MEASUREMENT BASED INVESTIGATON OF CYCLOSTATİONARITY OF OFDM SIGNALS

Yıl 2017, Cilt: 17 Sayı: 2, 3327 - 3334, 27.07.2017

Kürşat Tekbıyık , Hakan Alakoca Halim Bahadır Tuğrel Cem Ayyıldız Güneş Karabulut Kurt

Öz

Increasing need for efficient
usage of frequency spectrum causes to search new methods. Network operators are
forced to use their frequency band as efficient as possible due to high
subscriber demand. Blind recognition of unauthorized users became necessary to
obtain efficient usage of frequency bands. It is  found that the two different OFDMA signals
with the same CP length show different cyclostationary characteristics. In this paper, it is shown that it is possible that signals from two
different users with the same CP lengths can be separated from each other via mesurementS
results. Moreover, cyclic autocorrelation function and spectral correlation
density function are computed for each user. The differences between their
cyclic properties are shown by software defined radio measurement results. It
is shown that signals transmitted from specific users can be detected by using
cyclic autocorrelation and spectral density functions characteristics. 

Anahtar Kelimeler

OFDMA, cylostationarity, blind reconition

Kaynakça

• K. Tekbiyik, H. Alakoca, H. B. Tuğrel, C. Ayyildiz, and G. K. Kurt, “Identification of OFDM Signals using Cyclic Autocorrelation Function,” in National Conference on Electrical, Electronics and Biomedical Engineering (ELECO), 2016, pp. 622–626.
• A. Fehske, J. Gaeddert, and J. H. Reed, “A New Approach to Signal Classification using Spectral Correlation and
• M. Davy, A. Gretton, A. Doucet, P. J. Rayner et al., “Optimized Support Vector Machines for Nonstationary Signal Classification,” IEEE Signal Processing Letters, vol. 9, no. 12, pp. 442–445, 2002.
• G. Huang and J. K. Tugnait, “On Cyclostationarity Based Spectrum Sensing under Uncertain Gaussian Noise,” IEEE Transactions on Signal Processing, vol. 61, no. 8, pp. 2042–2054, 2013.
• C. Du, H. Zeng, W. Lou, and Y. T. Hou, “On Cyclostationary Analysis of WiFi Signals for Direction Estimation,” in IEEE International Conference on Communications (ICC), 2015, pp. 3557–3561.
• R. Zhou, X. Li, T. Yang, Z. Liu, and Z. Wu, “Real-time Cyclostationary Analysis for Cognitive Radio via Software Defined Radio,” in Global Communications Conference (GLOBECOM), 2012, pp. 1495–1500.
• Q. Yuan, P. Tao, W. Wenbo, and Q. Rongrong, “Cyclostationaritybased Spectrum Sensing for Wideband Cognitive Radio,” in IEEE International Conference on Communications and Mobile Computing, vol. 1, 2009, pp. 107–111.