Demod-CNN: A Robust Deep Learning Approach for Intelligent Reflecting Surface-Assisted Multiuser MIMO Communication

Area of Science:

• Wireless Communication
• Signal Processing
• Machine Learning

Background:

• Intelligent Reflecting Surface (IRS) is a key technology for future wireless networks.
• Machine learning offers powerful solutions for complex communication challenges.
• Existing demodulation techniques may lack efficiency in advanced wireless systems.

Purpose of the Study:

• To propose a novel machine learning-based demodulation technique for IRS-enabled wireless communication.
• To enhance demodulation performance in multi-user, multiple-input multiple-output (MIMO) systems.
• To leverage Convolutional Neural Networks (CNNs) for intelligent signal processing in wireless environments.

Main Methods:

• A Convolutional Neural Network (CNN) based demodulation technique, named Demod-CNN, was developed.
• A Multiple-Input Multiple-Output Orthogonal Multiple Frequency Division Multiplexing (MIMO-OFDM) system was used for channel modeling.
• Received signal data were utilized for training and testing the Demod-CNN model.

Main Results:

• The proposed Demod-CNN technique demonstrated superior performance compared to conventional demodulation methods.
• Simulation results validated the effectiveness of the CNN-based approach in IRS wireless communication.
• The model achieved improved accuracy in signal demodulation for multiple users.

Conclusions:

• Demod-CNN offers a promising advancement for demodulation in IRS-based wireless communication systems.
• Machine learning, specifically CNNs, can significantly improve signal processing efficiency.
• The proposed technique is a viable solution for enhancing sixth-generation wireless communication capabilities.