User-Intent-Driven Semantic Communication via Adaptive Deep Understanding

TL;DR

This paper introduces a user-intention-driven semantic communication system that uses multi-modal knowledge and adaptive modules to deeply understand and efficiently transmit user intents across varying channel conditions.

Contribution

The paper presents a novel system integrating a large semantic knowledge base and adaptive modules for deep user intent understanding in semantic communication.

Findings

Achieves significant improvements in PSNR, SSIM, and LPIPS over DeepJSCC.

Demonstrates robustness across different channel conditions.

Effectively highlights critical semantic regions for better transmission.

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.