User-Intent-Driven Semantic Communication via Adaptive Deep Understanding
This work addresses the challenge of intent-oriented communication for users in semantic communication systems, representing an incremental advancement with specific performance gains.
The paper tackles the problem of semantic communication systems failing to deeply understand and generalize users' real intentions by proposing a user-intention-driven system that interprets diverse abstract intents, achieving improvements of 8%, 6%, and 19% in PSNR, SSIM, and LPIPS under a Rayleigh channel at 5 dB SNR compared to DeepJSCC.
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.