ETRI Journal

  • 제43권6호
  • /
  • Pages.966-977
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  • 2021
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  • 1225-6463(pISSN)
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  • 2233-7326(eISSN)
한국전자통신연구원 (Electronics and Telecommunications Research Institute)
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Supervised learning and frequency domain averaging-based adaptive channel estimation scheme for filterbank multicarrier with offset quadrature amplitude modulation

  • 투고 : 2020.03.19
  • 심사 : 2020.07.02
  • 발행 : 2021.12.01
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초록

Filterbank multicarrier with offset quadrature amplitude modulation (FBMC-OQAM) is an attractive alternative to the orthogonal frequency division multiplexing (OFDM) modulation technique. In comparison with OFDM, the FBMC-OQAM signal has better spectral confinement and higher spectral efficiency and tolerance to synchronization errors, primarily due to per-subcarrier filtering using a frequency-time localized prototype filter. However, the filtering process introduces intrinsic interference among the symbols and complicates channel estimation (CE). An efficient way to improve the CE in FBMC-OQAM is using a technique known as windowed frequency domain averaging (FDA); however, it requires a priori knowledge of the window length parameter which is set based on the channel's frequency selectivity (FS). As the channel's FS is not fixed and not a priori known, we propose a k-nearest neighbor-based machine learning algorithm to classify the FS and decide on the FDA's window length. A comparative theoretical analysis of the mean-squared error (MSE) is performed to prove the proposed CE scheme's effectiveness, validated through extensive simulations. The adaptive CE scheme is shown to yield a reduction in CE-MSE and improved bit error rates compared with the popular preamble-based CE schemes for FBMC-OQAM, without a priori knowledge of channel's frequency selectivity.

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과제정보

This publication has emanated from research supported by a research grant from Science Foundation Ireland (SFI) and is co-funded under the European Regional Development Fund under Grant Number 13/RC/2077.

참고문헌

  1. B. Farhang-Boroujeny, OFDM versus filter bank multicarrier, IEEE Signal Process. Mag. 28 (2011), no. 3, 92-112. https://doi.org/10.1109/MSP.2011.940267
  2. D. Katselis, A. Rontogiannis, and S. Theodoridis, Preamble-based channel estimation in OFDM/OQAM systems: A review, Signal Process. 93 (2013), no. 7, 2038-2054. https://doi.org/10.1016/j.sigpro.2013.01.013
  3. V. Savaux and F. Bader, Mean square error analysis and linear minimum mean square error application for preamble-based channel estimation in orthogonal frequency division multiplexing/offset quadrature amplitude modulation systems, IET Commun. 9 (2015), no. 14, 1763-1773, doi: 10.1049/iet-com.2014.1181.
  4. V. K. Singh, M. F. Flanagan, and B. Cardiff, Generalized least squares based channel estimation for FBMC-OQAM, IEEE Access. 7 (2019), 129411-129420, doi: 10.1109/access.2019.2939674.
  5. S. Kang and K. Chang, A novel channel estimation scheme for OFDM/OQAM-IOTA system, ETRI J. 29 (2007), no. 4, 430-436, doi: 10.4218/etrij.07.0106.0184.
  6. C. Lele et al., Channel estimation methods for preamble--based OFDM/OQAM modulations, Eur. Trans. Telecommun. 19 (2008), no. 7, 741-750, doi: 10.1002/ett.1332.
  7. H. Wang, W. Du, and L. Xu, Novel preamble design for channel estimation in FBMC/OQAMsystems, KSII Trans. Int. Inform. Syst. 10 (2016), no. 8, doi: 10.3837/tiis.2016.08.014
  8. H. Wang et al., Preamble design with interference cancellation for channel estimation in MIMO-FBMC/OQAM systems, IEEE Access 6 (2018), 44072-44081. https://doi.org/10.1109/access.2018.2864221
  9. D. Kong et al., Frequency domain averaging for channel estimation in OQAM-OFDM systems, in Proc. IEEE Wirel. Commun. Netw. Conf. (Shanghai, China), Apr. 2013, pp. 3116-3121, doi: 10.1109/WCNC.2013.6555060.
  10. G. Guo et al., KNN Model-Based Approach in Classification, R. Meersman, in On The Move to Meaningful Internet Systems 2003: CoopIS, DOA, and ODBASE, vol. 2888. Springer, Berlin, Heidelberg, 2003, pp. 986-996.
  11. J. Li et al., Layered orthogonal frequency division multiplexing with index modulation, IEEE Syst. J. 13 (2019), no. 4, 3793-3802. https://doi.org/10.1109/jsyst.2019.2918068
  12. J. Li et al., Zukerman Moshe, cognitive radio network assisted by OFDM with index modulation, IEEE Trans. Veh. Technol. 69 (2020), no. 1, 1106-1110. https://doi.org/10.1109/tvt.2019.2951606
  13. M.Wen et al., E. Basar, Q. Li and F. Ji, Enhanced orthogonal frequency division multiplexing with indexmodulation, IEEE Trans. Wirel. Commun. 16 (2017), no. 7, 4786-4801. https://doi.org/10.1109/TWC.2017.2702618
  14. M. Wen et al., Generalized multiple-mode OFDM with index modulation, IEEE Trans. Wirel. Commun. 17 (2018), no. 10, 6531-6543. https://doi.org/10.1109/twc.2018.2860954
  15. A. Viholainen, Phydyas project, deliverable 5.1: Prototype filter and structure optimization, Plone Foundation, Fortville, IN, USA, Tech. Rep. FP7-ICT, 2009.
  16. M. Bellanger, Specification and design of a prototype filter for filter bank based multicarrier transmission, in Proc. IEEE Int. Conf. Acoust., Speech, Signal Process. (Salt Lake City, UT, USA), May 2001, pp. 2417-2420.
  17. R. Jain, Channel models: A tutorial, Wimax forum, HI, USA, 2007, pp. 1-6.
  18. S. A. Dudani, The distance-weighted k-nearest-neighbor rule, IEEE Trans. Syst. Man Cybern. SMC-6 (1976), no. 4, 325-327, doi: 10.1109/tsmc.1976.5408784.
  19. T.Wang, Efficient techniques for cost-sensitive learning with multiple cost considerations, Ph.D. Thesis, Faculty of Engineering and Information Technology, University of Technology, Sydney, Australia, 2013.
  20. E. T. Iorkyaseet al., RF-based location of partial discharge sources using received signal features, High Volt. 4 (2019), no. 1, 28-32, doi: 10.1049/hve.2018.5027.
  21. Recommendation ITU-R M.1225, Guidelines for evaluation of radio transmission technologies for imt-2000, 1997.