SIMO Haberleşme Kanallarında Enerji Tabanlı İşbirlikli Spektrum Algılama

Son yıllarda haberleşme sistemlerinde yaşanan hızlı gelişim süreci, kablosuz haberleşme uygulamalarının sayısını oldukça artırmışdurumdadır. Kablosuz haberleşme sistemlerinde gerçekleşen bu gelişim, beraberinde spektrumda daha fazla band genişliği ihtiyacınıda gerektirmektedir. Band genişliği talebinin artışı ise spektrum kıtlığı problemini gün yüzüne çıkarmıştır. Spektrum kıtlığı probleminiaşmak için en geçerli yol, sabit frekans tahsisi yerine fırsatçı spektrum kullanımına geçilmesidir. Spektrumu fırsatçı kullanarakdinamik spektrum atama yöntemlerini uygulamak ise Bilişsel Radyo sistemlerinin temel amacıdır. Bilişsel Radyo bulunduğu spektrumortamını algılayarak boş spektrum bölgelerini belirleyerek bu bölgeleri ikincil kullanıcıların erişimine açmaktadır. Ikincil kullanıcıspektrum kullanımı için belirli bir ücret ödemeyen veya bir spektrum bölgesini yasal olarak kullanma hakkına sahip olmayan kişidir.Aynı şekilde lisanslı kulanıcı ise belirli bir spektrum bölgesini yasal olarak kullanma hakkına sahip kişi olarak tanımlanmaktadır. Butanımlar doğrultusunda yapılan bu çalışmada Bilişsel Radyo sistemleri için spektrum algılama yöntemlerinden biri olan, EnerjiTabanlı Spektrum Algılama için, adaptif bir spektrum algılama yöntemi önerilmiştir. Ayrıca önerilen yöntemde gürültü seviyesinintahmini için farklı bir kestirim yöntemi önerilmektedir. Bilindiği üzere enerji tabanlı algılama yöntemlerinin en büyük dezavantajıgürültü belirsizliği faktörünün algılama performansı üzerinde oluşturduğu olumsuz etkidir. Bu olumsuz etkiyi azaltmak için önerilenyöntemde ortamda bulunan gürültü, Marhenko Pastur teoremi ile tahmin edilerek, eşik değeri adaptif şekilde değiştirilmektedir.Önerilen yöntemin Rayleigh sönümlemeli tek giriş- çok çıkışlı sistemlerde benzetim çalışmaları yapılmıştır. Benzetim çalışmalarıişbirlikli ve işbirliksiz algılama yöntemleri için farklı gürültü seviyeleri için incelenmiştir. Ayrıca benzetim sonuçlarında algılamateorisi çalışmaları için sıklıkla kullanılan ROC eğrilerine de yer verilmektedir. Böylece önerilen algılama yönteminde gelenekselenerji algılama yöntemine göre olumlu sonuçlar gözlemlenmiştir.

Energy-Based Cooperative Spectrum Sensing In SIMO Communication Channels

The rapid development process in communication systems in recent years has increased the number of wireless communication applications considerably. This development in wireless communication systems requires the need for more bandwidth in the spectrum. The increase in bandwidth demand brought up the problem of spectrum shortage. The most valid way to overcome the problem of spectrum shortage is to switch to the use of opportunistic spectrum instead of fixed frequency allocation. Using spectrum opportunistically is the main purpose of Cognitive Radio systems. Cognitive Radio detects the spectrum environment in which it is located and determines the empty spectrum regions and makes these regions accessible to secondary users. The secondary user is the person who does not pay a specific fee for the use of the spectrum or does not have the legal right to use a spectrum region. Similarly, the licensed user is defined as the person who has the legal right to use a certain spectrum region. An adaptive spectrum sensing method for Energy Based Spectrum Sensing, which is one of the spectrum sensing methods for Cognitive Radio systems, is proposed in this study conducted in line with these definitions. In addition, a different estimation method is proposed for the estimation of the noise level in the proposed method. As it is known, the biggest disadvantage of energy based sensing methods is the negative effect of noise uncertainty factor on sensing performance. In the proposed method to reduce this negative effect, the noise in the environment is estimated by Marhenko Pastur theorem and the threshold value is adaptively changed. Simulation studies of single-multi-output systems with Rayleigh damping are proposed. Simulation studies have been studied for different noise levels for cooperative and noncooperative detection methods. Also included in the simulation results are ROC curves that are frequently used for detection theory studies. Thus, positive results were observed in the proposed perception method compared to traditional energy perception method.

PDF

___

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
Ahmad, A. W., Yang, H., & Lee, C. (2015). Maximizing throughput with wireless spectrum sensing network assisted cognitive radios. International Journal of Distributed Sensor Networks, 2015, 1–10. https://doi.org/10.1155/2015/195794
Charan, C., & Paney, R. (2016). Eigenvalue based double threshold spectrum sensing under noise uncertainty for cognitive radio. Optik, 127(15), 5968–5975. https://doi.org/10.1016/j.ijleo.2016.04.049
Çiflikli, C., & Ilgin, F. Y. (2018). Covariance Based Spectrum Sensing with Studentized Extreme Eigenvalue. Technical Gazette, 25(6), 100–106.
Commission, F. C. (2002). Revision of Part 15 of the Commission’s Rules Regarding Ultra-Wideband Transmission Systems. First Report and Order in ET …. https://doi.org/10.1017/CBO9781107415324.004
Dahlman, E., Parkvall, S., & Skold, J. (2013). 4G: LTE/LTE-Advanced for Mobile Broadband. 4G: LTE/LTE-Advanced for Mobile Broadband. https://doi.org/10.1016/C2013-0-06829-6
De Vito, L. (2013). Methods and technologies for wideband spectrum sensing. Measurement: Journal of the International Measurement Confederation, 46(9), 3153–3165. https://doi.org/10.1016/j.measurement.2013.06.013
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
Edelman, A. (2005). Random matrix theory. Acta Numerica, 1–65. https://doi.org/10.1017/S0962492904000236
Erpek, T., Steadman, K., & Jones, D. (2007). CR..2 A..Spectrum Occupancy Measurements: Dublin Ireland Collected On April 16-18 , 2007. In Technical Report, Shared Spectrum Company Nov 2007 (pp. 1–34). Vienna: Shared Spectrum Company.
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
Kortun, A., Ratnarajah, T., Sellathurai, M., Zhong, C., & Papadias, C. B. (2011). On the performance of eigenvalue-based cooperative spectrum sensing for cognitive radio. IEEE Journal of Selected Topics in Signal Processing, 5(1), 49–55. https://doi.org/10.1109/JSTSP.2010.2066957
Kortun, Ayse, 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
Li, C. M., & Lu, S. H. (2016). Energy-Based Maximum Likelihood Spectrum Sensing Method for the Cognitive Radio. Wireless Personal Communications, 89(1), 289–302. https://doi.org/10.1007/s11277-016-3266-0
Lu, L., Li, G. Y., Swindlehurst, A. L., Ashikhmin, A., & Zhang, R. (2014). An overview of massive MIMO: Benefits and challenges. IEEE Journal on Selected Topics in Signal Processing, 8(5), 742–758. https://doi.org/10.1109/JSTSP.2014.2317671
Luo, X., Wang, X., Zhang, M., & Guan, X. (2019). Distributed detection and isolation of bias injection attack in smart energy grid via interval observer. Applied Energy, 256, 113703. https://doi.org/10.1016/J.APENERGY.2019.113703
Mitola, J. (2006). Cognitive Radio Architecture. In Cognitive Radio Technology (pp. 435–500). Newnes. https://doi.org/10.1016/B978-075067952-7/50015-5
Mitola, J., & Maguire, G. Q. (2001). Cognitive radio: Making software radios more personal. In Software Radio Technologies: Selected Readings. https://doi.org/10.1109/9780470546444.ch4
Mohammadi, A., Javadi, S. H., Ciuonzo, D., Persico, V., & Pescapé, A. (2019). Distributed detection with fuzzy censoring sensors in the presence of noise uncertainty. Neurocomputing. https://doi.org/10.1016/j.neucom.2019.03.044
Pillay, N., & Xu, H. J. (2012). Blind eigenvalue-based spectrum sensing for cognitive radio networks. IET Communications, 6(11), 1388. https://doi.org/10.1049/iet-com.2011.0506
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
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
Szczerba, K., Westbergh, P., Agrell, E., Karlsson, M., Andrekson, P. A., & Larsson, A. (2013). Comparison of intersymbol interference power penalties for OOK and 4-PAM in short-range optical links. Journal of Lightwave Technology, 31(22), 3525–3534. https://doi.org/10.1109/JLT.2013.2285468
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
Yaskov, P. (2016). A short proof of the Marchenko–Pastur theorem. Comptes Rendus Mathematique, 354(3), 319–322. https://doi.org/10.1016/J.CRMA.2015.12.008
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). Eigenvalue-based spectrum sensing algorithms for cognitive radio. IEEE Transactions on Communications, 57(6), 1784–1793. https://doi.org/10.1109/TCOMM.2009.06.070402