Semantic-Native Communication: A Simplicial Complex Perspective
- URL: http://arxiv.org/abs/2210.16970v1
- Date: Sun, 30 Oct 2022 22:33:44 GMT
- Title: Semantic-Native Communication: A Simplicial Complex Perspective
- Authors: Qiyang Zhao, Mehdi Bennis, Merouane Debbah, Daniel Benevides da Costa
- Abstract summary: We study semantic communication from a topological space perspective.
A transmitter first maps its data into a $k$-order simplicial complex and then learns its high-order correlations.
The receiver decodes the structure and infers the missing or distorted data.
- Score: 50.099494681671224
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Semantic communication enables intelligent agents to extract meaning (or
semantics) of information via interaction, to carry out collaborative tasks. In
this paper, we study semantic communication from a topological space
perspective, in which higher-order data semantics live in a simplicial complex.
Specifically, a transmitter first maps its data into a $k$-order simplicial
complex and then learns its high-order correlations. The simplicial structure
and corresponding features are encoded into semantic embeddings in latent space
for transmission. Subsequently, the receiver decodes the structure and infers
the missing or distorted data. The transmitter and receiver collaboratively
train a simplicial convolutional autoencoder to accomplish the semantic
communication task. Experiments are carried out on a real dataset of Semantic
Scholar Open Research Corpus, where one part of the semantic embedding is
missing or distorted during communication. Numerical results show that the
simplicial convolutional autoencoder enabled semantic communication effectively
rebuilds the simplicial features and infer the missing data with $95\%$
accuracy, while achieving stable performance under channel noise. In contrast,
the conventional autoencoder enabled communication fails to infer any missing
data. Moreover, our approach is shown to effectively infer the distorted data
without prior simplicial structure knowledge at the receiver, by learning
extracted semantic information during communications. Leveraging the
topological nature of information, the proposed method is also shown to be more
reliable and efficient compared to several baselines, notably at low
signal-to-noise (SNR) levels.
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