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

TL;DR

This paper introduces a multi-user semantic communication framework that leverages generative models and intent-aware knowledge bases to efficiently deliver personalized content in vehicular networks, improving semantic relevance and perceptual quality.

Contribution

It proposes a novel intent-aware semantic-splitting multiple access framework with a shared knowledge base and reinforcement learning optimization for multi-user generative semantic communication.

Findings

Effective personalized image generation using diffusion models.

Improved semantic relevance and perceptual quality demonstrated in simulations.

Robust joint optimization of semantic extraction and beamforming achieved.

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.