Generative diffusion models (GDMs) have recently shown great success in synthesizing multimedia signals with high perceptual quality, enabling highly efficient semantic communications in future wireless networks. In this paper, we develop an intent-aware generative semantic multicasting framework utilizing pre-trained diffusion models. In the proposed framework, the transmitter decomposes the source signal into multiple semantic classes based on the multi-user intent, i.e. each user is assumed to be interested in details of only a subset of the semantic classes. To better utilize the wireless resources, the transmitter sends to each user only its intended classes, and multicasts a highly compressed semantic map to all users over shared wireless resources that allows them to locally synthesize the other classes, namely non-intended classes, utilizing pre-trained diffusion models. The signal retrieved at each user is thereby partially reconstructed and partially synthesized utilizing the received semantic map. We design a communication/computation-aware scheme for per-class adaptation of the communication parameters, such as the transmission power and compression rate, to minimize the total latency of retrieving signals at multiple receivers, tailored to the prevailing channel conditions as well as the users' reconstruction/synthesis distortion/perception requirements. The simulation results demonstrate significantly reduced per-user latency compared with non-generative and intent-unaware multicasting benchmarks while maintaining high perceptual quality of the signals retrieved at the users.

翻译：生成式扩散模型（GDMs）近期在合成高感知质量的多媒体信号方面展现出巨大成功，为未来无线网络中的高效语义通信提供了可能。本文提出了一种利用预训练扩散模型的意图感知生成式语义多播框架。在该框架中，发射机根据多用户意图将源信号分解为多个语义类别，即假设每个用户仅对部分语义类别的细节感兴趣。为更高效利用无线资源，发射机仅向每个用户发送其目标类别，并通过共享无线资源向所有用户多播一份高度压缩的语义地图，使其能够利用预训练扩散模型在本地合成其他类别（即非目标类别）。因此，每个用户获取的信号部分通过接收的语义地图进行重构，部分通过合成生成。我们设计了一种通信/计算感知方案，用于按类别自适应调整通信参数（如发射功率和压缩率），以最小化多接收端获取信号的总延迟。该方案根据当前信道条件以及用户的重构/合成失真/感知需求进行定制。仿真结果表明，与非生成式及无意图感知的多播基准方法相比，所提方案在保持用户端获取信号高感知质量的同时，显著降低了单用户延迟。