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

arXiv: https://arxiv.org/abs/2503.13976 · HTML: https://mlsanity.com/paper/2503.13976/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 9 references that are in the map.
- First posted on arXiv 18 Mar 2025.

## Builds on (most cited first, 7 of 7)

- [DeepRx: Fully Convolutional Deep Learning Receiver](https://mlsanity.com/paper/2005.01494/) (2020)
- [Wireless Communications Through Reconfigurable Intelligent Surfaces](https://mlsanity.com/paper/1906.09490/) (2019)
- [OFDM-Autoencoder for End-to-End Learning of Communications Systems](https://mlsanity.com/paper/1803.05815/) (2018)
- [Deep MIMO Detection](https://mlsanity.com/paper/1706.01151/) (2017)
- [Massive MIMO Channel Estimation with an Untrained Deep Neural Network](https://mlsanity.com/paper/1908.00144/) (2019)
- [Unsupervised Learning for Passive Beamforming](https://mlsanity.com/paper/2001.02348/) (2020)
- [Untrained DNN for Channel Estimation of RIS-Assisted Multi-User OFDM System with Hardware Impairments](https://mlsanity.com/paper/2107.07423/) (2021)

## Abstract (arXiv)

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.