GeNet: A Graph Neural Network-based Anti-noise Task-Oriented Semantic Communication Paradigm

arXiv:2403.18296v1 Announce Type: new
Abstract: 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 …

abstract arxiv communication computational cs.ai cs.lg eess.sp gnn graph graph neural network however knowledge network neural network noise paper paradigm resources semantic signal tasks training type