.png){kind=icon}
返回
Task-Oriented Explainable Semantic Communications
.png){kind=icon}
OA
AI
DOI：10.1109/TWC.2023.3269444.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Semantic communications utilize the transceiver computing resources to alleviate scarce transmission resources, such as bandwidth and energy. Although the conventional deep learning (DL) based designs may achieve certain transmission efficiency, the uninterpretability issue of extracted features is the major challenge in the development of semantic communications. In this paper, we propose an explainable and robust semantic communication framework by incorporating the well-established bit-level communication system, which not only extracts and disentangles features into independent and semantically interpretable features, but also only selects task-relevant features for transmission, instead of all extracted features. Based on this framework, we derive the optimal input for rate-distortion perception theory, and derive both lower and upper bounds on the semantic channel capacity. Furthermore, based on the beta-variational autoencoder ( beta-VAE), we propose a practical explainable semantic communication system design, which simultaneously achieves semantic features selection and is robust against semantic channel noise. We further design a real-time wireless mobile semantic communication proof-of-concept prototype. Our simulations and experiments demonstrate that our proposed explainable semantic communications system can significantly improve transmission efficiency, and also verify the effectiveness of our proposed robust semantic transmission scheme.
Keyword:
Semantics
Feature extraction
Wireless communication
Task analysis
Electronic mail
Data mining
Communication systems
Explainable semantic communications
feature selection
semantic communications prototype
期刊
IF:
10.7
论文数:
1.3W
被引数:
5.3W
机构
引用论文
A Vision of 6G Wireless Systems: Applications, Trends, Technologies, and Open Research Problems6g无线系统的愿景: 应用，趋势，技术和开放研究问题.png)
IEEE NETWORK
OAAI
IEEE NETWORKIF6.3
OAAI分享收藏Asymmetric hydrogenation using ferrocenylphosphine rhodium(I) cationic complexes使用二茂铁基膦铑 (I) 阳离子配合物进行不对称氢化
PREAI
分享收藏Palladium-catalyzed substitution of esters of naphthylmethanols, 1-naphthylethanols, and analogues by sodium dimethyl malonate. Stereoselective synthesis from enantiomerically pure substratesTetrahedron
TetrahedronIF0
PREAI
分享收藏From Semantic Communication to Semantic-Aware Networking: Model, Architecture, and Open Problems从语义通信到语义感知网络: 模型、体系结构和开放问题OAAI
OAAI分享收藏
查看更多内容