A CNN-based End-to-End Learning for RIS-assisted Communication System
This work addresses performance improvement in beyond 5G systems for communication engineers, but it appears incremental as it applies a known deep learning method to a specific domain.
The authors tackled the problem of optimizing multiple components in a RIS-assisted communication system by proposing a CNN-based autoencoder that jointly optimizes the transmitter, receiver, and RIS, resulting in a bit error rate performance better than theoretical benchmarks.
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.