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8 janvier 2022
- handle: 10670/1.8mmp6k
- isbn: 978-1-6654-3161-3
- cea-04549348
- ARXIV: 2110.08049
- doi: 10.1109/CCNC49033.2022.9700645
Ce document est lié à :
info:eu-repo/semantics/altIdentifier/arxiv/2110.08049
Ce document est lié à :
info:eu-repo/semantics/altIdentifier/doi/10.1109/CCNC49033.2022.9700645
Ce document est lié à :
info:eu-repo/grantAgreement//101015956/EU/H2020 project Hexa-X (Grant Agreement no. 101015956)/Hexa-X
info:eu-repo/semantics/OpenAccess
Mots-clés
Representation learning 6G mobile communication Measurement 5G mobile communication Semantics Knowledge based systems ReceiversSujets proches
Communication, Primitive Mass communicationCiter ce document
Mohamed Sana et al., « Learning Semantics: An Opportunity for Effective 6G Communications », HAL-SHS : sciences de l'information, de la communication et des bibliothèques, ID : 10.1109/CCNC49033.2022.9700645
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Résumé
Recently, semantic communications are envisioned as a key enabler of future 6G networks. Back to Shannon’s information theory, the goal of communication has long been to guarantee the correct reception of transmitted messages irrespective of their meaning. However, in general, whenever communication occurs to convey a meaning, what matters is the receiver’s understanding of the transmitted message and not necessarily its correct reconstruction. Hence, semantic communications introduce a new paradigm: transmitting only relevant information sufficient for the receiver to capture the meaning intended can save significant communication bandwidth. Thus, this work explores the opportunity offered by semantic communications for beyond 5G networks. In particular, we focus on the benefit of semantic compression. We refer to semantic message as a sequence of well-formed symbols learned from the "meaning" underlying data, which have to be interpreted at the receiver. This requires a reasoning unit, here artificial, on a knowledge base: a symbolic knowledge representation of the specific application. Therefore, we present and detail a novel architecture that enables representation learning of semantic symbols for effective semantic communications. We first discuss theoretical aspects and successfully design objective functions, which help learn effective semantic encoders and decoders. Eventually, we show promising numerical results for the scenario of text transmission, especially when the sender and receiver speak different languages.