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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
 1 Samara National Research University, Moskovskoye Shosse 34, 443086, Samara, Russia,
 2 IPSI RAS – Branch of the  FSRC “Crystallography and Photonics” RAS, 
     Molodogvardeyskaya 151, 443001, Samara, Russia
 
 PDF, 1179 kB
  PDF, 1179 kB
DOI: 10.18287/2412-6179-CO-662
Pages: 60-66.
Full text of article: Russian language.
 
Abstract:
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.
Keywords:
OFDM, frequency  modulation, signal recovery, early recognition, gradient descent,  error-correcting codes.
Citation:
  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.
Acknowledgements:
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).
References:
  - Prasad R. OFDM for  wireless communications systems. Boston:  Artech House; 2004.
- Kovalev VV, Seletskaya  OY, Pokamestov DA. OFDM signal formation and processing [In Russian]. Young Scientist  2016; 118: 151-154.
- 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.
 
- Luo F-L, Zhang C, eds. Signal processing for 5G: algorithms and  implementations. Chichester: John Wiley & Sons Ltd; 2016. 
 
- Balevi E, Andrews JG. One-bit OFDM receivers via deep learning. Source: áhttps://arxiv.org/abs/1811.00971ñ.
 
- 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.
 
- 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.
 
- 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.
 
- 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.
 
- 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.  
 
- 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. 
- Soifer VA, Kotlyar VV, Doskolovich LL. Iterative methods for diffractive optical  elements computation. London:  Taylor & Francis Ltd; 1997. ISBN: 978-0-7484-0634-0.
   
  
  
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