{"ID":5438776,"CreatedAt":"2026-07-01T01:17:58.482524686Z","UpdatedAt":"2026-07-03T09:43:49.071287852Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2606.31426","arxiv_id":"2606.31426","title":"Towards a Joint Task-Oriented and Generative Semantic Communication Framework for 6G Networks","abstract":"Semantic Communication (SC) has emerged as a key enabler for 6G wireless systems by transmitting task-relevant meaning rather than raw data, thereby significantly reducing bandwidth consumption while preserving communication intent. In this work, we propose an end-to-end OFDM-based semantic communication framework that integrates a semantic encoder-decoder pipeline with a neural receiver operating over a 3GPP vehicular channel. The semantic encoder extracts the underlying meaning of a visual scene by transforming it into a graph-based representation consisting of object-level features and relational structure. At the receiver, the reconstructed scene graph is processed by a spatio-temporal graph neural network (ST-GNN)-based module for collision-risk estimation, enabling task-oriented inference. In parallel, a diffusion-based semantic decoder reconstructs the visual scene from the recovered semantics, providing dual functionality: safety prediction and image reconstruction. The proposed framework is evaluated in a MIMO configuration under varying SNR conditions. Experimental results show that it achieves up to 99.1% data compression relative to pixel-domain transmission, outperforming conventional compression-based methods (JPEG and HEVC) while preserving downstream inference performance. Furthermore, the diffusion-based reconstruction attains significantly lower frechet inception distance (FID) scores than existing semantic communication approaches, reflecting superior semantic and perceptual fidelity.","short_abstract":"Semantic Communication (SC) has emerged as a key enabler for 6G wireless systems by transmitting task-relevant meaning rather than raw data, thereby significantly reducing bandwidth consumption while preserving communication intent. In this work, we propose an end-to-end OFDM-based semantic communication framework that...","url_abs":"https://arxiv.org/abs/2606.31426","url_pdf":"https://arxiv.org/pdf/2606.31426v1","authors":"[\"Soheyb Ribouh\",\"Phil Polo Ditsia Di Ngoma\"]","published":"2026-06-30T09:49:30Z","proceeding":"eess.SP","tasks":"[\"eess.SP\"]","methods":"[\"Graph Neural Network\",\"Diffusion Model\"]","has_code":false}