  It is critical to design efficient beamforming in reconfigurable intelligent
surface (RIS)-aided integrated sensing and communication (ISAC) systems for
enhancing spectrum utilization. However, conventional methods often have
limitations, either incurring high computational complexity due to iterative
algorithms or sacrificing performance when using heuristic methods. To achieve
both low complexity and high spectrum efficiency, an unsupervised
learning-based beamforming design is proposed in this work. We tailor
image-shaped channel samples and develop an ISAC beamforming neural network
(IBF-Net) model for beamforming. By leveraging unsupervised learning, the loss
function incorporates key performance metrics like sensing and communication
channel correlation and sensing channel gain, eliminating the need of labeling.
Simulations show that the proposed method achieves competitive performance
compared to benchmarks while significantly reduces computational complexity.
