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Compressive Channel Estimation Based on the Deep Denoising Network in an IRS-Enhanced Massive MIMO System.
1School of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China.
Computational Intelligence and Neuroscience
|September 23, 2022
Summary
This study introduces a new method to reduce training overhead in intelligent reflecting surfaces (IRS) for millimeter-wave (mmWave) massive MIMO systems. The approach enhances channel estimation accuracy and reduces energy consumption in wireless communications.
Area of Science:
- Wireless Communications
- Signal Processing
- Machine Learning
Background:
- Intelligent Reflecting Surfaces (IRS) integrated with millimeter-wave (mmWave) massive MIMO enhance wireless communication performance.
- Accurate Channel State Information (CSI) is crucial but challenging to obtain due to high-dimensional cascaded channels and passive reflectors.
- Existing methods often assume ideal channel estimation, which is not practical for these complex systems.
Purpose of the Study:
- To propose a novel method for reducing training overhead in IRS-assisted mmWave massive MIMO systems.
- To improve the accuracy of channel estimation in these challenging communication environments.
- To decrease the energy consumption associated with large-scale antenna arrays and pilot training.
Main Methods:
- A partial ON/OFF model for IRS and an optimized pilot design strategy are introduced to reduce training overhead.
- An improved deep residual shrinkage denoising network with a soft thresholding model is proposed for channel data denoising.
- Deep learning is utilized to denoise channel data, thereby enhancing the accuracy of channel estimation.
Main Results:
- Significant reduction in energy consumption of large-scale antenna arrays.
- Substantial decrease in pilot overhead during the training phase of signal transmission.
- The proposed deep learning network demonstrates superior denoising performance compared to existing solutions.
- Improved accuracy in channel estimation is achieved through effective data denoising.
Conclusions:
- The proposed method effectively reduces training overhead and energy consumption in IRS-assisted mmWave massive MIMO systems.
- The improved deep residual shrinkage denoising network offers enhanced denoising capabilities, leading to more accurate channel estimation.
- Simulation results validate the superiority of the proposed approach over prior methods in terms of performance and efficiency.
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