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Estimation of Gauss Mixing Models with Expectation Maximum in Cognitive Radios

Year 2020, Issue: 19, 588 - 595, 31.08.2020
https://doi.org/10.31590/ejosat.726040

Abstract

Nowadays, it is known that there is a constantly increasing demand for the radio frequency spectrum. The biggest reason for this situation is the constantly increasing data sizes in wireless communication systems. Therefore, Cognitive Radio (BR) systems are potential technologies that can be a solution to future spectrum shortage problems. In the BR systems, the probability distribution function of the signal to be detected is utilized at the basis of spectrum detection. The probability distribution functions of the signals perceived by BR users according to their spatial location, which will perform spectrum detection in wireless communication systems, vary. The purpose of this study is to predict the common Gaussian mix model of signals perceived by different BR users using the expectation maximization algorithm. In the study, simulation results were performed according to different noise levels and different number of cognitive radio users in terms of spatial differences of different BR users. Thus, the estimated Gaussian mix models can be used to predict different spectrum sensing models.

References

  • Akyildiz, I. F., Lo, B. F., ve 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
  • Bailey, T. L., ve Elkan, C. 1994. Fitting a mixture model by expectation maximization to discover motifs in biopolymers. Proceedings / ... International Conference on Intelligent Systems for Molecular Biology ; ISMB. International Conference on Intelligent Systems for Molecular Biology.
  • Bao, Z., Pan, G., ve 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., ve 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
  • Cappé, O., ve Moulines, E. 2009. On-line expectation-maximization algorithm for latent data models. Journal of the Royal Statistical Society. Series B: Statistical Methodology. https://doi.org/10.1111/j.1467-9868.2009.00698.x
  • Çiflikli, C., ve Ilgin, F. Y. 2018. Covariance Based Spectrum Sensing with Studentized Extreme Eigenvalue. Technical Gazette, 25(6), 100–106.
  • Garriga, J., Palmer, J. R. B., Oltra, A., ve Bartumeus, F. 2016. Expectation-maximization binary clustering for behavioural annotation. PLoS ONE. https://doi.org/10.1371/journal.pone.0151984
  • Greff, K., Van Steenkiste, S., ve Schmidhuber, J. 2017. Neural expectation maximization. Advances in Neural Information Processing Systems.
  • Kortun, A., Ratnarajah, T., Sellathurai, M., Liang, Y. C., ve 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
  • Lavanya, S., ve 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
  • Li, C. M., ve 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
  • Lorincz, J., Ramljak, I., ve Begušić, D. 2019. A review of the noise uncertainty impact on energy detection with different OFDM system designs. Computer Communications, 148, 185–207. https://doi.org/10.1016/J.COMCOM.2019.09.013
  • Mahmoud, M., ve Xia, Y. 2014. Expectation Maximization. In Networked Filtering and Fusion in Wireless Sensor Networks. https://doi.org/10.1201/b17667-6
  • Rasmussen, C. E. 2000. The infinite Gaussian mixture model. Advances in Neural Information Processing Systems.
  • Aparna, P., ve 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., ve 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
  • Soltanmohammadi, E., Orooji, M., ve Naraghi-Pour, M. 2013. Spectrum sensing over MIMO channels using generalized likelihood ratio tests. IEEE Signal Processing Letters, 20(5), 439–442. https://doi.org/10.1109/LSP.2013.2250499
  • Souid, I., Ben Chikha, H., Dayoub, I., ve Attia, R. 2017. MIMO relaying networks for cooperative spectrum sensing: False alarm and detection probabilities. Physical Communication, 25, 194–200. https://doi.org/10.1016/j.phycom.2017.07.006
  • Ying-Chang Liang, Yonghong Zeng, Peh, E. C. Y., ve 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

Bilişsel Radyolarda Beklenti Maksimizasyonu ile Gauss Karışım Modelleri Kestirimi

Year 2020, Issue: 19, 588 - 595, 31.08.2020
https://doi.org/10.31590/ejosat.726040

Abstract

Günümüzde radyo frekans spektrumuna sürekli artan bir talep olduğu bilinmektedir. Bu durumun en büyük sebebi kablosuz haberleşme sistemlerinde iletilen veri boyutlarının sürekli artmasıdır. Bu nedenle Bilişsel Radyo (BR) sistemleri gelecekte yaşanacak spektrum kıtlığı problemlerine çözüm olabilecek potansiyel teknolojilerdir. BR sistemlerinde spektrum algılamanın temelinde algılanacak işaretin olasılık dağılım fonksiyonundan faydalanılmaktadır. Kablosuz haberleşme sistemlerinde spektrum algılama işlemini gerçekleştirecek olan BR kullanıcılarının uzaysal konumuna göre algıladığı işaretlerin olasılık dağılım fonksiyonları değişiklik göstermektedir. Bu çalışmanın amacı beklenti maksimizasyonu algoritması kullanarak farklı BR kullanıcıları tarafından algılanan işaretlerin, ortak Gauss karışım modelinin tahmin edilmesidir. Yapılan çalışmada benzetim sonuçları farklı BR kullanıcılarının uzaysal farklılıklarını içermesi açısından farklı gürültü seviyeleri ve farklı sayıda Bilişsel Radyo kullanıcı sayısına göre gerçekleştirilmiştir. Böylece tahmin edilen Gauss karışım modelleri kestirimi farklı spektrum algılama modellerine temel oluşturmak için kullanılabilir.

References

  • Akyildiz, I. F., Lo, B. F., ve 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
  • Bailey, T. L., ve Elkan, C. 1994. Fitting a mixture model by expectation maximization to discover motifs in biopolymers. Proceedings / ... International Conference on Intelligent Systems for Molecular Biology ; ISMB. International Conference on Intelligent Systems for Molecular Biology.
  • Bao, Z., Pan, G., ve 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., ve 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
  • Cappé, O., ve Moulines, E. 2009. On-line expectation-maximization algorithm for latent data models. Journal of the Royal Statistical Society. Series B: Statistical Methodology. https://doi.org/10.1111/j.1467-9868.2009.00698.x
  • Çiflikli, C., ve Ilgin, F. Y. 2018. Covariance Based Spectrum Sensing with Studentized Extreme Eigenvalue. Technical Gazette, 25(6), 100–106.
  • Garriga, J., Palmer, J. R. B., Oltra, A., ve Bartumeus, F. 2016. Expectation-maximization binary clustering for behavioural annotation. PLoS ONE. https://doi.org/10.1371/journal.pone.0151984
  • Greff, K., Van Steenkiste, S., ve Schmidhuber, J. 2017. Neural expectation maximization. Advances in Neural Information Processing Systems.
  • Kortun, A., Ratnarajah, T., Sellathurai, M., Liang, Y. C., ve 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
  • Lavanya, S., ve 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
  • Li, C. M., ve 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
  • Lorincz, J., Ramljak, I., ve Begušić, D. 2019. A review of the noise uncertainty impact on energy detection with different OFDM system designs. Computer Communications, 148, 185–207. https://doi.org/10.1016/J.COMCOM.2019.09.013
  • Mahmoud, M., ve Xia, Y. 2014. Expectation Maximization. In Networked Filtering and Fusion in Wireless Sensor Networks. https://doi.org/10.1201/b17667-6
  • Rasmussen, C. E. 2000. The infinite Gaussian mixture model. Advances in Neural Information Processing Systems.
  • Aparna, P., ve 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., ve 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
  • Soltanmohammadi, E., Orooji, M., ve Naraghi-Pour, M. 2013. Spectrum sensing over MIMO channels using generalized likelihood ratio tests. IEEE Signal Processing Letters, 20(5), 439–442. https://doi.org/10.1109/LSP.2013.2250499
  • Souid, I., Ben Chikha, H., Dayoub, I., ve Attia, R. 2017. MIMO relaying networks for cooperative spectrum sensing: False alarm and detection probabilities. Physical Communication, 25, 194–200. https://doi.org/10.1016/j.phycom.2017.07.006
  • Ying-Chang Liang, Yonghong Zeng, Peh, E. C. Y., ve 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
There are 19 citations in total.

Details

Primary Language Turkish
Subjects Engineering
Journal Section Articles
Authors

Fatih Yavuz Ilgın 0000-0002-7449-4811

Publication Date August 31, 2020
Published in Issue Year 2020 Issue: 19

Cite

APA Ilgın, F. Y. (2020). Bilişsel Radyolarda Beklenti Maksimizasyonu ile Gauss Karışım Modelleri Kestirimi. Avrupa Bilim Ve Teknoloji Dergisi(19), 588-595. https://doi.org/10.31590/ejosat.726040