Traditional approaches to semantic communication tasks rely on the knowledge of the signal-to-noise ratio (SNR) to mitigate channel noise. Moreover, 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：传统语义通信任务常依赖信噪比知识来抑制信道噪声，且需在特定信噪比条件下训练，消耗大量时间与计算资源。本文提出GeNet——一种基于图神经网络的抗噪语义通信范式，旨在实现任务导向通信。我们创新性地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，并通过信道传输。接收端采用基于GNN的解码器为任务导向通信重建源数据中的相关语义信息。实验评估表明，GeNet在解耦信噪比依赖性的同时，展现出抗噪任务导向通信的有效性。通过改变节点数量进一步评估GeNet性能，揭示了其作为语义通信新范式的通用性。此外，无需数据增强即可在不同旋转角度测试中验证GeNet对几何变换的鲁棒性。