{"ID":2885455,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.05884","arxiv_id":"2508.05884","title":"User-Intent-Driven Semantic Communication via Adaptive Deep Understanding","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.","short_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-dr...","url_abs":"https://arxiv.org/abs/2508.05884","url_pdf":"https://arxiv.org/pdf/2508.05884v1","authors":"[\"Peigen Ye\",\"Jingpu Duan\",\"Hongyang Du\",\"Yulan Guo\"]","published":"2025-08-07T22:26:27Z","proceeding":"cs.IT","tasks":"[\"cs.IT\",\"cs.AI\"]","methods":"[]","has_code":false}