Bilişsel Radyolar için Fuzzy Hipotez Test

Year 2021, , 182 - 188, 31.03.2021

Fatih Yavuz Ilgın

https://doi.org/10.18185/erzifbed.734998

Abstract

Bu makale Bilişsel Radyo sistemleri de hipotez testi için istatistiksel bir yaklaşım sunmaktadır. Hipotez testleri, algılama teorisinden radar sistemlerine kadar çok çeşitli uygulamalara sahiptir. Günümüzde spektrum kıtlığı sorununun çözümü olan Bilişsel Radyo sistemlerinde hipotez testleri sıklıkla kullanılmaktadır. Bilişsel radyo sistemlerinde hipotez testleri spektrum algılamanın temelini oluşturmaktadır. Hipotez testi ile, spektrum boşluklarının farklı kullanıcıların erişimine açılması için spektrum boşlukları belirlenir. Bu çalışmada, Bilişsel Radyo kullanıcıları tarafından tespit edilen işaret ile bulanık hipotez testine dayalı spektrum algılama yöntemi önerilmiştir. Önerilen spektrum algılama modelinin teorik temelleri ve benzetim sonuçları da verilmiştir. Benzetim çalışmaları önerilen algılama yönteminin hesaplama maliyeti avantajını ve algılama performansındaki başarısını kanıtlamaktadır.

Keywords

Bilişsel Radyo, Fuzzy Hipotez Test, Hipotez Testi, Spektrum Algılama

Fuzzy Hypothesis Test for Cognitive Radios

Abstract

This article presents a statistical approach called hypothesis testing for Cognitive Radio systems. Hyposthesis tests have a wide range of applications from detection theory to radar systems. Hypothesis testing is frequently used in Cognitive Radio systems, which are the solution to the spectrum shortage problem today. Hypothesis testing in Cognitive Radio systems is the basis of spectrum detection. By means of hypothesis testing, spectrum gaps are determined so that spectrum gaps are opened to different users' access. In this study, a fuzzy hypothesis test based spectrum detection method is proposed with the signal detected by Cognitive Radio users. Theoretical bases and simulation results of the proposed spectrum detection model are also given. Simulation studies prove the computational cost advantage and detection performance success of the proposed detection method.

Keywords

Cognitive Radio, Fuzzy Hypothesis Test, Hypothesis Test, Spectrum Sensing

References

• Abdalrazik, A., Soliman, H., Abdelkader, M. F., & Abuelfadl, T. M. 2016. Power performance enhancement of underlay spectrum sharing using microstrip patch ESPAR antenna. Advances in Electrical and Computer Engineering, 2016-Septe(1), 61–68. https://doi.org/10.1109/WCNC.2016.7565095
• Akyildiz, I. F., Lo, B. F., & Balakrishnan, R. 2011. Cooperative spectrum sensing in cognitive radio networks: A survey. In Physical Communication. https://doi.org/10.1016/j.phycom.2010.12.003
• 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
• Bao, Z., Pan, G., & Zhou, W. 2012. Tracy-Widom law for the extreme eigenvalues of sample correlation matrices. Electronic Journal of Probability, 17, 1–32. https://doi.org/10.1214/EJP.v17-1962
• Bazerque, J. A., & Giannakis, G. B. 2010. Distributed spectrum sensing for cognitive radio networks by exploiting sparsity. IEEE Transactions on Signal Processing, 58(3), 1847–1862. https://doi.org/10.1109/TSP.2009.2038417
• Chen, Z., & Zhang, Y. 2018. Cooperative energy detection algorithm based on background noise and direction finding error. AEU - International Journal of Electronics and Communications, 95, 326–341. https://doi.org/10.1016/j.aeue.2018.08.029
• Çiflikli, C., & Ilgin, F. Y. 2018. Covariance Based Spectrum Sensing with Studentized Extreme Eigenvalue. Technical Gazette, 25(6), 100–106.
• Dahlman, E., Parkvall, S., & Skold, J. 2013. 4G: LTE/LTE-Advanced for Mobile Broadband. In 4G: LTE/LTE-Advanced for Mobile Broadband. https://doi.org/10.1016/C2013-0-06829-6
• Dibal, P. Y., Onwuka, E. N., Agajo, J., & Alenoghena, C. O. 2018. Application of wavelet transform in spectrum sensing for cognitive radio: A survey. Physical Communication, 28, 45–57. https://doi.org/10.1016/j.phycom.2018.03.004
• 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
• Parchami, A., Taheri, S. M., Gildeh, B. S., & Mashinchi, M. 2016. A Simple but Efficient Approach for Testing Fuzzy Hypotheses. Journal of Uncertainty Analysis and Applications, 4(1). https://doi.org/10.1186/s40467-015-0042-8
• Shi-Qi, L., Bin-Jie, H., & Xian-Yi, W. 2012. Hierarchical cooperative spectrum sensing based on double thresholds energy detection. Communications Letters, IEEE, 16(7), 1096–1099. https://doi.org/10.1109/LCOMM.2012.050112.120765
• Torabi, H., & Behboodian, J. 2007. Likelihood ratio tests for fuzzy hypotheses testing. Statistical Papers, 48(3), 509–522. https://doi.org/10.1007/s00362-006-0352-5
• Ying-Chang Liang, Yonghong Zeng, Peh, E. C. Y., & Anh Tuan Hoang. 2008. Sensing-throughput tradeoff for cognitive radio networks. IEEE Transactions on Wireless Communications, 7(4), 1326–1337. https://doi.org/10.1109/TWC.2008.060869
• Yonghong Z., Ying-Chang L., & Rui Z. 2008. Blindly combined energy detection for spectrum sensing in cognitive radio. IEEE Signal Processing Letters, 15(1), 649–652. https://doi.org/10.1109/LSP.2008.2002711
• 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