Multi-User Generative Semantic Communication with Intent-Aware Semantic-Splitting Multiple Access

2025-07-01

AI Summary

To address the challenge of heterogeneous yet highly overlapping user demands in multi-user downlink semantic communication—particularly in vehicular content distribution—this paper proposes an intent-aware semantic segmentation multiple access framework. The method constructs a user-intent-driven shared knowledge base and introduces a semantic efficiency score to precisely decouple and jointly transmit public and private semantic information. It further integrates diffusion models with ControlNet for high-fidelity personalized semantic content generation, and jointly optimizes semantic feature extraction and beamforming via reinforcement learning. Experimental results demonstrate that the proposed framework significantly outperforms baseline approaches, achieving +32.7% improvement in semantic relevance and +28.4% in perceptual similarity. This leads to enhanced semantic transmission efficiency and superior user experience in dynamic vehicular networks.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Search and Optimization: Distributed Search
• Planning, Routing, and Scheduling: Planning with Language Models

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• User Modeling, Personalization and Recommendation: Personalized content ranking and presentation
• Web Mining and Content Analysis: Content-based information diffusion

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.

Problem

• Address diverse user demands in multi-user semantic communication
• Optimize semantic extraction and beamforming for efficient communication
• Enhance image generation quality using ControlNet-augmented diffusion model

Innovation

• Intent-aware shared knowledge base construction
• ControlNet-enhanced diffusion model for image generation
• Reinforcement learning for joint semantic-beamforming optimization