AI-Generated Incentive Mechanism and Full-Duplex Semantic Communications for Information Sharing

This repository hosts a demonstration of the semantic encoder and decoder algorithm as presented in the paper

"AI-Generated Incentive Mechanism and Full-Duplex Semantic Communications for Information Sharing"

Authored by Hongyang Du, Jiacheng Wang, Dusit Niyato, Jiawen Kang, Zehui Xiong, and Dong In Kim, accepted by IEEE JSAC.

The paper can be accessed Here or Arxiv.

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🔧 Environment Setup

To create a new conda environment, execute the following command:

conda create --name sems python==3.7

⚡Activate Environment

Activate the created environment with:

conda activate sems

📦 Install Required Packages

The following packages can be installed using pip:

pip install matplotlib==3.1.3
pip install torch
pip install opencv-python==4.1.2.30
pip install scipy
pip install yacs
pip install torchvision
pip install scikit-image

Download the checkpoints files by refering:

SemSharing\jsr_code\checkpoints\googledown.txt

🏃‍♀️ Run the Program

Run run.py to start the program.

🔍 Check the results

In this demo, we consider that there are two users, whose view images are:

After run the code, several results can be viewed in PyCharm:

For instance, the safe walk area calculated by the first user:

Semantic matching results of two view images:

Another way to show semantic matching results of two view images:

How the second user transforms the view image of the first user to match their own view image:

The safe walk area information that the second user obtains based on the semantic information shared by the first user:

Then, without performing the safe walk area detecing task, the second user can know that the road in front of him/her is safe.

📚 Cite Our Work

Should our code assist in your research, please acknowledge our work by citing:

@article{du2023ai,
  title={{AI}-generated incentive mechanism and full-duplex semantic communications for information sharing},
  author={Du, Hongyang and Wang, Jiacheng and Niyato, Dusit and Kang, Jiawen and Xiong, Zehui and Kim, Dong In},
  journal={IEEE Journal on Selected Areas in Communications},
  year={2023},
  publisher={IEEE}
}

📚 Acknowledgement

As we claimed in our paper, this repository used the codes in the following papers:

JSR-Net: https://github.com/vojirt/JSRNet
SuperPoint: https://github.com/rpautrat/SuperPoint
SuperGlue: https://github.com/magicleap/SuperGluePretrainedNetwork

Please consider to cite these papers if their codes are used in your research.

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For the AI-generated incentive part in the paper, please refer to our tutorial paper: Beyond Deep Reinforcement Learning: A Tutorial on Generative Diffusion Models in Network Optimization and the codes.