Abstract
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 to 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. The transmitter then 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, i.e. 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. This improves utilization of the wireless resources, with better preserving privacy of the non-intended classes. 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.
Abstract (translated)
生成扩散模型（GDMs）最近在以高感知质量合成多媒体信号方面取得了巨大成功，这使得未来无线网络中能够实现高效的语义通信。本文我们开发了一个基于意图感知的生成性语义多播框架，利用预训练的扩散模型。在提出的框架中，发射器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。然后，发射器只为每个用户提供其感兴趣的类别，并通过共享无线资源向所有用户多播高度压缩的语义图，使它们能够利用预训练的扩散模型在当地合成非目标类别。因此，在每个用户端检索到的信号是部分重构和部分合成的结果，利用接收到的语义图进行操作。这提高了无线资源的利用率，并更好地保护了非目标类别的隐私。我们设计了一种通信/计算感知方案，用于根据不同的传播参数（如传输功率和压缩率）对每个类别进行调整，以在现有的信道条件下以及用户重构/合成失真/感知需求下最小化多接收器检索信号的总延迟。仿真结果表明，与非生成性和无意图感知的多播基准相比，在保持高感知质量的同时显著降低了每用户的延迟。
URL
https://arxiv.org/abs/2411.02334
