Spectral Efficiency Improvement Using Bi-Deep Learning Model for IRS-Assisted MU-MISO Communication System.

Area of Science:

• Wireless Communication Systems
• Signal Processing
• Artificial Intelligence in Telecommunications

Background:

• Intelligent Reflecting Surfaces (IRS) offer programmable control over electromagnetic wave propagation to enhance spectral efficiency (SE) and wireless system performance.
• Accurate channel information is crucial for realizing the full potential of IRS, but conventional methods for obtaining this information are suboptimal.
• Deep learning models, particularly Bi-LSTM, show promise in improving prediction accuracy by processing data in both forward and backward directions.

Purpose of the Study:

• To propose a novel IRS-assisted Bi-LSTM model for a multi-user, multiple-input, single-output (MISO) downlink system.
• To enhance spectral efficiency (SE) and overall communication performance in wireless networks.
• To address the challenge of acquiring accurate channel information for effective IRS operation.

Main Methods:

• Development of an IRS-assisted system leveraging the Bi-LSTM deep learning model.
• Integration of a Wiener filter to optimize the phase of each IRS element.
• Performance evaluation through simulations comparing the proposed model against other deep learning (DL) models and conventional methods.

Main Results:

• The proposed IRS-assisted Bi-LSTM model demonstrates satisfactory spectral efficiency (SE) performance across various signal-to-noise ratios (SNRs).
• The model outperforms other DL models and existing schemes in SE enhancement during the online phase.
• Bi-LSTM's capability to process data bidirectionally contributes to improved prediction accuracy and system performance.

Conclusions:

• The IRS-assisted Bi-LSTM model is an effective approach for improving spectral efficiency in MISO downlink systems.
• This deep learning-based strategy offers a significant advancement over conventional methods for IRS channel information acquisition.
• The proposed system shows strong potential for enhancing future wireless communication performance.