Semantic Satellite Communications Based on Generative Foundation Model
Peiwen Jiang, Chao-Kai Wen, Xiao Li, Shi Jin, Geoffrey Ye Li

TL;DR
This paper introduces FMSAT, a foundation model-based semantic satellite communication framework that reduces bandwidth, enhances noise robustness, and improves semantic feature recovery in high-delay, interference-prone satellite environments.
Contribution
The study presents a novel FM-based semantic communication framework with adaptive encoding, semantic error detection, and image repair techniques tailored for satellite systems.
Findings
Significantly reduces bandwidth requirements.
Improves semantic feature recovery under high noise.
Ensures image quality with low delay.
Abstract
Satellite communications can provide massive connections and seamless coverage, but they also face several challenges, such as rain attenuation, long propagation delays, and co-channel interference. To improve transmission efficiency and address severe scenarios, semantic communication has become a popular choice, particularly when equipped with foundation models (FMs). In this study, we introduce an FM-based semantic satellite communication framework, termed FMSAT. This framework leverages FM-based segmentation and reconstruction to significantly reduce bandwidth requirements and accurately recover semantic features under high noise and interference. Considering the high speed of satellites, an adaptive encoder-decoder is proposed to protect important features and avoid frequent retransmissions. Meanwhile, a well-received image can provide a reference for repairing damaged images under…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsCognitive Computing and Networks · Advanced Computational Techniques and Applications
MethodsSPEED: Separable Pyramidal Pooling EncodEr-Decoder for Real-Time Monocular Depth Estimation on Low-Resource Settings
