The intelligent reflecting surface (IRS) is a novel and innovative communication technology that aims at the control of the wireless environment. The IRS is considered as a promising technology for sixth-generation wireless communication. In the last few years, machine learning has emerged as a powerful tool for solving complex problems in diverse application areas. In this paper, we propose a convolutional neural network (CNN)-based demodulation technique called Demod-CNN in IRS-based wireless communication for multiple users. A multiple-input multiple-output based orthogonal multiple frequency division multiplexing system is considered for channel modeling. The received signal data are used for training and testing the model. The simulation results show that the proposed model performs better than the conventional demodulation technique.

中文翻译：

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Demod-CNN：一种用于智能反射表面辅助多用户 MIMO 通信的稳健深度学习方法

智能反射面（IRS）是一种针对无线环境控制的新颖创新的通信技术。IRS 被认为是用于第六代无线通信的有前途的技术。在过去的几年里，机器学习已经成为解决不同应用领域复杂问题的强大工具。在本文中，我们在基于 IRS 的多用户无线通信中提出了一种基于卷积神经网络 (CNN) 的解调技术，称为 Demod-CNN。基于多输入多输出的正交多频分复用系统被考虑用于信道建模。接收到的信号数据用于训练和测试模型。仿真结果表明，所提出的模型比传统的解调技术性能更好。