DL-Beamforming-RIS-ISAC

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DL-Beamforming-RIS-ISAC

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This repository contains the source codes in ``Unsupervised Learning for Joint Beamforming Design in RIS-aided ISAC Systems'' to be published, which proposes an unsupervised learning-based beamforming design in a RIS-aided ISAC system.

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# Unsupervised-Learning-Joint-Beamforming-Design-RIS-ISAC
This repository contains the source codes for ``Unsupervised Learning for Joint Beamforming Design in RIS-aided ISAC Systems'' accepted by IEEE Wireless Communications Letters.

The paper of the work can be found in the link "https://ieeexplore.ieee.org/document/10533223"

The code is run by GPU. It is suggested implementing the CUDA-base pytorch framework to speed up the training. If no GPU available, one can modify the second paragraph to be device='cpu' to run the code in CPU.

If the readers have any questions about the code, feel free to contact "julianye953@163.com".

If you found the code is useful for your research, please cite this paper:

@ARTICLE{10533223,
  author={Ye, Junjie and Huang, Lei and Chen, Zhen and Zhang, Peichang and Rihan, Mohamed},
  journal={IEEE Wireless Communications Letters}, 
  title={Unsupervised Learning for Joint Beamforming Design in RIS-Aided ISAC Systems}, 
  year={2024},
  volume={},
  number={},
  pages={1-1},
  keywords={Array signal processing;Signal to noise ratio;Sensors;Reconfigurable intelligent surfaces;Unsupervised learning;Correlation;Communication channels;ISAC;RIS;beamforming design;lightweight network;unsupervised learning},
  doi={10.1109/LWC.2024.3402235}}

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