Research on deep learning-based channel estimation algorithm in 5G high-speed mobile scenario

Teng Wang; Jie Chen

doi:10.1117/12.3038771

22 July 2024 Research on deep learning-based channel estimation algorithm in 5G high-speed mobile scenario

Teng Wang, Jie Chen

Proceedings Volume 13222, International Conference on Signal Processing and Communication Security (ICSPCS 2024); 1322204 (2024) https://doi.org/10.1117/12.3038771
Event: Third International Conference on Signal Processing and Communication Security (ICSPCS 2024), 2024, Chengdu, China

Abstract

In the high-speed mobile environment supported by the fifth-generation mobile communication technology, higher vehicle speeds, more frequent switching and wider bandwidth make the design of high-speed mobile communication systems more challenging. High-quality wireless communication technology is needed to support future high-speed mobile scenarios to achieve low-latency and highly reliable communication, among which anti-Doppler frequency shift technology is the key. Based on this, this paper proposes a Rayleigh channel estimation algorithm based on image super-resolution (SR) network. This method models the channel response matrix obtained by the LS method as a two-dimensional image, uses the SR network and image restoration (Image restoration, IR) technology for learning optimization, and obtains the channel response at the unknown symbol. The simulation results show that the proposed algorithm is significantly better than the traditional Linear Minimum Mean Square Error (LMMSE) method. Compared with other SR-based channel estimation algorithms under low Signal to Noise Ratio (SNR), it has a 3dB gain and a 5dB gain under high SNR.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Teng Wang and Jie Chen "Research on deep learning-based channel estimation algorithm in 5G high-speed mobile scenario", Proc. SPIE 13222, International Conference on Signal Processing and Communication Security (ICSPCS 2024), 1322204 (22 July 2024); https://doi.org/10.1117/12.3038771

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