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Deep OFDM Channel Estimation: Capturing Frequency Recurrence (2401.05436v1)

Published 7 Jan 2024 in eess.SP, cs.LG, and cs.NI

Abstract: In this paper, we propose a deep-learning-based channel estimation scheme in an orthogonal frequency division multiplexing (OFDM) system. Our proposed method, named Single Slot Recurrence Along Frequency Network (SisRafNet), is based on a novel study of recurrent models for exploiting sequential behavior of channels across frequencies. Utilizing the fact that wireless channels have a high degree of correlation across frequencies, we employ recurrent neural network techniques within a single OFDM slot, thus overcoming the latency and memory constraints typically associated with recurrence based methods. The proposed SisRafNet delivers superior estimation performance compared to existing deep-learning-based channel estimation techniques and the performance has been validated on a wide range of 3rd Generation Partnership Project (3GPP) compliant channel scenarios at multiple signal-to-noise ratios.

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References (17)
  1. G. T. Zhou, M. Viberg, and T. McKelvey, “A first-order statistical method for channel estimation,” IEEE Signal Processing Letters, vol. 10, no. 3, pp. 57–60, 2003.
  2. N. Suga, R. Sasaki, and T. Furukawa, “Channel estimation using matrix factorization based interpolation for OFDM systems,” in 2019 IEEE 90th Vehicular Technology Conference, pp. 1–5.
  3. X. Li, J. Fang, H. Li, and P. Wang, “Millimeter wave channel estimation via exploiting joint sparse and low-rank structures,” IEEE Transactions on Wireless Communications, vol. 17, no. 2, pp. 1123–1133, 2017.
  4. M. Soltani, V. Pourahmadi, A. Mirzaei, and H. Sheikhzadeh, “Deep learning-based channel estimation,” IEEE Communications Letters, vol. 23, no. 4, pp. 652–655, 2019.
  5. W. Shen, Z. Qin, and A. Nallanathan, “Deep learning for super-resolution channel estimation in reconfigurable intelligent surface aided systems,” IEEE Transactions on Communications, vol. 71, no. 3, pp. 1491–1503, 2023.
  6. S. Zhang, Y. Liu, Q. Shi, S. Xu, and S. Cao, “LSRN: A recurrent residual learning framework for continuous wireless channel estimation using super-resolution concept,” IEEE Access, vol. 8, pp. 38 098–38 111, 2020.
  7. L. Ge, Y. Guo, Y. Zhang, G. Chen, J. Wang, B. Dai, M. Li, and T. Jiang, “Deep neural network based channel estimation for massive MIMO-OFDM systems with imperfect channel state information,” IEEE Systems Journal, vol. 16, no. 3, pp. 4675–4685, 2022.
  8. B. Lin, X. Wang, W. Yuan, and N. Wu, “A novel OFDM autoencoder featuring CNN-based channel estimation for internet of vessels,” IEEE Internet of Things Journal, vol. 7, no. 8, pp. 7601–7611, 2020.
  9. E. Balevi and J. G. Andrews, “Wideband channel estimation with a generative adversarial network,” IEEE Transactions on Wireless Communications, vol. 20, no. 5, pp. 3049–3060, 2021.
  10. Y. Liao, Y. Hua, X. Dai, H. Yao, and X. Yang, “ChanEstNet: A deep learning based channel estimation for high-speed scenarios,” in ICC 2019 - 2019 IEEE International Conference on Communications (ICC), 2019, pp. 1–6.
  11. P. Jiang, C.-K. Wen, S. Jin, and G. Y. Li, “Dual CNN-based channel estimation for MIMO-OFDM systems,” IEEE Transactions on Communications, vol. 69, no. 9, pp. 5859–5872, 2021.
  12. A. Yang, P. Sun, T. Rakesh, B. Sun, and F. Qin, “Deep learning based OFDM channel estimation using frequency-time division and attention mechanism,” in 2021 IEEE Globecom Workshops (GC Wkshps), 2021, pp. 1–6.
  13. N. Farsad and A. Goldsmith, “Neural network detection of data sequences in communication systems,” IEEE Transactions on Signal Processing, vol. 66, no. 21, pp. 5663–5678, 2018.
  14. J. Hou, H. Liu, Y. Zhang, W. Wang, and J. Wang, “Gru-based deep learning channel estimation scheme for the ieee 802.11 p standard,” IEEE Wireless Communications Letters, 2022.
  15. Q. Bai, J. Wang, Y. Zhang, and J. Song, “Deep learning-based channel estimation algorithm over time selective fading channels,” IEEE Transactions on Cognitive Communications and Networking, vol. 6, no. 1, pp. 125–134, 2019.
  16. V. Savaux and Y. Louët, “LMMSE channel estimation in OFDM context: a review,” IET Signal Processing, vol. 11, no. 2, pp. 123–134, 2017.
  17. X. Wang, S. Ju, X. Zhang, S. Ramjee, and A. El Gamal, “Efficient training of deep classifiers for wireless source identification using test SNR estimates,” IEEE Wireless Communications Letters, vol. 9, no. 8, pp. 1314–1318, 2020.
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