Çok Antenli Bilişsel Radyolarda GLRD Tabanlı Spektrum Algılama

Abstract

Bilindiği gibi, çok antenli Bilişsel Radyo sistemleri için özdeğer tabanlı spektrum algılama yöntemleri algılanacak sinyale ilişkin önceden hiçbir bilgi gerektirmemesi nedeni ile oldukça tercih edilen bir yöntemdir. Bunun yanısıra özdeğer tabanlı algılama yöntemleri genellikle gürültü belirsizliği faktöründen en az etkilenen yöntemlerdir. Özdeğer tabanlı algılama yöntemlerinde algılama performansı, test istatistiğinin doğru hesaplanmasına ve eşik değerine bağlıdır. Bu çalışmada genelleştirilmiş en çok olabilirlik tabanlı algılama(Generalized Likelihood Ratio Detection- GLRD) yöntemlerinde farklı eşik değerlerinin performans değerlendirilmesi amaçlanmıştır. Eşik değeri hesaplanırken, Wishart matrisleri için farklı olasılık dağılım fonksiyonları kullanılarak yanlış algılama olasılığı (Pfa) ve eşik değeri teorik olarak verilmiştir. Benzetim çalışmaları, MIMO-OFDM sistemleri için gürültü belirsizliği altında gerçekleştirilmiştir. Ayrıca benzetim çalışması sonuçlarında en yaygın spektrum algılama yöntemlerinden olan enerji algılamaya da yer verilmektedir. Yapılan benzetim çalışmaları farklı gürültü seviyeleri için verilmektedir. Alınan sonuçlara göre iyileştirilmiş GLRD yönteminin başarılı sonuçlar verdiği gözlenmiştir.

Keywords

• Bilişsel Radyo,GLRD Eşik Değeri,MIMO-OFDM Sistemleri,Tracy-Widom Dağılımı,Spektrum verimliliği

Multiple Antenna Spectrum Sensing Based on GLR Detector in Cognitive Radios

Abstract

As it is known, eigenvalue-based spectrum detection methods are very preferred method for multi-antenna Cognitive Radio systems since they do not require any prior knowledge of the signal to be detected. In addition, eigenvalue based detection methods are generally the least affected by the noise uncertainty factor. In eigenvalue-based detection methods, detection performance depends on the correct calculation of the test statistics and the threshold value. In this study, it is aimed to evaluate the performance of different threshold values in generalized Likelihood Ratio Detection (GLRD) methods. While calculating the threshold value, misperception probability (Pfa) and threshold value are given theoretically using different probability distribution functions for Wishart matrices. Simulation studies were carried out under noise uncertainty for MIMO-OFDM systems. In addition, energy detection, which is one of the most common spectrum sensing methods, is included in the results of the simulation study. Simulation studies are given for different noise levels. According to the results, it is observed that the improved GLRD method gives successful results.

Keywords

• Cognitive Radio,GLRD Threshold Value,MIMO-OFDM Systems,Tracy-Widom Distribution,Spectrum efficiency

References

1. Abbas T., Masoumeh Nasiri-Kenari, S. G. (2010). Multiple antenna spectrum sensing in cognitive radios. IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, 9(2), 814–823.
2. Bandari, S. K., Vakamulla, V. M., & Drosopoulos, A. (2018). GFDM/OQAM performance analysis under Nakagami fading channels. Physical Communication, 26, 162–169. https://doi.org/10.1016/J.PHYCOM.2017.12.008
3. Çiflikli, C., & Ilgin, F. Y. (2018). Covariance Based Spectrum Sensing with Studentized Extreme Eigenvalue. Technical Gazette, 25(6), 100–106.
4. Deo, R. S. (2016a). On the Tracy-Widom approximation of studentized extreme eigenvalues of Wishart matrices. Journal of Multivariate Analysis, 147, 265–272. https://doi.org/10.1016/j.jmva.2016.01.010
5. Deo, R. S. (2016b). On the Tracy-Widom approximation of studentized extreme eigenvalues of Wishart matrices. Journal of Multivariate Analysis, 147, 265–272. https://doi.org/10.1016/j.jmva.2016.01.010
6. He, Y., Ratnarajah, T., Yousif, E. H. G., Xue, J., & Sellathurai, M. (2016). Performance analysis of multi-antenna GLRT-based spectrum sensing for cognitive radio. Signal Processing, 120, 580–593. https://doi.org/10.1016/j.sigpro.2015.10.018
7. Kortun, A., Ratnarajah, T., Sellathurai, M., Liang, Y. C., & Zeng, Y. (2014). On the eigenvalue-based spectrum sensing and secondary user throughput. IEEE Transactions on Vehicular Technology, 63(3), 1480–1486. https://doi.org/10.1109/TVT.2013.2282344
8. Kortun, A., Sellathurai, M., Ratnarajah, T., & Zhong, C. (2012). Distribution of the ratio of the largest eigenvalue to the trace of complex wishart matrices. IEEE Transactions on Signal Processing, 60(10), 5527–5532. https://doi.org/10.1109/TSP.2012.2205922

9. Lavanya, S., & Bhagyaveni, M. A. (2019). EVM based rate maximized relay selection for cooperative cognitive radio networks. AEU - International Journal of Electronics and Communications, 104, 86–90. https://doi.org/10.1016/j.aeue.2018.12.018
10. Liu, Z., Wang, Y. (2017). A note on spiked Wishart matrices. Statistics and Probability Letters, 127, 1–6. https://doi.org/10.1017/CBO9781107415324.004
11. Maaref, A., & Aïssa, S. (2007). Eigenvalue distributions of wishart-type random matrices with application to the performance analysis of MIMO MRC systems. IEEE Transactions on Wireless Communications, 6(7), 2678–2689. https://doi.org/10.1109/TWC.2007.05990
12. QI, Y., PENG, T., WANG, W., & LUO, S. (2009). Cyclostationary signature design for common control channel of cognitive radio. The Journal of China Universities of Posts and Telecommunications, 16(2), 42–46. https://doi.org/10.1016/S1005-8885(08)60202-2
13. S, A. P., & Jayasheela, M. (2012). Cyclostationary feature detection in cognitive radio using different modulation schemes. International Journal of Computer Applications, 47(21), 975–8887. https://doi.org/10.7763/IJFCC.2013.V2.249
14. Verma, P., & Singh, B. (2016). Overcoming sensing failure problem in double threshold based cooperative spectrum sensing. Optik, 127(10), 4200–4204. https://doi.org/10.1016/j.ijleo.2016.01.108
15. Wei, L., Tirkkonen, O., & McKay, M. R. (1993). Exact demmel condition number distribution of complex wishart matrices via the mellin transform. IEEE Transactions on Aerospace and Electronic Systems, 29(3), 834–840. https://doi.org/10.1109/7.220934
16. Zeng, Y., & Liang, Y. C. (2009). Spectrum-sensing algorithms for cognitive radio based on statistical covariances. IEEE Transactions on Vehicular Technology, 58(4), 1804–1815. https://doi.org/10.1109/TVT.2008.2005267