University of Oulu

J. Liang, Y. Xiao, Y. Li, G. Shi and M. Bennis, "Life-long Learning for Reasoning-based Semantic Communication," 2022 IEEE International Conference on Communications Workshops (ICC Workshops), Seoul, Korea, Republic of, 2022, pp. 271-276, doi: 10.1109/ICCWorkshops53468.2022.9814575

Life-long learning for reasoning-based semantic communication

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Author: Liang, Jingming1; Xiao, Yong1,2; Li, Yingyu3;
Organizations: 1School of Elect. Inform. & Commun., Huazhong Univ. of Science & Technology, Wuhan, China
2Pengcheng National Laboratory (Guangzhou base), Guangzhou, China
3School of Mech. Eng. and Elect. Inform., China University of Geosciences, Wuhan, China
4School of Artificial Intelligence, Xidian University, Xi’an, China
5Centre for Wireless Communications, University of Oulu
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 0.3 MB)
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Language: English
Published: Institute of Electrical and Electronics Engineers, 2022
Publish Date: 2023-02-10


Semantic communication is an emerging paradigm that focuses on understanding and delivering semantics or meaning of messages. Most existing semantic communication solutions define semantic meaning as the labels of objects recognized from a given form of source signal, while ignoring intrinsic information that cannot be directly observed. Since the models for recognizing labels need to be pre-trained with labelled dataset, the total number of semantic objects are often limited by a fixed set. In this paper, we propose a novel reasoning-based semantic communication architecture in which the semantic meaning is represented by a graph-based knowledge structure in terms of semantic-entity, relationships, and reasoning rules. An embedding-based semantic interpretation framework is proposed to convert the high-dimensional graph-based representation of semantic meaning into a low-dimensional representation, which is efficient for channel transmission. We develop a novel inference function-based approach that can automatically infer hidden information such as missing entities and relations that cannot be directly observed from the message. Finally, we introduce a life-long model updating approach in which the receiver can learn from previously received messages and automatically update the rules for reasoning the hidden information when new unknown semantic entities and relations have been discovered. Extensive experiments are conducted based on a real-world knowledge database and numerical results show that our proposed solution achieves 76% interpretation accuracy of the hidden meaning at the receiver when some entities are missing in the transmitted message.

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Series: IEEE International Conference on Communications workshop
ISSN: 2164-7038
ISSN-E: 2694-2941
ISSN-L: 2164-7038
ISBN: 978-1-6654-2671-8
ISBN Print: 978-1-6654-2672-5
Pages: 271 - 276
DOI: 10.1109/iccworkshops53468.2022.9814575
Host publication: 2022 IEEE International Conference on Communications Workshops (ICC Workshops)
Conference: IEEE International Conference on Communications Workshops
Type of Publication: A4 Article in conference proceedings
Field of Science: 213 Electronic, automation and communications engineering, electronics
Funding: This work was supported in part by the National Natural Science Foundation of China under Grant No. 61836008 and 62071193, the Pengcheng National Laboratory project under Grant No. PCL2021A12, and the Key R&D Program of Hubei Province of China under Grant No. 2021EHB015 and 2020BAA002.
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