Hybrid LS-MMSE Channel Estimation for OFDM Systems Over Frequency-Selective Rayleigh Fading Channel

Abstract

To achieve reliable orthogonal frequency division multiplexing (OFDM) communications in frequency-selective fading channels, accurate channel estimation is critical. The traditional least squares (LS) estimator enhances noise at pilot locations, whereas the traditional pilot-only minimum mean square error (MMSE) estimator adds residual interpolation error that remains independent of signal-to-noise ratio (SNR). This paper suggests an Adaptive Full-Band Hybrid LS-MMSE channel estimator which removes both of the limitations by a one-step linear minimum mean square error (LMMSE) Wiener filter of size NxNP analytically computed based on the complete channel frequency-domain cross-correlation matrix using the exponential power delay profile (PDP). The proposed estimator directly interpolates all pilot measurements to estimates in the entire frequency band in a single step, ensuring the Hybrid MSE results better than LS and MMSE results at any SNR. Monte Carlo simulated 16-QAM OFDM system with N = 64 subcarriers, NP = 16 pilots and L = 6 tap Rayleigh fading channel show consistent performance improvements. The proposed method has a NMSE of -18.21 dB (4.83 dB gain over LS and 3.14 dB gain over MMSE) and a BER of 0.0649, and spectral efficiency of 8.4 bits/s/Hz at SNR = 28 dB. The estimator is computationally tractable, and it achieves the Cramer-Rao Lower Bound at high SNR, making it a high-performance solution to 5G and beyond OFDM systems.

Country : Iraq

1 Mustafa Ghanim2 Ayman N. Muhi3 Mohaimen Q. Algburi

  1. College of Communication Engineering, University of Technology- Iraq
  2. College of Communication Engineering, University of Technology- Iraq
  3. College of Communication Engineering, University of Technology- Iraq

IRJIET, Volume 10, Issue 4, April 2026 pp. 276-286

doi.org/10.47001/IRJIET/2026.104040

References

  1. Zhang, Z., Xiao, Y., Ma, Z., Xiao, M., Ding, Z., Lei, X., Karagiannidis, G. K., & Fan, P. (2019). 6G Wireless Networks: Vision, Requirements, Architecture, and Key Technologies. IEEE Vehicular Technology Magazine, 14(3), 28–41. https://doi.org/10.1109/mvt.2019.2921208
  2. H. A. Le, T. V. Chien, T. H. Nguyen, H. Choo, and V. D. Nguyen, "Machine learning-based 5G-and-beyond channel estimation for MIMO-OFDM communication systems," Sensors, vol. 21, no. 14, p. 4861, Jul. 2021. https://doi.org/10.3390/s21144861
  3. Dahlman, E., Parkvall, S., & Sköld, J. (2018). NR Overview. 5G NR: The Next Generation Wireless Access Technology, 57–71. https://doi.org/10.1016/b978-0-12-814323-0.00005-3
  4. R. Prasad, OFDM for Wireless Communications Systems. Artech House, 2004.
  5. A.F. Molisch, Wireless Communications, 2nd ed. Wiley-IEEE Press, 2011.
  6. Jin, J, Chen, M, Jiang, X, Ai, B & Wu, E 2019, ‘Iterative channel estimation and pilot design rules for high‐mobility comb‐pilot OFDM system’, International Journal of Communication Systems, vol. 32, no. 8, http://dx.doi.org/10.1002/dac.3933
  7. Uwaechia, A. N., & Mahyuddin, N. M. (2019). Spectrum-Efficient Distributed Compressed Sensing Based Channel Estimation for OFDM Systems Over Doubly Selective Channels. IEEE Access, 7, 35072–35088. https://doi.org/10.1109/access.2019.2904596
  8. Ding, Y., Deng, H., Xie, Y., Wang, H., & Sun, S. (2024). Time-Varying Channel Estimation Based on Distributed Compressed Sensing for OFDM Systems. Sensors, 24(11), 3581. https://doi.org/10.3390/s24113581
  9. Yang, H., Geng, X., Xu, H., & Shi, Y. (2023). An improved least squares (LS) channel estimation method based on CNN for OFDM systems. Electronic Research Archive, 31(9), 5780–5792. https://doi.org/10.3934/era.2023294
  10. Hu, B., Li, X., & Xue, L. (2022). A Pilot-Based Integration Method of Ranging and LS Channel Estimation for OFDM Systems. Drones, 6(12), 400. https://doi.org/10.3390/drones6120400
  11. Soman, A. M., Nakkeeran, R., & Shinu, M. J. (2021). Pilot Based MMSE Channel Estimation for Spatial Modulated OFDM Systems. International Journal of Electronics and Telecommunications, 685–691. https://doi.org/10.24425/ijet.2021.137863
  12. S. M. Kay, Fundamentals of Statistical Signal Processing: Estimation Theory. Prentice Hall, 1993.
  13. Ozdemir, M., & Arslan, H. (2007). Channel estimation for wireless ofdm systems. IEEE Communications Surveys & Tutorials, 9(2), 18–48. https://doi.org/10.1109/comst.2007.382406
  14. Sutar, M., & Patil, V. (2017). LS and MMSE estimation with different fading channels for OFDM system. 2017 International conference of Electronics, Communication and Aerospace Technology (ICECA), 1, 740-745. https://doi.org/10.1109/iceca.2017.8203641.
  15. Astawa, I., Pertiwi, B., & , A. (2021). LS and MMSE Estimation Channel Techniques for DVB-T2 System Based on MIMO-OFDM. 2021 IEEE 5th International Conference on Information Technology, Information Systems and Electrical Engineering (ICITISEE), 257-261. https://doi.org/10.1109/icitisee53823.2021.9655798.
  16. Kondepogu, V., & Bhattacharyya, B. (2024). Hybrid AE and Bi-LSTM-Aided Sparse Multipath Channel Estimation in OFDM Systems. IEEE Access, 12, 7952-7965. https://doi.org/10.1109/access.2024.3350212.
  17. Senthil Kumaran, V. N., Guttula, R., & Reddy, G. N. (2024). Hybrid Optimized LMMSE-Based Channel Estimation with Low Power Trellis Coded Modulation. Cybernetics and Systems, 1–23. https://doi.org/10.1080/01969722.2024.2343989
  18. Dong, P., Zhang, H., Li, G. Y., Gaspar, I. S., &NaderiAlizadeh, N. (2019). Deep CNN-Based Channel Estimation for mmWave Massive MIMO Systems. IEEE Journal of Selected Topics in Signal Processing, 13(5), 989–1000. https://doi.org/10.1109/jstsp.2019.2925975
  19. Soltani, M., Pourahmadi, V., Mirzaei, A., &Sheikhzadeh, H. (2019). Deep Learning-Based Channel Estimation. IEEE Communications Letters, 23(4), 652–655. https://doi.org/10.1109/lcomm.2019.2898944
  20. Ye, H., Li, G. Y., & Juang, B.-H. (2018). Power of Deep Learning for Channel Estimation and Signal Detection in OFDM Systems. IEEE Wireless Communications Letters, 7(1), 114–117. https://doi.org/10.1109/lwc.2017.2757490
  21. Huang, C., Alexandropoulos, G. C., Zappone, A., Yuen, C., & Debbah, M. (2019). Deep Learning for UL/DL Channel Calibration in Generic Massive MIMO Systems. ICC 2019 - 2019 IEEE International Conference on Communications (ICC), 1–6. https://doi.org/10.1109/icc.2019.8761962
  22. Bajwa, W. U., Haupt, J., Sayeed, A. M., & Nowak, R. (2010). Compressed Channel Sensing: A New Approach to Estimating Sparse Multipath Channels. Proceedings of the IEEE, 98(6), 1058–1076. https://doi.org/10.1109/jproc.2010.2042415
  23. Taubock, G., &Hlawatsch, F. (2008). A compressed sensing technique for OFDM channel estimation in mobile environments: Exploiting channel sparsity for reducing pilots. 2008 IEEE International Conference on Acoustics, Speech and Signal Processing, 2885–2888. https://doi.org/10.1109/icassp.2008.4518252
  24. Bajwa, W. U., Sayeed, A., & Nowak, R. (2009). Sparse Multipath Channels: Modeling and Estimation. 2009 IEEE 13th Digital Signal Processing Workshop and 5th IEEE Signal Processing Education Workshop, 320–325. https://doi.org/10.1109/dsp.2009.4785942
  25. Berger, C. R., Shengli Zhou, Preisig, J. C., & Willett, P. (2010). Sparse Channel Estimation for Multicarrier Underwater Acoustic Communication: From Subspace Methods to Compressed Sensing. IEEE Transactions on Signal Processing, 58(3), 1708–1721. https://doi.org/10.1109/tsp.2009.2038424
  26. Gao, Z., Dai, L., Wang, Z., & Chen, S. (2015). Spatially Common Sparsity Based Adaptive Channel Estimation and Feedback for FDD Massive MIMO. IEEE Transactions on Signal Processing, 63(23), 6169–6183. https://doi.org/10.1109/tsp.2015.2463260
  27. Tseng, P., & Yun, S. (2007). A coordinate gradient descent method for nonsmooth separable minimization. Mathematical Programming, 117(1–2), 387–423. https://doi.org/10.1007/s10107-007-0170-0
  28. Liu, Y., Tan, Z., Hu, H., Cimini, L. J., & Li, G. Y. (2014). Channel Estimation for OFDM. IEEE Communications Surveys & Tutorials, 16(4), 1891–1908. https://doi.org/10.1109/comst.2014.2320074
  29. Noh, S., Zoltowski, M. D., Sung, Y., & Love, D. J. (2014). Pilot Beam Pattern Design for Channel Estimation in Massive MIMO Systems. IEEE Journal of Selected Topics in Signal Processing, 8(5), 787–801. https://doi.org/10.1109/jstsp.2014.2327572
  30. Alkhateeb, A., El Ayach, O., Leus, G., & Heath, R. W. (2014). Channel Estimation and Hybrid Precoding for Millimeter Wave Cellular Systems. IEEE Journal of Selected Topics in Signal Processing, 8(5), 831–846. https://doi.org/10.1109/jstsp.2014.2334278.
  31. Kondepogu, V., & Bhattacharyya, B. (2024). Hybrid ae and bi-lstm-aided sparse multipath channel estimation in ofdm systems. IEEE Access, 12, 7952–7965. https://doi.org/10.1109/ACCESS.2024.3350212
  32. Senthil Kumaran, V. N., Guttula, R., & Reddy, G. N. (2024). Hybrid optimized lmmse-based channel estimation with low power trellis coded modulation. Cybernetics and Systems, 1–23. https://doi.org/10.1080/01969722.2024.2343989
  33. Lim, S., Wang, H., & Ko, K. (2023). Power-delay-profile-based mmse channel estimations for ofdm systems. Electronics12(3), 510. https://doi.org/10.3390/electronics12030510
  34. He, R., Liu, X., Mei, K., Gong, G., Xiong, J., & Wei, J. (2022). Iterative joint estimation procedure of channel and pdp for ofdm systems. Entropy24(11), 1664. https://doi.org/10.3390/e24111664
  35. Mei, K., Liu, J., Liu, X., Xiong, J., Zhang, X., & Wei, J. (2021). LMMSE channel estimation for OFDM systems with channel correlation function selection. IET Communications, 15(17), 2159–2175. https://doi.org/10.1049/cmu2.12250