# SAFE: Semantic Adaptive Feature Extraction with Rate Control for 6G Wireless Communications

arXiv: https://arxiv.org/abs/2410.01597 · HTML: https://mlsanity.com/paper/2410.01597/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 17 references that are in the map.
- First posted on arXiv 2 Oct 2024.

## Builds on (most cited first, 11 of 11)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Deep Learning Enabled Semantic Communication Systems](https://mlsanity.com/paper/2006.10685/) (2020)
- [Semantic Communication Systems for Speech Transmission](https://mlsanity.com/paper/2102.12605/) (2021)
- [Semantic Communications for Future Internet: Fundamentals, Applications, and Challenges](https://mlsanity.com/paper/2207.00427/) (2022)
- [Nonlinear Transform Source-Channel Coding for Semantic Communications](https://mlsanity.com/paper/2112.10961/) (2021)
- [Bandwidth-Agile Image Transmission with Deep Joint Source-Channel Coding](https://mlsanity.com/paper/2009.12480/) (2020)
- [Deep Joint Source-Channel Coding for Wireless Image Transmission with Adaptive Rate Control](https://mlsanity.com/paper/2110.04456/) (2021)
- [Semantic-Forward Relaying: A Novel Framework Towards 6G Cooperative Communications](https://mlsanity.com/paper/2310.07987/) (2023)
- [DeepJSCC-l++: Robust and Bandwidth-Adaptive Wireless Image Transmission](https://mlsanity.com/paper/2305.13161/) (2023)
- [SemantIC: Semantic Interference Cancellation Towards 6G Wireless Communications](https://mlsanity.com/paper/2310.12768/) (2023)
- [Scalable Extraction Based Semantic Communication for 6G Wireless Networks](https://mlsanity.com/paper/2407.11334/) (2024)

## Abstract (arXiv)

Most current Deep Learning-based Semantic Communication (DeepSC) systems are designed and trained exclusively for particular single-channel conditions, which restricts their adaptability and overall bandwidth utilization. To address this, we propose an innovative Semantic Adaptive Feature Extraction (SAFE) framework, which significantly improves bandwidth efficiency by allowing users to select different sub-semantic combinations based on their channel conditions. This paper also introduces three advanced learning algorithms to optimize the performance of SAFE framework as a whole. Through a series of simulation experiments, we demonstrate that the SAFE framework can effectively and adaptively extract and transmit semantics under different channel bandwidth conditions, of which effectiveness is verified through objective and subjective quality evaluations.