DOI QR코드

DOI QR Code

Joint synchronization and parameter estimation in OFDM signaling

  • Sara Karami (Faculty of Electrical and Computer Engineering, Malek-Ashtar University of Technology) ;
  • Hossein Bahramgiri (Faculty of Electrical and Computer Engineering, Malek-Ashtar University of Technology)
  • Received : 2021.12.26
  • Accepted : 2022.06.13
  • Published : 2023.04.20

Abstract

Challenges in cognitive radio and tactical communications include recognizing anonymously received signals and estimating parameters in a blind or semi-blind manner. In this paper, we examine this issue for orthogonal frequency division multiplexing (OFDM) signaling. There are several parameters in OFDM signaling, and the blind receiver must extract and consider the synchronization issue. We assume that the blind receiver is aware of modulation type, OFDM, and not aware of chip duration and the length of cyclic prefix. First, we present new criteria based on kurtosis to estimate these parameters and compare their performance at different levels of additive white Gaussian noise with methods based on correlation, kurtosis, maximum likelihood, and matched filter. Then, we perform synchronization and estimate the start time based on these criteria and several new criteria in two steps: fine and coarse synchronization. Finally, in a more practical setup, we present the idea of jointly estimating the mentioned parameters and the signal start time as coarse synchronization. We compare different criteria and show that one of the proposed criteria has the highest efficiency.

Keywords

References

  1. M. Firdaoussi, H. Ghennioui, and M. El Kamili, Recognition of ofdm and scld signals based on the generalized mean ambiguity function, (Proceeding of the International Conference on Wireless Networks and Mobile Communications, Fez, Morocco), 2016, pp. 230-234.
  2. A. Gorcin and H. Arslan, Identification of OFDM signals under multipath fading channels, (IEEE Military Communications Conference, Orlando, FL, USA) 2012, pp. 1-7.
  3. R. Gupta, S. Kumar, and S. Majhi, Blind modulation classification for asynchronous OFDM systems over unknown signal parameters and channel statistics, IEEE Trans. Vehic. Technol. 69 (2020), no. 5, 5281-5292. https://doi.org/10.1109/TVT.2020.2981935
  4. W. Tang, H. Cha, M. Wei, B. Tian, and X. Ren, Identification method for OFDM signal based on fractal box dimension and pseudo-inverse spectrum, APSIPA Trans. Signal Inform Process. 7 (2018). https://doi.org/10.1017/ATSIP.2018.19
  5. Y. Zhang, D. Liu, J. Liu, Y. Xian, and X. Wang, Improved deep neural network for OFDM signal recognition using hybrid grey wolf optimization, IEEE Access 8 (2020), 133622-133632. https://doi.org/10.1109/ACCESS.2020.3010589
  6. H. Ishii and G. W. Wornell, OFDM blind parameter identification in cognitive radios, (IEEE 16th International Symposium on Personal, Indoor and Mobile Radio Communications, Berlin, Germany) Sept. 2005. https://doi.org/10.1109/PIMRC.2005.1651526
  7. V. Le Nir, T. Van Waterschoot, M. Moonen, and J. Duplicy, Blind CP-OFDM and ZP-OFDM parameter estimation in frequency selective channels, EURASIP J. Wirel. Commun. Netw. 2009 (2009), 1-10.
  8. T. Zhang, W. Qian, G. Zhang, F. Ye, C. Gao, and H. Zhao, Parameter estimation of MC-CDMA signals based on modified cyclic autocorrelation, Elsevier Digital Signal Process. 54 (2016), 46-53. https://doi.org/10.1016/j.dsp.2016.03.007
  9. Z. Sun, R. Liu, and W. Wang, Joint time-frequency domain cyclostationarity-based approach to blind estimation of OFDM transmission parameters, EURASIP J. Wirel. Commun. Netw. 2013 (2013), no. 1, 1-8. https://doi.org/10.1186/1687-1499-2013-1
  10. M. Firdaoussi, H. Ghennioui, and M. El Kamili, New algorithm for blind recognition of OFDM based systems using second-order statistics, (International Conference on Wireless Networks and Mobile Communications, Marrakech, Morocco), 2015, pp. 1-4.
  11. D.-S. Shin, J. Y. Kim, A. Chaudhry, S. H. Min, S. H. Choi, H. J. Kim, C. J. Kim, and N. Pinstech, A blind OFDM parameter estimation method based on cyclicprefix analysis, Life Sci. J. 15 (2018), no. 9.
  12. E. Kanterakis and W. Su, Blind OFDM parameter estimation techniques in frequency-selective Rayleigh channels, (IEEE Radio and Wireless Symposium, Phoenix, AZ, USA), 2011, pp. 150-153.
  13. A. Gorcin and H. Arslan, An OFDM signal identification method for wireless communications systems, IEEE Trans. Vehic. Technol. 64 (2015), no. 12, 5688-5700. https://doi.org/10.1109/TVT.2015.2388671
  14. A. Bouzegzi, P. Ciblat, and P. Jallon, New algorithms for blind recognition of OFDM based systems, Signal Processing 90 (2010), no. 3, 900-913. https://doi.org/10.1016/j.sigpro.2009.09.017
  15. Y. S. Cho, J. Kim, W. Y. Yang, and C. G. Kang, MIMO-OFDM wireless communications with MATLAB, John Wiley & Sons, 2010.
  16. T. Hwang, C. Yang, G. Wu, S. Li, and G. Y. Li, OFDM and its wireless applications: A survey, IEEE Trans. Vehic. Technol. 58 (2008), no. 4, 1673-1694.
  17. J. An, L. Gan, and H. Liao, A non-data-aided algorithm based on ML for OFDM synchronization, (International Conference on Electronics Technology, Chengdu, China), 2018, pp. 1-6.
  18. J. G. Doblado, V. Baena, A. C. Oria, D. Perez-Calderon, and P. Lopez, Coarse time synchronisation for DVB-T2, Electron. Lett. 46 (2010), no. 11, 797-799. https://doi.org/10.1049/el.2010.0807
  19. H. Yang, L. Li, and J. Li, A Robust Timing Synchronization Method for OFDM systems over Multipath Fading Channels, (IEEE/CIC International Conference on Communications in China, Chongqing, China), 2020, pp. 136-141.
  20. P. Sasithong, L. Wuttisittikulkij, and P. Vanichchanunt, Joint timing offset and delay spread estimation for ofdm symbol synchronization over multipath fading channels, (16th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology, Pattaya, Thailand), 2019, pp. 689-692.
  21. D. Bok and J. Heckenbach, OFDM Waveform Synchronisation for Multistatic Radar and DVB-T2 Illumination, (International Radar Conference, Toulon, France), 2019, pp. 1-6.
  22. A. Haglund, P.-O. Frolind, and Lars MH Ulander, Simulation of effect of periodically missing samples on decoding in passive synthetic aperture radar system using OFDM, (IEEE International Geoscience and Remote Sensing Symposium, Yokohama, Japan), 2019, pp. 2949-2952.
  23. S. Karami and H. Bahramgiri, A synchronization method for OFDM-based passive radars, 2020 7th Iranian Conference on Radar and Surveilance System, Tehran, Iran), 2020.
  24. A. Salim, D. Tuninetti, N. Devroye, and D. Erricolo, Modeling the interference of pulsed radar signals in OFDM-based communications systems, (IEEE Radar Conference, Seattle, WA, USA), 2017, pp. 657-662.
  25. F. Behner, S. Reuter, H. Nies, and O. Loffeld, Low-cost Passive Radar Using OFDM Broadcast, (IEEE Radar Conference, Boston, MA, USA), 2019, pp. 1-6.
  26. Y. Liu, J. Yi, X. Wan, X. Zhang, and H. Ke, Time-varying clutter suppression in CP-OFDM based passive radar for slowly moving targets detection, IEEE Sensors J. 20 (2020), no. 16, 9079-9090.
  27. S. Karami and H. Bahramgiri, Time synchronization accuracy in OFDM communication and radar systems, (12th National Conference of Iranian scentific society of Command Control, Communications and Intelligence, sivilica), 2020.