# Scalable Extraction Based Semantic Communication for 6G Wireless Networks

arXiv: https://arxiv.org/abs/2407.11334 · HTML: https://mlsanity.com/paper/2407.11334/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Jul 2024.

## Built on by (most cited first, 4 of 4)

- [SC-CDM: Enhancing Quality of Image Semantic Communication with a Compact Diffusion Model](https://mlsanity.com/paper/2410.02121/) (2024)
- [SAFE: Semantic Adaptive Feature Extraction with Rate Control for 6G Wireless Communications](https://mlsanity.com/paper/2410.01597/) (2024)
- [Lossy Cooperative UAV Relaying Networks: Outage Probability Analysis and Location Optimization](https://mlsanity.com/paper/2410.02120/) (2024)
- [Embodied Semantic Communication for Collective Autonomous Agents: A Tutorial on Representation, Wireless Delivery, and Closed-Loop Coordination](https://mlsanity.com/paper/2609.35936/) (2026)

## Abstract (arXiv)

Due to the challenges of satisfying the demands for communication efficiency and intelligent connectivity, sixth-generation (6G) wireless network requires new communication frameworks to enable effective information exchange and the integrated Artificial Intelligence (AI) and communication. The Deep Learning (DL) based semantic communication, which can integrate application requirements and the data meanings into data processing and transmission, is expected to become a new paradigm in 6G wireless networks. However, existing semantic communications frameworks rely on sending full semantic feature, which can maximize the semantic fidelity but fail to achieve the efficient semantic communications. In this article, we introduce a novel Scalable Extraction based Semantic Communication (SE-SC) model to support the potential applications in 6G wireless networks and then analyze its feasibility. Then, we propose a promising the SE-SC framework to highlight the potentials of SE-SC model in 6G wireless networks. Numerical results show that our proposed SE-SC scheme can offer an identical Quality of Service (QoS) for the downstream task with much fewer transmission symbols than the full semantic feature transmission and the traditional codec scheme. Finally, we discuss several challenges for further investigating the scalable extraction based semantic communications.