Extended Blahut-Arimoto Algorithm for Semantic Rate-Distortion Function.

IF 2.1 3区 物理与天体物理 Q2 PHYSICS, MULTIDISCIPLINARY
Entropy Pub Date : 2025-06-18 DOI:10.3390/e27060651
Yuxin Han, Yang Liu, Yaping Sun, Kai Niu, Nan Ma, Shuguang Cui, Ping Zhang
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Abstract

Semantic communication has recently gained significant attention in theoretical analysis due to its potential to improve communication efficiency by focusing on meaning rather than exact signal reconstruction. In this paper, we extend the Blahut-Arimoto (BA) algorithm, a fundamental method in classical information theory (CIT) for computing the rate-distortion (RD) function, to semantic communication by proposing the extended Blahut-Arimoto (EBA) algorithm, which iteratively updates transition and reconstruction distributions to calculate the semantic RD function based on synonymous mapping in semantic information theory (SIT). To address scenarios where synonymous mappings are unknown, we develop an optimization framework that combines the EBA algorithm with simulated annealing. Initialized with a syntactic mapping, the framework progressively merges syntactic symbols and identifies the mapping with a maximum synonymous number that satisfies objective constraints. Furthermore, by considering the semantic knowledge base (SKB) as a specific instance of synonymous mapping, the EBA algorithm provides a theoretical approach for analyzing and predicting the SKB size. Numerical results validate the effectiveness of the EBA algorithm. For Gaussian sources, the semantic RD function decreases with an increasing synonymous number and becomes significantly lower than its classical counterpart. Additionally, analysis on the CUB dataset demonstrates that larger SKB sizes lead to higher semantic communication compression efficiency.

语义率失真函数的扩展Blahut-Arimoto算法。
语义交际通过关注意义而不是精确的信号重建来提高交际效率,近年来在理论分析中受到了极大的关注。本文将经典信息论(CIT)中计算率失真(RD)函数的基本方法Blahut-Arimoto (BA)算法扩展到语义通信中,提出了扩展的Blahut-Arimoto (EBA)算法,该算法迭代更新迁移和重构分布,以计算基于语义信息论(SIT)中同义映射的语义RD函数。为了解决同义映射未知的情况,我们开发了一个将EBA算法与模拟退火相结合的优化框架。该框架以语法映射初始化,逐步合并语法符号,并使用满足客观约束的最大同义数识别映射。此外,EBA算法将语义知识库(SKB)作为同义映射的具体实例,为分析和预测语义知识库的大小提供了理论途径。数值结果验证了EBA算法的有效性。对于高斯源,语义RD函数随着同义数的增加而减小,并明显低于其经典对应函数。此外,对CUB数据集的分析表明,更大的SKB大小导致更高的语义通信压缩效率。
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来源期刊
Entropy
Entropy PHYSICS, MULTIDISCIPLINARY-
CiteScore
4.90
自引率
11.10%
发文量
1580
审稿时长
21.05 days
期刊介绍: Entropy (ISSN 1099-4300), an international and interdisciplinary journal of entropy and information studies, publishes reviews, regular research papers and short notes. Our aim is to encourage scientists to publish as much as possible their theoretical and experimental details. There is no restriction on the length of the papers. If there are computation and the experiment, the details must be provided so that the results can be reproduced.
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