Deep Learning-Based Channel Estimation in OFDM Systems for Time-Varying Rayleigh Fading Channels

Document Type : Research Paper

Authors

Department of Electrical Engineering, Malek-Ashtar University of Technology, Tehran, Iran.

Abstract

For Orthogonal Frequency Division multiplexing (OFDM) systems in environments with high mobility and non-stationary channel characteristics, channel estimation is a very challenging task. To handle this issue, a deep learning (DL)-based channel estimation and data extraction algorithm is proposed. The purpose of this paper is to analyse DL-based OFDM data extraction algorithm in time-variant Rayleigh fading channels. Moreover, the model is examined in time-invariant environments. The proposed long short-term memory with projection layer (LSTMP) model, can not only exploit the features of channel variation from previous channel estimations, but also extract more features from pilots and received signals. Moreover, the LSTMP can take advantage of the LSTM estimation to further improve the performance of the channel estimation by reducing the complexity and increasing the accuracy. The LSTMP is first trained with simulated data in an offline manner and then tracks the dynamic channel in an online manner. The simulation results show that the proposed LSTMP model algorithm can be effectively employed to adapt to the characteristics of time-variant channels, compared to the conventional algorithms. Additionally, the trade-off between accuracy and complexity is discussed and compared with that of Convolutional Neural Network (CNN) and LSTM.

Keywords

References

  1. Aida and B. Ridha, "LMMSE channel estimation for block - pilot insertion in OFDM systems under time varying conditions," 2011 11th Mediterranean Microwave Symposium (MMS), Yasmine Hammamet, Tunisia, 2011, pp. 223-228.
  2. -G. Kim and J. -T. Lim, "MAP-Based Channel Estimation for MIMO–OFDM Over Fast Rayleigh Fading Channels," IEEE Trans. Vehicular Technology, vol. 57, no. 3, pp. 1963-1968, May 2008.
  3. Rugini, P. Banelli, and G. Leus, "Block DFE and windowing for Doppler-affected OFDM systems," IEEE 6th Workshop on Signal Processing Advances in Wireless Communications, 2005., New York, NY, USA, 2005, pp. 470-474.
  4. A. D. Teo and S. Ohno, "Optimal MMSE finite parameter model for doubly-selective channels," GLOBECOM '05. IEEE Global Telecommunications Conference, 2005., St. Louis, MO, USA, 2005, pp. 5.
  5. Hijazi and L. Ros, "Polynomial Estimation of Time-Varying Multipath Gains with Intercarrier Interference Mitigation in OFDM Systems," IEEE Trans. Vehicular Technology, vol. 58, no. 1, pp. 140-151, Jan. 2009.
  6. O’Shea and J. Hoydis, "An Introduction to Deep Learning for the Physical Layer," IEEE Trans. Cognitive Comm. and Networking, vol. 3, no. 4, pp. 563-575, Dec. 2017.
  7. Vanhoucke, A. Senior, and M. Z. Mao, “Improving the speed of neural networks on CPUs,'' in Proc. Deep Learn. Unsupervised Feature Learn. Workshop, vol. 1, 2011, p. 4.
  8. Sun, X. Chen, Q. Shi, M. Hong, X. Fu and N. D. Sidiropoulos, "Learning to Optimize: Training Deep Neural Networks for Interference Management," IEEE Trans. Signal Processing, vol. 66, no. 20, pp. 5438-5453, Oct. 2018.
  9. Ye, G. Y. Li, and B. -H. Juang, "Power of Deep Learning for Channel Estimation and Signal Detection in OFDM Systems," IEEE Wireless Communications Letters, vol. 7, no. 1, pp. 114-117, Feb. 2018.
  10. Yang, F. Gao, X. Ma, and S. Zhang, "Deep Learning-Based Channel Estimation for Doubly Selective Fading Channels," IEEE Access, vol. 7, pp. 36579-36589, 2019.
  11. Ponnaluru and S. Penke, "Retraction note to: Deep learning for estimating the channel in orthogonal frequency division multiplexing systems," J. Ambient Intell. Hum. Comput., vol. 12, pp. 5325-5336, June 2022.
  12. H. Essai Ali, “Deep learning based pilot assisted channel state estimator for OFDM systems,” IET Communications. vol. 15, no. 2, pp. 257-64, Jan. 2021.
  13. Gao, S. Jin, C. -K. Wen, and G. Y. Li, "ComNet: Combination of Deep Learning and Expert Knowledge in OFDM Receivers," IEEE Communications Letters, vol. 22, no. 12, pp. 2627-2630, Dec. 2018.
  14. Pan, H. Shan, R. Li, Y. Wu, W. Wu, and T. Q. S. Quek, "Channel Estimation Based on Deep Learning in Vehicle-to-Everything Environments," IEEE Communications Letters, vol. 25, no. 6, pp. 1891-1895, June 2021.
  15. He, C. -K. Wen, S. Jin, and G. Y. Li, "Deep Learning-Based Channel Estimation for Beamspace mm Wave Massive MIMO Systems," IEEE Wireless Communications Letters, vol. 7, no. 5, pp. 852-855, Oct. 2018.
  16. Liu, X. Liu, D. W. K. Ng, and J. Yuan, "Deep Residual Learning for Channel Estimation in Intelligent Reflecting Surface-Assisted Multi-User Communications," IEEE Trans. Wireless Communications, vol. 21, no. 2, pp. 898-912, Feb. 2022.
  17. Soltani, V. Pourahmadi, A. Mirzaei, and H. Sheikhzadeh, "Deep Learning-Based Channel Estimation," IEEE Communications Letters, vol. 23, no. 4, pp. 652-655, April 2019.
  18. Li, H. Chen, H. -H. Chang and L. Liu, "Deep Residual Learning Meets OFDM Channel Estimation," IEEE Wireless Communications Letters, vol. 9, no. 5, pp. 615-618, May 2020.
  19. Zhao, Y. Fang, and L. Qiu, "Deep Learning-Based channel estimation with SRGAN in OFDM Systems," 2021 IEEE Wireless Communications and Networking Conference (WCNC), Nanjing, China, 2021, pp. 1-6.
  20. Honkala, D. Korpi, and J. M. J. Huttunen, "DeepRx: Fully Convolutional Deep Learning Receiver," IEEE Trans. Wireless Communications, vol. 20, no. 6, pp. 3925-3940, June 2021.
  21. Korpi, M. Honkala, J. M. J. Huttunen, and V. Starck, "DeepRx MIMO: Convolutional MIMO Detection with Learned Multiplicative Transformations," ICC 2021-IEEE International Conference on Communications, Montreal, QC, Canada, 2021, pp. 1-7.
  22. Zhao, M. C. Vuran, F. Guo, and S. D. Scott, "Deep-Waveform: A Learned OFDM Receiver Based on Deep Complex-Valued Convolutional Networks," IEEE Journal on Selected Areas in Communications, vol. 39, no. 8, pp. 2407-2420, Aug. 2021.
  23. Careem, A. Dutta, and N. Thawdar, "On Equivalence of Neural Network Receivers," ICC 2021 - IEEE International Conference on Communications, Montreal, QC, Canada, 2021, pp. 1-7.
  24. Pihlajasalo et al., "Deep Learning Based OFDM Physical-Layer Receiver for Extreme Mobility," 2021 55th Asilomar Conference on Signals, Systems, and Computers, Pacific Grove, CA, USA, 2021, pp. 395-399.
  25. Pihlajasalo et al., "HybridDeepRx: Deep Learning Receiver for High-EVM Signals," 2021 IEEE 32nd Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC), Helsinki, Finland, 2021, pp. 622-627.
  26. Felix, S. Cammerer, S. Dörner, J. Hoydis, and S. Ten Brink, "OFDM-Autoencoder for End-to-End Learning of Communications Systems," 2018 IEEE 19th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), Kalamata, Greece, 2018, pp. 1-5.
  27. Xie, X. Liu, K. C. The, and Y. L. Guan, "Robust Deep Learning-Based End-to-End Receiver for OFDM System with Non-Linear Distortion," IEEE Communications Letters, vol. 26, no. 2, pp. 340-344, Feb. 2022.
  28. Olickal, Sebin J., and Renu Jose. "LSTM projected layer neural network-based signal estimation and channel state estimator for OFDM wireless communication systems." AIMS Electronics and Electrical Engineering, vol. 7, no. 2, pp. 187-195, Jun. 2023.
  29. Le Floch, M. Alard, and C. Berrou, "Coded orthogonal frequency division multiplex [TV broadcasting]," Proceedings of the IEEE, vol. 83, no. 6, pp. 982-996, June 1995.
  30. Song, C. Lan, J. Xing, W. Zeng, and J. Liu, "Spatio-Temporal Attention-Based LSTM Networks for 3D Action Recognition and Detection," IEEE Trans. Image Processing, vol. 27, no. 7, pp. 3459-3471, July 2018.
  31. Zhang, G. Lindholm, and H. Ratnaweera, ‘‘Use long short-term memory to enhance Internet of Things for combined sewer overflow monitoring,’’ Journal of Hydrology, vol. 556, pp. 409–418, Jan. 2018.
  32. Jia, YuKang, Wu, Zhicheng, Xu, Yanyan, Ke, Dengfeng, and Su, Kaile. “Long Short-Term Memory Projection Recurrent Neural Network Architectures for Piano’s Continuous Note Recognition,” Journal of Robotics. 2017, pp. 1-7, Sept. 2017.

Files

Share

How to cite

Statistics