###### Abstract  
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.  
intelligent reflecting surface; demodulation; convolutional neural network; MIMO; OFDM 20  
optimization for IRS-based communication. The convolutional neural network (CNN) is a well known DL architecture that can provide effective solutions for different problem domains. In [15], authors proposed a denoising-convolution-neural-network-based channel estimation model for MIMO IRS. The transmitted complex data are received as a noisy image to increase the channel accuracy. The study in [16] proposed a twin CNN for the estimation of the direct channel and the cascade channel for IRS. The CNN network is trained with pilot data and can tolerate a user location of about 4 degrees. Another study in [17] used a CNN-based approach to estimate milimeter wave channel estimation. In addition to channel estimation, CNN can be applied in performance analysis such as bit error rate (BER) or symbol error rate (SER) for IRS-based communication.  
Motivated from above, in this paper we propose a CNN-based ML architecture to improve the BER and SER performance of the MIMO-based IRS system. Orthogonal frequency division multiplexing (OFDM) based pilot symbols are transmitted through a frequency selective fading channel. Both IRS-based cascade channel model and direct channel are considered for communication. A CNN-based demodulation technique was proposed named Demod-CNN (Demodulation using CNN) to improve the channel performance in the case of MIMO communication. The proposed CNN model is trained with received complex data to map into bits for the channel in different signal-to-noise ratio (SNR) values. The trained model was tested with different datasets for performance evaluation.  
The contributions of this study can be listed as:  
* An IRS-based MIMO channel configuration system is considered for wireless communication to test machine learning assisted demodulation.
* A CNN-based demodulation technique Demod-CNN is proposed to demodulate the received signal.  
_Notations_: The lower-case and upper-case boldface letter \(\mathbf{h}\) and \(\mathbf{H}\) represent a vector and matrix, respectively; \(\mathbf{H}^{H}\) denotes the conjugate transpose matrix of \(\mathbf{H}\); \(\text{diag}(\mathbf{x})\) denotes the diagonal matrix having vector \(\mathbf{x}\) on its diagonal; and \(\otimes\) is the Kronecker product.