Channel Semantic Characterization for Integrated Sensing and Communication Scenarios: From Measurements to Modeling
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arXiv
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| Hauptverfasser: | , , , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866917943326015488 |
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| author | Zhang, Zhengyu He, Ruisi Ai, Bo Yang, Mi Zhang, Xuejian Qi, Ziyi Zhong, Zhangdui |
| author_facet | Zhang, Zhengyu He, Ruisi Ai, Bo Yang, Mi Zhang, Xuejian Qi, Ziyi Zhong, Zhangdui |
| contents | With the advancement of sixth-generation (6G) wireless communication systems, integrated sensing and communication (ISAC) is crucial for perceiving and interacting with the environment via electromagnetic propagation, termed channel semantics, to support tasks like decision-making. However, channel models focusing on physical characteristics face
challenges in representing semantics embedded in the channel, thereby limiting the evaluation of ISAC systems. To tackle this, we present a novel framework for channel modeling from
the conceptual event perspective. By leveraging a multi-level semantic structure and characterized knowledge libraries, the framework decomposes complex channel characteristics into
extensible semantic characterization, thereby better capturing the relationship between environment and channel, and enabling more flexible adjustments of channel models for different events without requiring a complete reset. Specifically, we define channel semantics on three levels: status semantics, behavior semantics, and event semantics, corresponding to channel multipaths, channel time-varying trajectories, and channel topology, respectively. Taking realistic vehicular ISAC scenarios as an example, we perform semantic clustering, characterizing status semantics via multipath statistical distributions, modeling behavior semantics using Markov chains for time variation, and representing event semantics through a co-occurrence matrix. Results show the model accurately generates channels while capturing rich semantic information. Moreover, its generalization supports customized semantics. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_01383 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Channel Semantic Characterization for Integrated Sensing and Communication Scenarios: From Measurements to Modeling Zhang, Zhengyu He, Ruisi Ai, Bo Yang, Mi Zhang, Xuejian Qi, Ziyi Zhong, Zhangdui Signal Processing With the advancement of sixth-generation (6G) wireless communication systems, integrated sensing and communication (ISAC) is crucial for perceiving and interacting with the environment via electromagnetic propagation, termed channel semantics, to support tasks like decision-making. However, channel models focusing on physical characteristics face challenges in representing semantics embedded in the channel, thereby limiting the evaluation of ISAC systems. To tackle this, we present a novel framework for channel modeling from the conceptual event perspective. By leveraging a multi-level semantic structure and characterized knowledge libraries, the framework decomposes complex channel characteristics into extensible semantic characterization, thereby better capturing the relationship between environment and channel, and enabling more flexible adjustments of channel models for different events without requiring a complete reset. Specifically, we define channel semantics on three levels: status semantics, behavior semantics, and event semantics, corresponding to channel multipaths, channel time-varying trajectories, and channel topology, respectively. Taking realistic vehicular ISAC scenarios as an example, we perform semantic clustering, characterizing status semantics via multipath statistical distributions, modeling behavior semantics using Markov chains for time variation, and representing event semantics through a co-occurrence matrix. Results show the model accurately generates channels while capturing rich semantic information. Moreover, its generalization supports customized semantics. |
| title | Channel Semantic Characterization for Integrated Sensing and Communication Scenarios: From Measurements to Modeling |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2503.01383 |