标题： 具有意图感知语义分割多址接入的多用户生成语义通信 显示英文标题

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

摘要： 随着生成式人工智能（GAI）的蓬勃发展，语义通信（SemCom）作为一种可靠且高效的通信新范式应运而生。 本文考虑了一个多用户下行SemCom系统，以车联网作为多用户内容分发的典型场景。 为应对多样化但又重叠的用户需求，我们提出了一种多用户生成式SemCom增强的意图感知语义分割多址接入（SS-MGSC）框架。 在该框架中，我们构建了一个意图感知的共享知识库（SKB），该知识库融合了语义信息（SI）的先验知识和用户特定的偏好。 随后，我们将公共SI设为一个独热语义图，广播给所有用户，而私有SI则作为个性化文本分别发送给每个用户。 在接收端，采用一种结合ControlNet的扩散模型来生成高质量的个性化图像。 为了捕捉语义相关性和感知相似性，我们设计了一种新的语义效率得分（SES）指标作为优化目标。 在此基础上，我们制定了一个多用户语义提取和波束成形的联合优化问题，并由于其在高维环境中的鲁棒性，采用基于强化学习的算法进行求解。 仿真结果证明了所提出方案的有效性。 显示英文摘要

摘要： 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.