Blind Channel Estimation Method Using CNN-Based Resource Grouping

Gayeon Kim, Yumin Kim, Daegun Jang, Byeong-Gwon Kang and Taehyoung Kim ()
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Gayeon Kim: Department of ICT Convergence, Soonchunhayng University, Asan 31538, Republic of Korea
Yumin Kim: Department of Information and Communication Engineering, Soonchunhayng University, Asan 31538, Republic of Korea
Daegun Jang: Department of ICT Convergence, Soonchunhayng University, Asan 31538, Republic of Korea
Byeong-Gwon Kang: Department of Information and Communication Engineering, Soonchunhayng University, Asan 31538, Republic of Korea
Taehyoung Kim: School of Electrical Engineering, Kookmin University, Seoul 02707, Republic of Korea

Mathematics, 2025, vol. 13, issue 3, 1-16

Abstract: This paper proposes a novel blind channel estimation method using convolutional neural network (CNN)-based resource grouping. The traditional K-means-based blind channel estimation scheme suffers limitations in reflecting fine-grained channel variations in both the time and frequency domains. To address these limitations, we propose dynamic resource grouping based on CNN architecture utilizing a two-step learning process that adapts to various channel conditions. The first step of the proposed method identifies the optimal number of subcarriers for each channel condition, providing a foundation for the second step. The second step adjusts the number of orthogonal frequency division multiplexing (OFDM) symbols, a parameter for determining the proposed pattern in the time domain, to adapt to dynamic channel variations. Simulation results demonstrate that the proposed CNN-based blind channel estimation method achieves high channel estimation accuracy across various signal-to-noise ratio (SNR) levels, attaining the highest accuracy of 82.5% at an SNR of 10 dB. Even when classification accuracy is relatively low, the CNN effectively mitigates signal distortion, delivering superior performance compared to conventional methods in terms of mean squared error (MSE) across diverse channel conditions. Notably, the proposed method maintains robust performance under high-mobility scenarios and severe channel variations.

Keywords: channel estimation; K-means algorithm; clustering; CNN (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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