A CNN-based End-to-End Learning for RIS-assisted Communication System
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866913742681276416 |
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| author | Ginige, Nipuni Rajatheva, Nandana Latva-aho, Matti |
| author_facet | Ginige, Nipuni Rajatheva, Nandana Latva-aho, Matti |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2503_13976 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A CNN-based End-to-End Learning for RIS-assisted Communication System Ginige, Nipuni Rajatheva, Nandana Latva-aho, Matti Machine Learning 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. |
| title | A CNN-based End-to-End Learning for RIS-assisted Communication System |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2503.13976 |