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arXiv · 2503.13976

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

Nipuni Ginige·Nandana Rajatheva·Matti Latva-aho
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Abstract

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.

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Nipuni Ginige, Nandana Rajatheva, Matti Latva-aho. 2025-03-18. A CNN-based End-to-End Learning for RIS-assisted Communication System. https://arxiv.org/abs/2503.13976

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