Learning Semantics: An Opportunity for Effective 6G Communications

TL;DR

This paper explores semantic communications for 6G networks, proposing a novel architecture for semantic encoding and decoding that enhances bandwidth efficiency by transmitting meaning rather than raw data.

Contribution

It introduces a new architecture for semantic representation learning and designs objective functions for effective semantic encoding and decoding in communication systems.

Findings

Promising results in text transmission scenarios

Effective semantic compression reduces bandwidth usage

Improved communication between speakers of different languages

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.