# LLM-Enhanced Space-Air-Ground-Sea Integrated Networks

arXiv: https://arxiv.org/abs/2509.02540 · HTML: https://mlsanity.com/paper/2509.02540/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Sep 2025.

## Built on by (most cited first, 1 of 1)

- [From Intent to Infrastructure: LLM-Driven Agent Compilers for ISAC Networks](https://mlsanity.com/paper/2607.16269/) (2026)

## Abstract (arXiv)

The space-air-ground-sea integrated networking (SAGSIN) concept promises seamless global multimedia connectivity, yet two obstacles still limit its practical deployment. Firstly, high-velocity satellites, aerial relays and sea-surface platforms suffer from obsolete channel state information (CSI), undermining feedback-based adaptation. Secondly, data-rate disparity across the protocol stack is extreme: terabit optical links in space coexist with kilobit acoustic under-water links. This article shows that a single large language model (LLM) backbone, trained jointly on radio, optical and acoustic traces, can provide a unified, data-driven adaptation layer that addresses both rapid CSI ageing and severe bandwidth disparity across the SAGSIN protocol stack. Explicitly, an LLM-based long-range channel predictor forecasts the strongest delay-Doppler components several coherence intervals ahead, facilitating near-capacity reception despite violent channel fluctuations. Furthermore, our LLM-based semantic encoder turns raw sensor payloads into task-oriented tokens. This substantially reduces the SNR required for high-fidelity image delivery in a coastal underwater link, circumventing the data rate limitation by semantic communications. Inclusion of these tools creates a medium-agnostic adaptation layer that spans radio, optical and acoustic channels. We conclude with promising open research directions in on-device model compression, multimodal fidelity control, cross-layer resource orchestration and trustworthy operation, charting a path from laboratory prototypes to field deployment.