LLM-Enhanced Space-Air-Ground-Sea Integrated Networks
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866916930220195840 |
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| author | Yang, Halvin Lambotharan, Sangarapillai Derakhshani, Mahsa Hanzo, Lajos |
| author_facet | Yang, Halvin Lambotharan, Sangarapillai Derakhshani, Mahsa Hanzo, Lajos |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_02540 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | LLM-Enhanced Space-Air-Ground-Sea Integrated Networks Yang, Halvin Lambotharan, Sangarapillai Derakhshani, Mahsa Hanzo, Lajos Signal Processing 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. |
| title | LLM-Enhanced Space-Air-Ground-Sea Integrated Networks |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2509.02540 |