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6G networks: Beyond Shannon towards semantic and goal-oriented communications

Calvanese Strinati, Emilio ; Barbarossa, Sergio

Computer networks (Amsterdam, Netherlands : 1999), 2021-05, Vol.190, p.107930, Article 107930 [Periódico revisado por pares]

Amsterdam: Elsevier B.V

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  • Título:
    6G networks: Beyond Shannon towards semantic and goal-oriented communications
  • Autor: Calvanese Strinati, Emilio ; Barbarossa, Sergio
  • Assuntos: 6G mobile communication ; Algorithms ; Beyond 5G ; Computer Science ; Engineering Sciences ; Goal oriented communications ; Green communications ; Knowledge representation ; Machine learning ; MEC ; Networking and Internet Architecture ; Networks ; Semantic communications ; Semantic learning ; Semantics ; Sustainability ; System effectiveness
  • É parte de: Computer networks (Amsterdam, Netherlands : 1999), 2021-05, Vol.190, p.107930, Article 107930
  • Descrição: The goal of this paper is to promote the idea that including semantic and goal-oriented aspects in future 6G networks can produce a significant leap forward in terms of system effectiveness and sustainability. Semantic communication goes beyond the common Shannon paradigm of guaranteeing the correct reception of each single transmitted bit, irrespective of the meaning conveyed by the transmitted bits. The idea is that, whenever communication occurs to convey meaning or to accomplish a goal, what really matters is the impact that the received bits have on the interpretation of the meaning intended by the transmitter or on the accomplishment of a common goal. Focusing on semantic and goal-oriented aspects, and possibly combining them, helps to identify the relevant information, i.e. the information strictly necessary to recover the meaning intended by the transmitter or to accomplish a goal. Combining knowledge representation and reasoning tools with machine learning algorithms paves the way to build semantic learning strategies enabling current machine learning algorithms to achieve better interpretation capabilities and contrast adversarial attacks. 6G semantic networks can bring semantic learning mechanisms at the edge of the network and, at the same time, semantic learning can help 6G networks to improve their efficiency and sustainability.
  • Editor: Amsterdam: Elsevier B.V
  • Idioma: Inglês
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  • Realização: ABCD-USP USP   Logos de Redes Sociais: Freepik

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