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

Md Abdul Aziz1,2, Md Habibur Rahman1,2, Mohammad Abrar Shakil Sejan1,2

  • 1Department of Information and Communication Engineering, Sejong University, Seoul 05006, Republic of Korea.

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Summary
This summary is machine-generated.

This study introduces an intelligent reflecting surface (IRS) assisted Bi-LSTM deep learning model to enhance spectral efficiency (SE) in wireless systems. The novel approach improves communication performance by optimizing channel information for IRS deployment.

Keywords:
Bi-LSTMIRSmachine learningmulti-user multiple input single outputspectral efficiency

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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.