  This paper considers reconfigurable intelligent surface (RIS)-assisted
point-to-point multiple-input multiple-output (MIMO) communication systems,
where a transmitter communicates with a receiver through an RIS. Based on the
main target of reducing the bit error rate (BER) and therefore enhancing the
communication reliability, we study different model-based and data-driven
(autoencoder) approaches. In particular, we consider a model-based approach
that optimizes both active and passive optimization variables. We further
propose a novel end-to-end data-driven framework, which leverages the recent
advances in machine learning. The neural networks presented for conventional
signal processing modules are jointly trained with the channel effects to
minimize the bit error detection. Numerical results demonstrate that the
proposed data-driven approach can learn to encode the transmitted signal via
different channel realizations dynamically. In addition, the data-driven
approach not only offers a significant gain in the BER performance compared to
the other state-of-the-art benchmarks but also guarantees the performance when
perfect channel information is unavailable.
