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pytorch implementation of "Deep Learning-Enabled Semantic Communication Systems with Task-Unaware Transmitter and Dynamic Data"
| Date | Stars |
|---|---|
| 2026-07-31 | 351 |
| 2026-08-02 | 351 |
| 2026-08-06 | 352 |
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# Deep-Learning-Enabled-Semantic-Communication-Systems-with-Task-Unaware-Transmitter-and-Dynamic-Data
pytorch implementation of "Deep Learning-Enabled Semantic Communication Systems with Task-Unaware Transmitter and Dynamic Data"
## Prerequites
* [Python 3.7]
* [PyTorch 0.1.12]
* [Torchvision 0.9.1]
* [Torch 1.5.1]
* [Numpy 1.21.2]
## The folders
["semantic_extraction"](./semantic_extraction) and ["semantic_system_with_DA"](./semantic_system_with_DA) are the semantic extraction part and the data adaptation part of the proposed method in the paper.
The details are represented in the two sub-folders.
## Other Instructions
This is an example of semantic communication using a small-sized dataset based on MLP and CNN.
***If you require a more advanced neural network framework or a system with better performance, we recommend using our [another code repository based on Swin Transformer](https://github.com/SJTU-mxtao/semantic-communication-w-codebook)***
## Citation
Please use the following BibTeX citation if you use this repository in your work:
```
@ARTICLE{9953099,
author={Zhang, Hongwei and Shao, Shuo and Tao, Meixia and Bi, Xiaoyan and Letaief, Khaled B.},
journal={IEEE Journal on Selected Areas in Communications},
title={Deep Learning-Enabled Semantic Communication Systems With Task-Unaware Transmitter and Dynamic Data},
year={2023},
volume={41},
number={1},
pages={170-185},
doi={10.1109/JSAC.2022.3221991}}
```
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matched fp:7d6106bd7ed06c63, llm:Repository description: 'pytorch implementation of "Deep Learning-Enabled Semantic Communication Systems with Task-Unaware Transmitter and Dynamic Data"' (language: Python).
matched fp:7d6106bd7ed06c63, llm:Repository description: 'pytorch implementation of "Deep Learning-Enabled Semantic Communication Systems with Task-Unaware Transmitter and Dynamic Data"' (language: Python).
matched fp:7d6106bd7ed06c63, llm:Repository description: 'pytorch implementation of "Deep Learning-Enabled Semantic Communication Systems with Task-Unaware Transmitter and Dynamic Data"' (language: Python).