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.

翻译：传统语义通信任务方法依赖信噪比知识来缓解信道噪声。然而，这些方法需要在特定信噪比条件下进行训练，消耗大量时间和计算资源。本文提出GeNet——一种基于图神经网络的抗噪声语义通信范式，旨在实现任务导向通信（TOC）。我们提出一种新方法：首先将输入数据图像转换为图结构，然后利用基于GNN的编码器从源数据中提取语义信息，该语义信息随后通过信道传输。在接收端，采用基于GNN的解码器为TOC重构源数据中的相关语义信息。通过实验评估，我们证明了GeNet在解耦信噪比依赖性的同时，在抗噪声TOC中的有效性。进一步通过改变节点数量评估GeNet的性能，揭示了其作为语义通信新范式的多功能性。此外，在不依赖数据增强的情况下，通过不同旋转角度的测试，证明了GeNet对几何变换的鲁棒性。