Super-Resolution Channel Estimation based Deep Learning in Reconfigurable Intelligent Surface Systems

Authors

  • Wala’a Hussein Department of Computer and Communication Systems Engineering, Faculty of Engineering, Universiti Putra Malaysia, Serdang, Selangor 43400, Malaysia
  • Kamil Audah Department of Computer and Communication Systems Engineering, Faculty of Engineering, Universiti Putra Malaysia, Serdang, Selangor 43400, Malaysia
  • Nor K. Noordin Department of Computer and Communication Systems Engineering, Faculty of Engineering, Universiti Putra Malaysia, Serdang, Selangor 43400, Malaysia
  • Mod Fadlee B. A. Rasid Department of Computer and Communication Systems Engineering, Faculty of Engineering, Universiti Putra Malaysia, Serdang, Selangor 43400, Malaysia
  • Alyani Binti Ismail Department of Computer and Communication Systems Engineering, Faculty of Engineering, Universiti Putra Malaysia, Serdang, Selangor 43400, Malaysia

Keywords:

Channel estimation, Deep Learning, Image Super-resolution, Image restoration, MIMO-OFDM, RIS

Abstract

The propagation environment may be configured using a reconfigurable intelligent surface (RIS). The channel estimate is a critical problem in implementing the RIS-aided communication system. A cascaded channel with large dimensions and complex statistics is used in a RIS-aided multi-user multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) communication system. This research elucidates the application of deep learning (DL) for communication channel estimation. It employs a two-dimensional visualization to represent the time-frequency attributes of a rapidly fading communication channel. The objective is to decipher the undisclosed channel response by contrasting it with recognized values at designated "pilot points." We have introduced an extensive framework that integrates sophisticated image processing methods, including techniques like image super-resolution (SR) and image restoration (IR), to accomplish this goal. This method views the pilot data collectively as a low-quality image and calculates the channel using an SR system paired with a noise-reducing IR system.Moreover, a practical application of the proposed procedure is also detailed. According to the simulation results, the trained DL estimator outperforms the Least Square estimators in predicting the channel and identifying transmitted symbols, although the suggested SRIR estimator is more sophisticated. Furthermore, the DL estimator exhibits its efficacy with varying pilot densities and cycle prefix times. The findings show that this pipeline may be utilized effectively in channel estimation.

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Published

09.02.2024

How to Cite

Hussein, W. ., Audah, K. ., Noordin, N. K. ., Rasid, M. F. B. A. ., & Ismail, A. B. . (2024). Super-Resolution Channel Estimation based Deep Learning in Reconfigurable Intelligent Surface Systems. International Journal of Intelligent Systems and Applications in Engineering, 12(15s), 659 –. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/4828

Issue

Vol. 12 No. 15s (2024)

Section

Research Article

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