University of Oulu

H. Seo, J. Park, M. Bennis and M. Debbah, "Semantics-Native Communication via Contextual Reasoning," in IEEE Transactions on Cognitive Communications and Networking, vol. 9, no. 3, pp. 604-617, June 2023, doi: 10.1109/TCCN.2023.3250206

Semantics-native communication via contextual reasoning

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Author: Seo, Hyowoon1; Park, Jihong2; Bennis, Mehdi3;
Organizations: 1Department of Electronics and Communications Engineering, Kwangwoon University, Seoul 01897, Korea
2School of Information Technology, Deakin University, Geelong, VIC, Australia
3Centre for Wireless Communications, University of Oulu, Oulu 90014, Finland
4AI and Digital Science Research Center, Technology Innovation Institute, Abu Dhabi, UAE,Centrale Supelec, University Paris-Saclay, Gif-sur-Yvette, France
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 1.5 MB)
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Language: English
Published: IEEE Communications Society, 2023
Publish Date: 2023-09-13


Recently, machine learning (ML) has shown its effectiveness in improving communication efficiency by reinstating the semantics of bits. To understand its underlying principles, we propose a novel stochastic model of semantic communication, dubbed semantics-native communication (SNC). Inspired from human communication, we consider a point-to-point SNC scenario where a speaker has an intention of referring to an entity, extracts its semantic concepts, and maps these concepts into symbols that are communicated to a target listener through the traditional Shannon communication channel. Next, we recall rational humans who can communicate more efficiently by reasoning about the others’ contexts before communication, referred to as contextual reasoning. This motivates us to propose a novel SNC framework harnessing agent states as side information in a way that the speaker locally and iteratively communicates with a virtual agent having the listener’s state, and vice versa. Theoretically, we prove the convergence of contextual reasoning, at which it minimizes the bit length while guaranteeing a target reliability. Simulation results corroborate that contextual reasoning based SNC can significantly reduce bit lengths, and be a robust solution to imperfect agent states by quantizing the entity-concept-symbol mapping before contextual reasoning.

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Series: IEEE transactions on cognitive communications and networking
ISSN: 2372-2045
ISSN-E: 2332-7731
ISSN-L: 2372-2045
Volume: 9
Issue: 3
Pages: 604 - 617
DOI: 10.1109/TCCN.2023.3250206
Type of Publication: A1 Journal article – refereed
Field of Science: 213 Electronic, automation and communications engineering, electronics
Funding: This work was supported in parts by the Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT under Grant 2021M1B3A3102358 and 2022R1F1A1075078; in part by the Research Grant of Kwangwoon University in 2022; in part by EU-CHISTERA LeadingEDGE; in part by CONNECT and project SCR6GE.
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