International Journal of Wireless and Microwave Technologies(IJWMT)

ISSN: 2076-1449 (Print), ISSN: 2076-9539 (Online)

Published By: MECS Press

IJWMT Vol.9, No.5, Sep. 2019

Detection and Extraction of OFDM Parameters Using Difference of Gaussians

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Amin Naemi

Index Terms

Multi-carrier signals;OFDM;difference of Gaussians;machine vision


Signals type detection is very important in telecommunication. Telecommunication signals can be divided into two major groups: single-carrier signals and multi-carrier signals. The first step in extracting data in multi-carrier communication signals is to detect signals and their subcarriers. OFDM signals are one of the most popular multi-carrier signals that are used widely. This paper will introduce a blind detection method for OFDM signals, subcarriers, and the central frequency of them based on the Difference of Gaussians (DoG) technique which is applied for blob detection in machine vision. Performance of our method is compared with high-resolution spectral estimation such as Capon, Borgiotti-Lagunas, and MUSIC. Results showed that it has less computational complexity than the others. Also, there is no need to learn parameters, so the response time of the system is appropriate. Furthermore, many tests have been done on real and artificial signals corrupted with noise and fading and the results showed our proposed method has better performance and cause the lower error in the severe condition like SNR=0.

Cite This Paper

Amin Naemi, "Detection and Extraction of OFDM Parameters Using Difference of Gaussians", International Journal of Wireless and Microwave Technologies(IJWMT), Vol.9, No.5, pp. 12-24, 2019.DOI: 10.5815/ijwmt.2019.05.02


[1]Ye, H., G.Y. Li, and B.-H.J.I.W.C.L. Juang, Power of deep learning for channel estimation and signal detection in OFDM systems. 2018. 7(1): p. 114-117.

[2]Sun, X., et al., A blind OFDM signal detection method based on cyclostationarity analysis. 2017. 94(3): p. 393-413.

[3]Zhang, H.-y. and C.-w. Yuan. A Method for Blind Detection of OFDM Signal Based on Power Spectrum Reprocessing. in Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing, 2007. SNPD 2007. Eighth ACIS International Conference on. 2007. IEEE.

[4]Chen, Q., et al. Computation-efficient blind estimation of OFDM signal parameters for interception and data recovery. in SPIE Defense, Security, and Sensing. 2011. International Society for Optics and Photonics.

[5]Chen, Q., et al. Progressive automatic detection of OFDM system parameters for universal mobile DTV receiver. in Vehicular Technology Conference Fall (VTC 2010-Fall), 2010 IEEE 72nd. 2010. IEEE.

[6]Le Nir, V., et al., Blind CP-OFDM and ZP-OFDM parameter estimation in frequency selective channels. EURASIP Journal on wireless communications and networking, 2009. 2009(1): p. 315765.

[7]Shi, M., Y. Bar-Ness, and W. Su. Blind OFDM systems parameters estimation for software defined radio. in New Frontiers in Dynamic Spectrum Access Networks, 2007. DySPAN 2007. 2nd IEEE International Symposium on. 2007. IEEE.

[8]Wang, R., et al., Channel estimation, carrier recovery, and data detection in the presence of phase noise in OFDM relay systems. IEEE Transactions on Wireless Communications, 2016. 15(2): p. 1186-1205.

[9]Prema, G. and P. Gayatri. Blind spectrum sensing method for OFDM signal detection in Cognitive Radio communications. in Communication and Network Technologies (ICCNT), 2014 International Conference on. 2014. IEEE.

[10]Sohn, S.H., et al. OFDM signal sensing method based on cyclostationary detection. in Cognitive Radio Oriented Wireless Networks and Communications, 2007. CrownCom 2007. 2nd International Conference on. 2007. IEEE.

[11]Bolcskei, H., Blind estimation of symbol timing and carrier frequency offset in wireless OFDM systems. IEEE Transactions on Communications, 2001. 49(6): p. 988-999.

[12]Heath, R.W. and G.B. Giannakis, Exploiting input cyclostationarity for blind channel identification in OFDM systems. IEEE Transactions on Signal Processing, 1999. 47(3): p. 848-856.

[13]Manolakis, D.G., V.K. Ingle, and S.M. Kogon, Statistical and adaptive signal processing: spectral estimation, signal modeling, adaptive filtering, and array processing. Vol. 46. 2005: Artech House Norwood.

[14]Hayes, M.H., Statistical digital signal processing and modeling. 2009: John Wiley & Sons.

[15]Ghosh, S.J.a.p.a., Performance evaluation on the basis of Bit error rate for different order of Modulation and different length of Subchannels in ofdm system. 2014.

[16]Ehm, H., S. Winter, and R. Weigel. Analytic quantization modeling of OFDM signals using normal Gaussian distribution. in 2006 Asia-Pacific Microwave Conference. 2006. IEEE.

[17]Wei, S., D.L. Goeckel, and P.A.J.I.T.o.I.T. Kelly, Convergence of the complex envelope of bandlimited OFDM signals. 2010. 56(10): p. 4893-4904.

[18]Kotzer, I., et al. A model for OFDM signals with applications. in European Wireless 2012; 18th European Wireless Conference 2012. 2012. VDE.

[19]Bretzner, L. and T. Lindeberg, Feature tracking with automatic selection of spatial scales. Computer Vision and Image Understanding, 1998. 71(3): p. 385-392.