Background Knowledge Aware Semantic Coding Model Selection

Zhao, F., Sun, Y. , Cheng, R. and Imran, M. A. (2023) Background Knowledge Aware Semantic Coding Model Selection. In: 2022 IEEE 22nd International Conference on Communication Technology (ICCT), Nanjing, China, 11-14 Nov 2022, ISBN 9781665470681 (doi: 10.1109/ICCT56141.2022.10072458)

Abstract

Semantic communication is deemed to break Shannon channel capacity by transmitting extracted semantics rather than all binary bits. One critical challenge in semantic communication system is how to select a matching semantic coding model (SCM) in light of complicated source information, diversified user background knowledge (BK) and dynamic wireless channel. In this paper, we mathematically model the relationship among different BKs by using graph theory, and introduce a metric to evaluate SCMs performance as per BK relationships. Then, we propose a Background knowledge Aware SCM SElection (BASE) scheme, where a deep learning algorithm is exploited to accurately predict SCM performance in context of the modeled BK, guiding the SCM selection. Numerical simulation results show that the BASE has superiorities in information recovery accuracy along with the probability of selecting the optimal SCM when compared with other benchmarks.

• Item Type: Conference Proceedings
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Imran, Professor Muhammad and Zhao, Fangzhou and Sun, Dr Yao and CHENG, RUNZE
• Authors: Zhao, F., Sun, Y., Cheng, R., and Imran, M. A.
• College/School: College of Science and Engineering > School of Engineering > Autonomous Systems and Connectivity
• ISSN: 2576-7844
• ISBN: 9781665470681
• Copyright Holders: Copyright © 2023 IEEE
• First Published: First published in Proceedings of the 2022 IEEE 22nd International Conference on Communication Technology (ICCT)
• Publisher Policy: Reproduced under a Creative Commons License

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