SavedAdd your journal

Journals / Avrupa Bilim ve Teknoloji Dergisi / 2020 / Cilt: 0 - Sayı: 19

Estıimation of Gauss Mixing Models with Expectation Maximum in Cognitive Radios

Bilişsel Radyolarda Beklenti Maksimizasyonu ile Gauss Karışım Modelleri Kestirimi

Fatih Yavuz ILGIN
Pages
588–595
Publication Date
2022-10-11
DOI
—
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Thus, the estimated Gaussian mix models can be used to predict different spectrum sensing models.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Nowadays, it is known that there is a constantly increasing demand for the radio frequency spectrum.
📊
Key Findings & Data:The biggest reason for this situation is the constantly increasing data sizes in wireless communication systems.
💡
Conclusion & Impact:Thus, the estimated Gaussian mix models can be used to predict different spectrum sensing models.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Domain Parameters

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.

Özet

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

🔖

Cite This Article

ILGIN, F. Y. (2022). Estıimation of Gauss Mixing Models with Expectation Maximum in Cognitive Radios. Avrupa Bilim ve Teknoloji Dergisi, Cilt: 0 - Sayı: 19, 588–595. https://ajindex.com/articles/457726

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (9)

  1. 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
  2. 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
  3. 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
  4. Çiflikli, C., ve Ilgin, F. Y. 2018. Covariance Based Spectrum Sensing with Studentized Extreme Eigenvalue. Technical Gazette, 25(6), 100–106.
  5. 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
  6. 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
  7. 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
  8. 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
  9. 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