Learning Semantics: An Opportunity for Effective 6G Communications
- URL: http://arxiv.org/abs/2110.08049v1
- Date: Thu, 14 Oct 2021 08:00:54 GMT
- Title: Learning Semantics: An Opportunity for Effective 6G Communications
- Authors: Mohamed Sana and Emilio Calvanese Strinati
- Abstract summary: semantic communications are envisioned as a key enabler of future 6G networks.
This work explores the opportunity offered by semantic communications for beyond 5G networks.
We present and detail a novel architecture that enables representation learning of semantic symbols for effective semantic communications.
- Score: 8.262718096663077
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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.
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