GeNet: A Graph Neural Network-based Anti-noise Task-Oriented Semantic
  Communication Paradigm
Chunhang Zheng Kechao Cai
Traditional approaches to semantic communication tasks rely on the knowledge
of the signal-to-noise ratio (SNR) to mitigate channel noise. However, these
methods necessitate training under specific SNR conditions, entailing
considerable time and computational resources. In this paper, we propose GeNet,
a Graph Neural Network (GNN)-based paradigm for semantic communication aimed at
combating noise, thereby facilitating Task-Oriented Communication (TOC). We
propose a novel approach where we first transform the input data image into
graph structures. Then we leverage a GNN-based encoder to extract semantic
information from the source data. This extracted semantic information is then
transmitted through the channel. At the receiver's end, a GNN-based decoder is
utilized to reconstruct the relevant semantic information from the source data
for TOC. Through experimental evaluation, we show GeNet's effectiveness in
anti-noise TOC while decoupling the SNR dependency. We further evaluate GeNet's
performance by varying the number of nodes, revealing its versatility as a new
paradigm for semantic communication. Additionally, we show GeNet's robustness
to geometric transformations by testing it with different rotation angles,
without resorting to data augmentation.