In this work, we aim to design and evaluate an autoencoder-based communication model with RIS support, which can adapt to changes in the environment and the receiver’s position. Specifically, we use neural networks to present the encoder and decoder of the system and the parameters of these networks are trained to minimize the reconstruction error at the receiver. |