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arXiv · 2508.05884

User-Intent-Driven Semantic Communication via Adaptive Deep Understanding

Peigen Ye·Jingpu Duan·Hongyang Du·Yulan Guo
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Abstract

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.

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Peigen Ye, Jingpu Duan, Hongyang Du, Yulan Guo. 2025-08-07. User-Intent-Driven Semantic Communication via Adaptive Deep Understanding. https://arxiv.org/abs/2508.05884

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