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.

提出了一种基于图神经网络的语义通信范式 GeNet，旨在抵抗噪声，从而促进面向任务的通信（TOC）。通过将输入数据图像转换为图结构，利用基于 GNN 的编码器从源数据中提取语义信息，然后通过信道传输。接收端利用基于 GNN 的解码器从源数据中重构相关的语义信息以实现 TOC。通过实验评估表明，GeNet 在抗噪声的 TOC 中具有有效性，并解耦了 SNR 的依赖性。通过改变节点数量进一步评估了 GeNet 的性能，展示了其作为语义通信的新范式的多用途性。此外，通过在不依赖数据增强的情况下测试具有不同旋转角度的几何变换，证明了 GeNet 的鲁棒性。