User-Intent-Driven Semantic Communication via Adaptive Deep Understanding
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866911103323209728 |
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| author | Ye, Peigen Duan, Jingpu Du, Hongyang Guo, Yulan |
| author_facet | Ye, Peigen Duan, Jingpu Du, Hongyang Guo, Yulan |
| contents | Semantic communication focuses on transmitting task-relevant semantic information, aiming for intent-oriented communication. While existing systems improve efficiency by extracting key semantics, they still fail to deeply understand and generalize users' real intentions. To overcome this, we propose a user-intention-driven semantic communication system that interprets diverse abstract intents. First, we integrate a multi-modal large model as semantic knowledge base to generate user-intention prior. Next, a mask-guided attention module is proposed to effectively highlight critical semantic regions. Further, a channel state awareness module ensures adaptive, robust transmission across varying channel conditions. Extensive experiments demonstrate that our system achieves deep intent understanding and outperforms DeepJSCC, e.g., under a Rayleigh channel at an SNR of 5 dB, it achieves improvements of 8%, 6%, and 19% in PSNR, SSIM, and LPIPS, respectively. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_05884 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | User-Intent-Driven Semantic Communication via Adaptive Deep Understanding Ye, Peigen Duan, Jingpu Du, Hongyang Guo, Yulan Information Theory Artificial Intelligence Semantic communication focuses on transmitting task-relevant semantic information, aiming for intent-oriented communication. While existing systems improve efficiency by extracting key semantics, they still fail to deeply understand and generalize users' real intentions. To overcome this, we propose a user-intention-driven semantic communication system that interprets diverse abstract intents. First, we integrate a multi-modal large model as semantic knowledge base to generate user-intention prior. Next, a mask-guided attention module is proposed to effectively highlight critical semantic regions. Further, a channel state awareness module ensures adaptive, robust transmission across varying channel conditions. Extensive experiments demonstrate that our system achieves deep intent understanding and outperforms DeepJSCC, e.g., under a Rayleigh channel at an SNR of 5 dB, it achieves improvements of 8%, 6%, and 19% in PSNR, SSIM, and LPIPS, respectively. |
| title | User-Intent-Driven Semantic Communication via Adaptive Deep Understanding |
| topic | Information Theory Artificial Intelligence |
| url | https://arxiv.org/abs/2508.05884 |