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.
翻译:本文考虑了可重新配置的智能表面(RIS)辅助点对点多输出输出(MIMO)通信系统,在这种系统中,一个发射机通过RIS与接收器通信。根据降低比特误率(BER)并因此提高通信可靠性的主要目标,我们研究了不同的模型和数据驱动(autoencoder)方法。特别是,我们考虑了一种基于模型的方法,优化了主动和被动优化变量。我们进一步提出了一个新的端对端数据驱动框架,利用了机器学习的最新进展。为常规信号处理模块提供的神经网络与频道效应共同接受了培训,以尽量减少比特误检测。数字结果表明,拟议的数据驱动方法可以动态地通过不同频道的实现来学习对传输信号进行编码。此外,数据驱动方法不仅与其他最先进的基准相比,在BER性业绩方面带来重大收益,而且还保证在无法提供完美的频道信息时能够实现。