基于基础模型的生成式语义通信的语义感知功率分配
Semantic-Aware Power Allocation for Generative Semantic Communications with Foundation Models
AI总结：
本文提出基于基础模型的生成式语义通信框架，建立传输可靠性与图像感知质量及语义价值的关系模型，并通过约束解耦和二分搜索设计语义感知功率分配方法，在保证语义性能的同时显著降低总功率消耗。
摘要：
近年来，扩散模型的进展在生成建模方面取得了重大突破。生成模型与语义通信（SemCom）的结合使得在超低速率下实现高保真语义信息交换成为可能。本文提出了一种用于图像任务的新型生成式语义通信框架，其中预训练的基础模型分别作为语义编码器和解码器，用于语义特征提取和图像再生。本文对传输可靠性与再生图像的感知质量以及语义特征的语义价值之间的数学关系进行了建模，这些关系通过在Kodak数据集上进行数值仿真获得。我们还研究了语义感知功率分配问题，目标是在保证语义性能的同时最小化总功率消耗。为解决该问题，分别通过约束解耦和二分搜索提出了两种语义感知功率分配方法。数值结果表明，所提出的语义感知方法在总功率消耗方面相比传统方法表现出优越的性能。
英文摘要：
Recent advancements in diffusion models have made a significant breakthrough in generative modeling. The combination of the generative model and semantic communication (SemCom) enables high-fidelity semantic information exchange at ultra-low rates. A novel generative SemCom framework for image tasks is proposed, wherein pre-trained foundation models serve as semantic encoders and decoders for semantic feature extractions and image regenerations, respectively. The mathematical relationship between the transmission reliability and the perceptual quality of the regenerated image and the semantic values of semantic features are modeled, which are obtained by conducting numerical simulations on the Kodak dataset. We also investigate the semantic-aware power allocation problem, with the objective of minimizing the total power consumption while guaranteeing semantic performance. To solve this problem, two semanticaware power allocation methods are proposed by constraint decoupling and bisection search, respectively. Numerical results show that the proposed semantic-aware methods demonstrate superior performance compared to the conventional one in terms of total power consumption.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
