are an essential component of sixth-generation (6G) mobile networks, which provide ubiquitous connectivity for global services. However, the satellite uplink remains a critical bottleneck for ground devices: their limited transmit power and antenna apertures result in low data rates and high packet errors. To overcome this bottleneck, this paper advocates a novel relaying paradigm termed generate-and-forward (GF) communications, where satellites exploit on-orbit generative artificial intelligence (AI) to robustly reconstruct corrupted data prior to forwarding. Specifically, we propose SpaceDiffusion, an over-the-orbit diffusion framework for satellite-assisted image transmission. The core of this framework is a channel-distortion-aware diffusion theory developed using the following approach. By formulating the recovery of compressed and lost image tokens as an inverse problem, this theory incorporates a channel-distortion correction term directly into the conventional denoising diffusion implicit model (DDIM) update. As a result, this design enables a single pretrained diffusion model to adapt dynamically to varying packet-loss patterns and compression distortions without retraining. Furthermore, we analytically characterize the progressive token-reconstruction error and derive a diffusion-step activation threshold that predicts when SpaceDiffusion is expected to outperform conventional decode-and-forward (DF) relaying. Building on these theoretical insights, we further develop an energy-aware early-exit policy to efficiently deploy SpaceDiffusion in orbit. Experimental results demonstrate that SpaceDiffusion achieves lower end-to-end latency compared to DF scheme with retransmission protocol and saves approximately 15 dB of uplink transmit power at a target perceptual quality.

Figures & tables

Fig. 1: Comparison of the satellite generate-and-forward scheme with the conventional decode-and-forward scheme. For satellite communications with limited-power devices, the downlink capacity (on the order of Mbps) is much larger than the uplink capacity (on the order of Kbps) [ 12 ] .
Fig. 2: The framework of the Satellite GF system
Fig. 3: Illustration of the proposed posterior-based Diffusion theory. (a) Comparison of the reverse process with and without the plug-in term Lt\mathcal{L}_{t} . (b) Computational flow of the reverse process of the plug-and-play DDIM algorithm.
Fig. 4: LPIPS score versus the number of reverse diffusion steps. The total step TT , the average packet error ρ\rho , and the quantization step ss are set as 4040 , 0.10.1 , and 11 , respectively.
Fig. 5: E2E latency comparisons. The LPIPS for all the schemes are around 0.20.2 .
ParameterDefault setting
DDIM parametersσt=0,η=0.8\sigma_{t}=0,\eta=0.8
Packet length20482048
Uplink channel coding rate0.10.1
Carrier frequency22 GHz
Bandwidth180180 kHz
Uplink transmit power2323 dBm
TABLE I: Default simulation parameters.
Fig. 6: Reconstruction quality versus packet error rate.
Fig. 7: Reconstruction quality versus uplink transmit power.
Fig. 8: Visual reconstruction examples on the AFHQ dataset.
Fig. 9: Visual reconstruction examples on the Kodak dataset [ 40 ] .
Fig. 10: Energy-limited diffusion scheduling.
More in this topic

Wireless Communications

Wireless communications studies how information is sent over radio links and how networks manage signals, spectrum, and resources reliably.

See all 403 papers

Explore similar work

CardsList
Sep 1, 2026

Space Generative AI with Solar Energy Harvesting

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 (C2^2) 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 C2^2 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 C2^2 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.

Jierui Zhang, Jianhao Huang, Zhanwei Wang +1

Department of Electrical and Computer Engineering, The University of Hong Kong (HKU), Hong Kong SAR, China

Wireless Communications
AskPDF
Jul 10, 2026

Generative Communications: Overview, Technologies, and Trends

The groundbreaking development of generative artificial intelligence (AI) is rapidly boosting the ability to generate content such as images and videos, reshaping communication paradigms. This article introduces generative communications (GenCom), a novel paradigm for 6G networks in which large AI models (LAMs) drive semantic understanding, reasoning, and content generation, embedding these into the communication process. Unlike traditional systems that strictly pursue accurate bit transmission, GenCom enables transmitters to convey only minimal yet sufficient information, while receivers leverage shared generative priors and knowledge bases to synthesize the intended output. Communication is thus redefined as controlled generation rather than data reproduction. We formalize the concept of GenCom, clarify its AI-native and generation-driven properties, and present its core mechanisms. A two-layer GenCom architecture supported by key enabling technologies is proposed, and analysis of four representative application scenarios demonstrates that GenCom offers ultra-efficient transmission, semantic-level robustness, and new network functions. Finally, we outline future research directions, including foundational theory and real-time processing, highlighting a promising pathway toward 6G networks.

Wenjun Zhang, Zhiyong Chen, Tong Wu +4

Cooperative Medianet Innovation Center and the School of Information Science and Electronic Engineering, Shanghai Jiao Tong University, Shanghai 200240, China

6G NetworksSemantic CommunicationWireless Communications
AskPDF
Sep 7, 2026

Foundation Models for Generalizable Semantic and Goal-Oriented Communication

Semantic and goal-oriented communication is increasingly studied for 6G, but generalization beyond seen data remains a key weakness under tight rate budgets. Many existing systems overfit their training data and degrade sharply at very low bit rates because they attempt to compress the entire signal. We introduce Foundation Model-Guided Semantic and Goal-Oriented Communication (FMSGOC), a framework that uses broad visual-linguistic Foundation Model priors to mitigate overfitting. It further improves rate efficiency by concentrating bits on sparse, goal-aligned anchors and relying on generative foundation-model priors to reconstruct the masked regions. By decoupling what to send from how to reconstruct, a vision-language foundation model selects and transmits a sparse set of semantic anchors, while a pretrained diffusion model, fine-tuned for masked completion, reconstructs the image at the receiver. In our experiments, FMSGOC reaches 0.039 bits per pixel (BPP), maintains high semantic fidelity (cosine similarity 0.87-0.90 on CIFAR-10), remains robust on previously unseen inputs (0.83-0.86 on ImageNet), and shows good perceptual similarity (0.1278/0.1558, CIFAR-10/ImageNet), outperforming strong end-to-end baselines at lower bit rates.

Boliang Liu, Wint Yi Poe, Riccardo Trivisonno +1

Technical University of Berlin, Berlin, Germany · Huawei Technologies Düsseldorf GmbH, Munich Research Center, Munich, Germany

Learned Image CompressionSemantic Communication
AskPDF