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}}