(44-1) 08 * << * >> * Russian * English * Content * All Issues

Methods for early recognition of OFDM data

R.R. Yuzkiv 1, V.A. Fedoseev 1,2, V.V. Myasnikov 1,2, V.V. Sergeyev 1,2

Samara National Research University, Moskovskoye Shosse 34, 443086, Samara, Russia,

IPSI RAS – Branch of the FSRC “Crystallography and Photonics” RAS,
Molodogvardeyskaya 151, 443001, Samara, Russia

 PDF, 1179 kB

DOI: 10.18287/2412-6179-CO-662

Pages: 60-66.

Full text of article: Russian language.

A technique of early recognition (recovery) of data transmitted using OFDM technology by an incompletely received signal is considered. Theoretically, this approach is able to increase the speed of information transfer, as well as the resistance of the de-encoder to the loss of part of the transmitted signal. The article proposes a mathematical formulation of the OFDM signal early recognition problem, and also discusses several methods for solving it: a regularization method, an iterative method based on the fast Fourier transform, a gradient method based on learning, and an inverse operator method. The possibility of simultaneously using several methods to improve the accuracy of information recovery is considered. The results of numerical experiments presented in this work confirm the practical potential of the proposed approach.

OFDM, frequency modulation, signal recovery, early recognition, gradient descent, error-correcting codes.

Yuzkiv RR, Fedoseev VA, Myasnikov VV, Sergeyev VV. Methods for early recognition of OFDM data. Computer Optics 2020; 44(1): 60-66. DOI: 10.18287/2412-6179-CO-662.

This work was supported by the Russian Foundation for Basic Research under project No. 18-01-00748 (Sections 1 and 2.1), project No. 19-07-00357 (Sections 2.2-2.4), and by the RF Ministry of Science and Higher Education within the government project of FSRC «Crystallography and Photonics» RAS under agreement 007ГЗ/Ч3363/26 (Section 3).


  1. Prasad R. OFDM for wireless communications systems. Boston: Artech House; 2004.
  2. Kovalev VV, Seletskaya OY, Pokamestov DA. OFDM signal formation and processing [In Russian]. Young Scientist 2016; 118: 151-154.
  3. Fazel K, Kaiser S. Multi-carrier and spread spectrum systems: From OFDM and MC-CDMA to LTE and WiMAX. 2nd ed. Chichester: John Wiley & Sons Ltd; 2008.
  4. Luo F-L, Zhang C, eds. Signal processing for 5G: algorithms and implementations. Chichester: John Wiley & Sons Ltd; 2016.
  5. Balevi E, Andrews JG. One-bit OFDM receivers via deep learning. Source: áhttps://arxiv.org/abs/1811.00971ñ.
  6. Balevi E, Andrews JG. Reliable low resolution OFDM receivers via deep learning. 52nd Asilomar Conference on Signals, Systems, and Computers 2018: 697-701. DOI: 10.1109/ACSSC.2018.8645190.
  7. Jawhar YA, Audah L, Taher MA, Ramli KN, Shah NSM, Musa M, Ahmed MS. A review of partial transmit sequence for PAPR reduction in the OFDM systems. IEEE Access 2019; 7: 18021-18041. DOI: 10.1109/ACCESS.2019.2894527.
  8. Muhammad IG, Tepe KE, Abdel-Raheem E. QAM equalization and symbol detection in OFDM systems using extreme learning machine. Neural Comput Applicat 2013; 22: 491-500. DOI: 10.1007/s00521-011-0796-y.
  9. Ye H, Li GY, Juang B-H. Power of deep learning for channel estimation and signal detection in OFDM systems. IEEE Wireless Commun Lett 2018; 7: 114-117. DOI: 10.1109/LWC.2017.2757490.
  10. Gao X, Jin S, Wen C-K, Li GY. ComNet: Combination of deep learning and expert knowledge in OFDM receivers. IEEE Commun Lett 2018; 22: 2627-2630. DOI: 10.1109/LCOMM.2018.2877965.
  11. Leonovich GI, Lykov KV, Novikov SYa, Tsvetov VP. Mathematical modeling of algorithms for fast recognition of OFDM/QAM symbols under the influence of non-stationary interference on a narrow-band radio channel [In Russian]. Mathematical and Computer Modeling: Materials of the Scientific-Practical Conference 2014: 7-10.
  12. Soifer VA, Kotlyar VV, Doskolovich LL. Iterative methods for diffractive optical elements computation. London: Taylor & Francis Ltd; 1997. ISBN: 978-0-7484-0634-0.


© 2009, IPSI RAS
151, Molodogvardeiskaya str., Samara, 443001, Russia; E-mail: ko@smr.ru ; Tel: +7 (846) 242-41-24 (Executive secretary), +7 (846) 332-56-22 (Issuing editor), Fax: +7 (846) 332-56-20