A CNN-based End-to-End Learning for RIS-assisted Communication System

Reconfigurable intelligent surface (RIS) is an emerging technology that is used to improve the system performance in beyond 5G systems. In this letter, we propose a novel convolutional neural network (CNN)-based autoencoder to jointly optimize the transmitter, the receiver, and the RIS of a RIS-assisted communication system. The proposed system jointly optimizes the sub-tasks of the transmitter, the receiver, and the RIS such as encoding/decoding, channel estimation, phase optimization, and modulation/demodulation. Numerically we have shown that the bit error rate (BER) performance of the CNN-based autoencoder system is better than the theoretical BER performance of the RIS-assisted communication systems.

@article{ginige2025_2503.13976,
  title={ A CNN-based End-to-End Learning for RIS-assisted Communication System },
  author={ Nipuni Ginige and Nandana Rajatheva and Matti Latva-aho },
  journal={arXiv preprint arXiv:2503.13976},
  year={ 2025 }
}