RIS-Assisted MIMO Communication Systems: Model-based versus Autoencoder Approaches

TL;DR

This paper compares model-based and autoencoder-based data-driven approaches for RIS-assisted MIMO systems, demonstrating that the neural network framework significantly improves BER performance and robustness to channel uncertainties.

Contribution

It introduces a novel end-to-end neural network framework for RIS-assisted MIMO, outperforming traditional methods in BER and robustness.

Findings

Data-driven approach achieves lower BER than benchmarks.

Neural network adapts to different channel realizations.

Method maintains performance without perfect channel info.

Abstract

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.