An adaptive semantic communication method for tropospheric over-the-horizon transmission integrating radio meteorology

The spatiotemporal non-uniformity of maritime meteorological conditions leads to dynamic attenuation of electromagnetic waves, multipath effects, and noise interference. Moreover, the communication system cannot rely on fixed base stations and depends on over-the-horizon propagation. The severe vari...

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Vydané v:Physical communication Ročník 73; s. 102895
Hlavní autori: Chen, Can, Li, Lei, Zhang, Long, Ren, Danping, Wu, Xiaoyu
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Elsevier B.V 01.12.2025
Predmet:
Adaptive modulation and coding
Path loss
Semantic communication
Transformer
Tropospheric over-the-horizon communication
Tropospheric over-the-horizon communication
Semantic communication
Transformer
Path loss
Adaptive modulation and coding
ISSN:1874-4907
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Popis
Shrnutí:The spatiotemporal non-uniformity of maritime meteorological conditions leads to dynamic attenuation of electromagnetic waves, multipath effects, and noise interference. Moreover, the communication system cannot rely on fixed base stations and depends on over-the-horizon propagation. The severe variations in channel conditions further exacerbate the high bit error rates and low transmission efficiency issues of traditional communication systems. This makes semantic communication systems based on static channel models ill-equipped to handle complex maritime meteorological environments, failing to effectively optimize signal transmission and severely impacting the performance of tropospheric over-the-horizon communication systems. To address this issue, this paper proposes an Adaptive Semantic Communication System (ASCS). By incorporating Channel State Information Score (CSIS) into the Transformer encoding structure, ASCS dynamically adjusts information priority to ensure that critical information is transmitted first. At the same time, dynamically adjusting coding and modulation strategies based on the electromagnetic wave propagation mechanism to construct an environment-adaptive channel transmission scheme. By deeply coupling meteorological environmental information, the semantic layer and physical layer are collaboratively optimized, enhancing semantic transmission efficiency and improving accuracy. Experimental results demonstrate that the proposed method can dynamically respond to meteorological environments, exhibiting strong robustness to channel variations, and showing significant performance advantages especially under high path loss (low signal-to-noise ratio) conditions.
ISSN:1874-4907
DOI:10.1016/j.phycom.2025.102895