Öz
In today's communication systems, especially in wireless networks, have
been proven as a result of measurements made in the radio frequency spectrum.
This density means that there is space shortage for new services or
technologies in the radio frequency spectrum. The main reason for this
congestion in the frequency spectrum is the inefficient use of the radio
frequency spectrum due to the fixed frequency assignment policies. Cognitive radio systems are the general name of technologies that have
been developed in recent years to remedy the increasing spectral density. The
purpose of this technology is to continuously monitor the radio frequency
spectrum to evaluate free space. It is the initial phase of these technologies
to perceive the frequency spectrum in the most accurate way in cognitive radio
systems. Despite the use of very different methods in
the literature for spectrum detection, eigenvalue spectrum detection is one of
the most studied topics on the basis of some features. The selection of the
threshold value directly affects the algorithm performance in the eigenvalue
detection. In this study, the effect of different threshold values used for
different eigenvalue detection methods on the algorithm performance is
examined.
Anahtar Kelimeler
Detection Theory Cognitive Radio Tracy-Widom Distribution Covariance Matrix Spectrum Efficiency
Kaynakça
- [1] Cabric, D., (2008). Addressing Feasibility of Cognitive Radios. IEEE Signal Process. Mag., vol:25, no:6, pp:85–93.
- [2] Mitola, J. and Maguire, G.Q., (1999). Cognitive Radio: Making Software Radios more Personal. IEEE Pers. Commun., vol:6, no:4, pp:13–18.
- [3] Zeng, Y. and Liang, Y.C., (2010). Robust Spectrum Sensing in Cognitive Radio. IEEE 21st Int. Symp. Pers. Indoor Mob. Radio Commun. Work., pp: 1–8.
- [4] Zeng, Y. and Liang, Y.C., (2009).Spectrum-sensing Algorithms for Cognitive Radio Based on Statistical Covariances. IEEE Trans. Veh. Technol., vol:58, no:4, pp:1804–1815.
- [5] Sun, H., Nallanathan, A., Wang, C.X., and Chen, Y., (2013). Wideband Spectrum Sensing for Cognitive Radio Networks: a Survey. IEEE Wirel. Commun., vol:20, no:2, pp:74–81.
- [6] Vito, L., (2013).Methods and Technologies for Wideband Spectrum Sensing. Meas. J. Int. Meas. Confed., vol:46, no:9, pp:3153–3165.
- [7] Li, Y., Winters, J.H., and Sollenberger, N.R., (2002). MIMO-OFDM for Wireless Communications: Signal Detection with Enhanced Channel Estimation. IEEE Trans. Commun., vol:50, no:9, pp:1471–1477.
- [8] Annamalai, A. and Olaluwe, A., (2013). On the Energy Detection of Unknown Signals in κ-μ And η-μ Fading Channels with Diversity Receivers. Int. Conf. Connect. Veh. Expo, ICCVE 2013 - Proc., pp: 127–132.
- [9] Gibson, A. and Zafar, J., (2008). Cyclostationary spectrum Detection in Cognitive Radios. IET Semin. Cogn. Radio Softw. Defin. Radio Technol. Tech., Vol:1, no:1, pp:17–17.
- [10] Charan, C. and Paney, R., (2016). Eigenvalue based Double Threshold Spectrum Sensing Under Noise Uncertainty for Cognitive Radio. Optik (Stuttg)., Vol:127, no:15, pp:5968–5975.
- [11] Zeng, Y. and Liang, Y.C., (2009). Eigenvalue-based Spectrum Sensing Algorithms for Cognitive Radio. IEEE Trans. Commun., vol: 57, no:6, pp: 1784–1793.
- [12] Pillay, N. and Xu H.J., (2012). Blind eigenvalue-based Spectrum Sensing for Cognitive Radio Networks. IET Commun., vol:6, no:11, pp:1388.
- [13] Kortun, A., Sellathurai, M., Ratnarajah, T., and Zhong, C., (2012). Distribution of the Ratio of the Largest Eigenvalue to the Trace of Complex Wishart Matrices. IEEE Trans. Signal Process., vol:60, no:10, pp:5527–5532.
- [14] Soltanmohammadi, E., Orooji, M., and Naraghi-Pour, M., (2013). Spectrum Sensing Over MIMO Channels Using Generalized Likelihood Ratio Tests, IEEE Signal Process. Lett., vol:20, no:5, pp:439–442.
- [15] Kortun, A., Ratnarajah, T., Sellathurai, M., Liang, Y.C., and Zeng, Y., (2014). On the Eigenvalue-Based Spectrum Sensing and Secondary User Throughput. IEEE Trans. Veh. Technol., vol:63, no:3, pp:1480–1486.
- [16] Deo, R.S., (2016). On the Tracy-Widom Approximation of Studentized Extreme Eigenvalues of Wishart Matrices. J. Multivar. Anal., vol:147, pp:265–272.
Öz
Günümüz haberleşme
sistemlerinin özellikle kablosuz ağlarda yoğunlaşması üzerine, radyo frekans
spektrumunda yoğunluk oluştuğu yapılan ölçümler sonucunda kanıtlanmıştır.
Meydana gelen bu yoğunluk radyo frekans spektrumunda yeni oluşturulacak servis
veya teknolojiler için yer kıtlığı anlamına gelmektedir. Frekans spektrumundaki
bu yoğunluğa temel sebep ise radyo frekans spektrumunun sabit frekans atama
politikaları nedeni ile verimsiz kullanımıdır. Bilişsel radyo sistemleri ise son
yılarda artan bu spektrum yoğunluğuna çare bulmak için geliştirilen
teknolojilerin genel adıdır. Bu teknolojide amaç radyo frekans spektrumunu
sürekli izleyerek boş alanların değerlendirilmesidir. Bilişsel radyo
sistemlerinde frekans spektrumunu en doğru şekilde algılamak ise bu teknolojilerin
başlangıç aşamasıdır. Spektrum algılama için literatürde çok farklı yöntemler
kullanılmasına rağmen özdeğer tabanlı spektrum algılama bazı özellikleri nedeni
ile üzerinde fazlaca çalışılan başlıklar arasındadır. Özdeğer tabanlı algılama
da eşik değerinin seçimi algoritma performansını doğrudan etkilemektedir. Bu
çalışmada farklı özdeğer tabanlı algılama yöntemleri için kullanılan farklı
eşik değerlerinin algoritma performansına etkisi incelenmiştir.
Anahtar Kelimeler
Algılama Teorisi Bilişsel Radyo Tracy-Widom Olasılık Dağılımı Kovaryans Matrisi Spektrum Verimliliği
Kaynakça
- [1] Cabric, D., (2008). Addressing Feasibility of Cognitive Radios. IEEE Signal Process. Mag., vol:25, no:6, pp:85–93.
- [2] Mitola, J. and Maguire, G.Q., (1999). Cognitive Radio: Making Software Radios more Personal. IEEE Pers. Commun., vol:6, no:4, pp:13–18.
- [3] Zeng, Y. and Liang, Y.C., (2010). Robust Spectrum Sensing in Cognitive Radio. IEEE 21st Int. Symp. Pers. Indoor Mob. Radio Commun. Work., pp: 1–8.
- [4] Zeng, Y. and Liang, Y.C., (2009).Spectrum-sensing Algorithms for Cognitive Radio Based on Statistical Covariances. IEEE Trans. Veh. Technol., vol:58, no:4, pp:1804–1815.
- [5] Sun, H., Nallanathan, A., Wang, C.X., and Chen, Y., (2013). Wideband Spectrum Sensing for Cognitive Radio Networks: a Survey. IEEE Wirel. Commun., vol:20, no:2, pp:74–81.
- [6] Vito, L., (2013).Methods and Technologies for Wideband Spectrum Sensing. Meas. J. Int. Meas. Confed., vol:46, no:9, pp:3153–3165.
- [7] Li, Y., Winters, J.H., and Sollenberger, N.R., (2002). MIMO-OFDM for Wireless Communications: Signal Detection with Enhanced Channel Estimation. IEEE Trans. Commun., vol:50, no:9, pp:1471–1477.
- [8] Annamalai, A. and Olaluwe, A., (2013). On the Energy Detection of Unknown Signals in κ-μ And η-μ Fading Channels with Diversity Receivers. Int. Conf. Connect. Veh. Expo, ICCVE 2013 - Proc., pp: 127–132.
- [9] Gibson, A. and Zafar, J., (2008). Cyclostationary spectrum Detection in Cognitive Radios. IET Semin. Cogn. Radio Softw. Defin. Radio Technol. Tech., Vol:1, no:1, pp:17–17.
- [10] Charan, C. and Paney, R., (2016). Eigenvalue based Double Threshold Spectrum Sensing Under Noise Uncertainty for Cognitive Radio. Optik (Stuttg)., Vol:127, no:15, pp:5968–5975.
- [11] Zeng, Y. and Liang, Y.C., (2009). Eigenvalue-based Spectrum Sensing Algorithms for Cognitive Radio. IEEE Trans. Commun., vol: 57, no:6, pp: 1784–1793.
- [12] Pillay, N. and Xu H.J., (2012). Blind eigenvalue-based Spectrum Sensing for Cognitive Radio Networks. IET Commun., vol:6, no:11, pp:1388.
- [13] Kortun, A., Sellathurai, M., Ratnarajah, T., and Zhong, C., (2012). Distribution of the Ratio of the Largest Eigenvalue to the Trace of Complex Wishart Matrices. IEEE Trans. Signal Process., vol:60, no:10, pp:5527–5532.
- [14] Soltanmohammadi, E., Orooji, M., and Naraghi-Pour, M., (2013). Spectrum Sensing Over MIMO Channels Using Generalized Likelihood Ratio Tests, IEEE Signal Process. Lett., vol:20, no:5, pp:439–442.
- [15] Kortun, A., Ratnarajah, T., Sellathurai, M., Liang, Y.C., and Zeng, Y., (2014). On the Eigenvalue-Based Spectrum Sensing and Secondary User Throughput. IEEE Trans. Veh. Technol., vol:63, no:3, pp:1480–1486.
- [16] Deo, R.S., (2016). On the Tracy-Widom Approximation of Studentized Extreme Eigenvalues of Wishart Matrices. J. Multivar. Anal., vol:147, pp:265–272.
Ayrıntılar
| Birincil Dil | Türkçe |
|---|---|
| Konular | Mühendislik |
| Bölüm | Makaleler |
| Yazarlar | |
| Yayımlanma Tarihi | 31 Ocak 2019 |
| Yayımlandığı Sayı | Yıl 2019 Cilt: 14 Sayı: 1 |