让通信懂用户真实意图，提升传输效率与适应性
User-Intent-Driven Semantic Communication via Adaptive Deep Understanding
- 用大模型生成用户意图先验，理解抽象需求
- 在5dB信噪比下，图像质量提升8%~19%
- 适合需要精准理解用户意图的智能通信场景
语义通信旨在传输与任务相关的语义信息，实现以意图为中心的通信。现有系统虽通过提取关键语义提升了效率，但仍难以深入理解并泛化用户真实意图。为此，我们提出一种用户意图驱动的语义通信系统，能够解析多样化的抽象意图。首先，引入多模态大模型作为语义知识库，生成用户意图先验；其次，提出掩码引导注意力模块，有效突出关键语义区域；进一步，设计信道状态感知模块，确保在不同信道条件下自适应、鲁棒地传输。大量实验表明，该系统实现了深层意图理解，性能优于DeepJSCC：在瑞利信道、5dB信噪比下，峰值信噪比（PSNR）、结构相似性（SSIM）和学习感知图像像素相似度（LPIPS）分别提升8%、6%和19%。
原文摘要 · Abstract (English)
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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