Multi-User Generative Semantic Communication with Intent-Aware Semantic-Splitting Multiple Access
Journal:
arXiv
Published Date:
Jul 2, 2025
Abstract
With the booming development of generative artificial intelligence (GAI),
semantic communication (SemCom) has emerged as a new paradigm for reliable and
efficient communication. This paper considers a multi-user downlink SemCom
system, using vehicular networks as the representative scenario for multi-user
content dissemination. To address diverse yet overlapping user demands, we
propose a multi-user Generative SemCom-enhanced intent-aware semantic-splitting
multiple access (SS-MGSC) framework. In the framework, we construct an
intent-aware shared knowledge base (SKB) that incorporates prior knowledge of
semantic information (SI) and user-specific preferences. Then, we designate the
common SI as a one-hot semantic map that is broadcast to all users, while the
private SI is delivered as personalized text for each user. On the receiver
side, a diffusion model enhanced with ControlNet is adopted to generate
high-quality personalized images. To capture both semantic relevance and
perceptual similarity, we design a novel semantic efficiency score (SES) metric
as the optimization objective. Building on this, we formulate a joint
optimization problem for multi-user semantic extraction and beamforming, solved
using a reinforcement learning-based algorithm due to its robustness in
high-dimensional settings. Simulation results demonstrate the effectiveness of
the proposed scheme.
