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

• URL: http://arxiv.org/abs/2503.13976v1
• Date: Tue, 18 Mar 2025 07:24:55 GMT
• Title: A CNN-based End-to-End Learning for RIS-assisted Communication System
• Authors: Nipuni Ginige, Nandana Rajatheva, Matti Latva-aho,
• Abstract summary: We propose a novel CNN-based autoencoder to jointly optimize the transmitter, the receiver, and the RIS of a RIS-assisted communication system.<n> 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.
• Score: 10.177301687464238
• License: http://creativecommons.org/licenses/by/4.0/
• 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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