Channel estimation for intelligent reflecting surface-assisted wireless communication systems based on deep learning

Xuhui Zhang; Yan Xu; Xiaoqi Zhang

doi:10.1117/12.3012294

21 December 2023 Channel estimation for intelligent reflecting surface-assisted wireless communication systems based on deep learning

Xuhui Zhang, Yan Xu, Xiaoqi Zhang

Author Affiliations +

Proceedings Volume 12970, Fourth International Conference on Signal Processing and Computer Science (SPCS 2023); 129700P (2023) https://doi.org/10.1117/12.3012294
Event: Fourth International Conference on Signal Processing and Computer Science (SPCS 2023), 2023, Guilin, China

Abstract

Intelligent Reflecting Surface (IRS) is capable of performing phase and amplitude transformations on incoming signals, thereby achieving precise signal transmission and improving coverage and transmission efficiency. However, these advantages can only be achieved when the precise Channel State Information (CSI) is known. Due to the passive nature of IRS elements, obtaining accurate CSI is challenging. Therefore, a combination of Compressive Sensing (CS) algorithm and Deep Learning (DL) is proposed to address this issue. Firstly, joint-link structure is used to optimize the traditional CS algorithm, enabling better Normalized Mean Square Error (NMSE) performance at lower pilot overhead and Signal-to-Noise Ratio (SNR). Secondly, the channel estimation problem is regarded as a denoising problem, where the output of the optimized CS algorithm is treated as noisy CSI. A multi-layered deep denoising block network is utilized to further denoise the CSI, resulting in more accurate estimation. Finally, experimental results demonstrate that the proposed algorithm outperforms the comparison algorithms under the same SNR, yielding better accuracy.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

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Xuhui Zhang, Yan Xu, and Xiaoqi Zhang "Channel estimation for intelligent reflecting surface-assisted wireless communication systems based on deep learning", Proc. SPIE 12970, Fourth International Conference on Signal Processing and Computer Science (SPCS 2023), 129700P (21 December 2023); https://doi.org/10.1117/12.3012294

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KEYWORDS

Matrices

Denoising

Reflection

Signal to noise ratio

Wireless communications

Telecommunications

Deep learning

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