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Dergiler / Electrica / 2017 / Cilt: 17 Sayı: 2

MEASUREMENT BASED INVESTIGATON OF CYCLOSTATİONARITY OF OFDM SIGNALS

Kürşat TEKBIYIK
Hakan ALAKOCA
Halim Bahadır TUĞREL
Cem AYYILDIZ
Güneş KARABULUT KURT
Sayfa
3327–3334
Yayın Tarihi
2017-07-27
DOI
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⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

It is shown that signals transmitted from specific users can be detected by using cyclic autocorrelation and spectral density functions characteristics.

📌 3 Maddede Bu Makale

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Amaç & Hipotez:Increasing need for efficient usage of frequency spectrum causes to search new methods.
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Temel Bulgular & Veri: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.
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Sonuç & Katkı:It is shown that signals transmitted from specific users can be detected by using cyclic autocorrelation and spectral density functions characteristics.
🔬Tür:Araştırma Makalesi
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:OFDMA · cylostationarity · blind reconition

Abstract

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.

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Bu Makaleye Atıf Yap

TEKBIYIK, K., ALAKOCA, H., TUĞREL, H. B., AYYILDIZ, C., KURT, G. K. (2017). MEASUREMENT BASED INVESTIGATON OF CYCLOSTATİONARITY OF OFDM SIGNALS. Electrica, Cilt: 17 Sayı: 2, 3327–3334. https://ajindex.com/articles/1016735

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Kaynakça (5)

  1. 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.
  2. 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.
  3. 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.
  4. 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.
  5. 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.