User-Intent-Driven Semantic Communication via Adaptive Deep Understanding

2025/08/07 by Ye, Peigen, Duan, Jingpu, Du, Hongyang +1
#Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Information Theory (cs.IT)

paper · doi:10.48550/arxiv.2508.05884

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.

Citations

• Semantic Importance-Aware Communications with Semantic Correction Using Large Language Models
• Codebook-enabled Generative End-to-end Semantic Communication Powered by Transformer
• MOC-RVQ: Multilevel Codebook-Assisted Digital Generative Semantic Communication
• Large AI Model Empowered Multimodal Semantic Communications
• Large AI Model Empowered Multimodal Semantic Communications
• LISA: Reasoning Segmentation via Large Language Model
• Visual Instruction Tuning
• Micrograph segmentations for DDEVD
• Segment Anything
• Deep Learning Enabled Semantic Communications with Speech Recognition and Synthesis
• Contextual Transformer Networks for Visual Recognition
• LoRA: Low-Rank Adaptation of Large Language Models
• Microsoft COCO: Common Objects in Context

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