Lightweight Semantic Communication-Compliant Shortest Path Selection in Large-Scale LEO Satellite Networks
Guo, Binquan Xiong, Zehui Zhang, Zhou Yang, Qianqian Li, Baosheng Niyato, Dusit Guizani, Mohsen Han, Zhu
Guo, Binquan
Xiong, Zehui
Zhang, Zhou
Yang, Qianqian
Li, Baosheng
Niyato, Dusit
Guizani, Mohsen
Han, Zhu
Supervisor
Department
Machine Learning
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Journal article
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English
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Abstract
Enhanced by inter-satellite links and satellite direct-to-device capabilities, satellite networks can offer low-latency communication globally. However, limited spectrum resources and the capacity bounds of the Shannon's information theory pose fundamental challenges for supporting bandwidth-intensive multimedia services. Semantic communication (SemCom) offers a promising solution by transmitting compressed semantic representations instead of raw data, thereby alleviating bandwidth pressure. However, it also introduces SemCom-related constraints that render conventional schemes such as contact graph routing inapplicable. To overcome this challenge, we investigate SemCom-compliant path selection and formulate it as a non-NP hard mixed-integer linear programming problem. To address the problem, we develop a graph-based scheme that exploits the special structure of the solution space, the sparsity of SemCom-capable satellites, and the property of Dijkstra's algorithm, thus achieving optimal solutions with polynomial-time complexity. Simulation results on the Starlink constellation confirm that the proposed scheme facilitates SemCom with negligible computational overhead and significant bandwidth reduction. While the bandwidth reduction comes at the cost of increased delay and path hops, these effects are shown to be mitigatable through higher SemCom deployment in a satellite network or by enabling semantic processing at the user side.
Citation
B. Guo, Z. Xiong, Z. Zhang, Q. Yang, B. Li, D. Niyato , et al., "Lightweight Semantic Communication-Compliant Shortest Path Selection in Large-Scale LEO Satellite Networks," IEEE Transactions on Mobile Computing, vol. PP, no. 99, pp. 1-17, 2026, https://doi.org/10.1109/tmc.2026.3675010.
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IEEE Transactions on Mobile Computing
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Keywords
40 Engineering, 4006 Communications Engineering, 46 Information and Computing Sciences
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IEEE