基于基础模型的生成式语义通信：感知误差分析与语义感知功率分配
Generative Semantic Communications with Foundation Models: Perception-Error Analysis and Semantic-Aware Power Allocation
AI总结：
本文提出搭载预训练基础模型的生成式语义通信框架，分析感知-误差的非递减关系，定义语义值，提出两种语义感知功率分配方法，在图像任务仿真中较传统方法最高节省90%功耗。
摘要：
生成式基础模型可革新语义通信（SemCom）系统的设计，实现超低速下语义信息的高保真交互。本文提出一种搭载预训练基础模型的生成式SemCom框架，为语义解码器设计了未编码前向纠错和编码丢弃纠错两种方案。为表征传输可靠性对再生信号感知质量的影响，从率-失真-感知角度分析了二者的数学关系，并证明该关系具有非递减特性。据此定义了语义值，用于衡量多模态语义特征的语义信息量。本文还研究了面向超低速高保真SemCom的、以功耗最小化为目标的语义感知功率分配问题。利用感知-误差关系的非递减特性，提出了两种语义感知功率分配方法求解该问题。数值实验基于Kodak数据集，得到了图像任务下两种方案中语义数据流的感知-误差函数和语义值。仿真结果表明，所提出的语义感知方法显著优于传统方法，在信道编码场景下效果尤为突出，最高可节省90%的功耗。
英文摘要：
Generative foundation models can revolutionize the design of semantic communication (SemCom) systems allowing high fidelity exchange of semantic information at ultra low rates. In this work, a generative SemCom framework with pretrained foundation models is proposed, where both uncoded forward-with-error and coded discard-with-error schemes are developed for the semantic decoder. To characterize the impact of transmission reliability on the perceptual quality of the regenerated signal, their mathematical relationship is analyzed from a rate-distortion-perception perspective, which is proved to be non-decreasing. The semantic values are defined to measure the semantic information of multimodal semantic features accordingly. We also investigate semantic-aware power allocation problems aiming at power consumption minimization for ultra low rate and high fidelity SemComs. To solve these problems, two semantic-aware power allocation methods are proposed by leveraging the non-decreasing property of the perception-error relationship. Numerically, perception-error functions and semantic values of semantic data streams under both schemes for image tasks are obtained based on the Kodak dataset. Simulation results show that our proposed semanticaware method significantly outperforms conventional approaches, particularly in the channel-coded case (up to 90% power saving).
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