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Difüzyon yolu ile moleküler haberleşme modelinin birikimli dağılım fonksiyonları ile analizi

Year 2024, Volume: 39 Issue: 4, 2353 - 2362, 20.05.2024
https://doi.org/10.17341/gazimmfd.1296267

Abstract

Nano boyutlu cihazlar (nano makineler) arasında yeni bir iletişim yöntemi olan Moleküler Haberleşme (MOH), son donemde literatürde artarak ilgi görmektedir. Alıcıya ulaşan moleküllerin sayısı ve molekül girişim oranı gibi faktörleri analiz etmek için çok sayıda MOH modeli kullanılmıştır. Bununla birlikte, mevcut MOH modellerinde gözlemlenen ortak bir eğilim, taşıyıcı moleküllerin difüzyon ortamı içindeki hareketini açıklamak için Normal dağılım fonksiyonunun baskın olarak kullanılmasıdır. Mevcut literatürün aksine, bu çalışma optimum performansa sahip MOH modelini belirlemek için alınan molekül sayısını dikkate alarak moleküllerin difüzyon ortamındaki hareketi için alternatif dağılım fonksiyonlarını kapsamlı bir şekilde araştırmayı amaçlamaktadır. Çalışma, literatürde kapsamlı bir şekilde araştırılan sistem ve çevresel parametrelerin iyileştirilmesine odaklanarak MOH sisteminin performansının önemli ölçüde artırılabileceğini öngörmektedir. Sonuç olarak, bu araştırma mevcut bilgi birikimine değerli iç görüler katmaya çalışmaktadır. Bu çalışmada, uç değer dağılımı (EVRND), normal dağılım (NRND), t-dağılım (TRND), genelleştirilmiş uç değer dağılım (GEVRND) ve genelleştirilmiş Pareto (GPRND) rastgele dağılım fonksiyonları, haberleşme sisteminin performansını önemli ölçüde etkileyen farklı sistem parametreleri ile karşılaştırılarak en iyi MOH modeli bulunmaya çalışılmıştır. Analizler, GPRND dağılımının en yüksek performansı, NRND dağılımının ise en kötü performansı gösterdiğini ortaya koymuştur. Literatürdeki MOH modellerinin analizinde NRND dağılımının yaygın kullanımı göz önüne alındığında, bu çalışmanın önemi bir kez daha ortaya çıkmaktadır.

References

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Year 2024, Volume: 39 Issue: 4, 2353 - 2362, 20.05.2024
https://doi.org/10.17341/gazimmfd.1296267

Abstract

References

  • 1. Akyildiz, I. F., Brunetti, F., Blázquez, C., Nanonetworks: A new communication paradigm. Comput. Networks, 52, 2260–2279, 2008.
  • 2. Nakano, T., Andrew W. E., Molecular Communication., Cambridge University Press, 2013.
  • 3. Isik I., Tagluk M.E., Isik E., Interference and molecule reception probability analysis in nano/micro scale communication systems using Fick’s diffusion law, Journal of the Faculty of Engineering and Architecture of Gazi University, 37 (2), 967–983, 2022.
  • 4. Isik, I., Yilmaz, H. B., Demirkol, I., Tagluk, M. E., Effect of receiver shape and volume on the Alzheimer disease for molecular communication via diffusion, IET nanobiotechnology, 14, 602–608, 2020.
  • 5. Isik, I., How mobility of transmitter and receiver affects the communication quality, 12, 0–1, 2022.
  • 6. Isik, E., Analyzing of the diffusion constant on the nano-scale systems by using artificial neural networks, AIP Adv. 11, 2021.
  • 7. Yilmaz, H. B., Cho, Y., Guo, W., Chae, C., Interference reduction via enzyme deployment for molecular communication, Electronics letters, 52, 2016.
  • 8. Farsad, N., Yilmaz, H. B., Eckford, A., Chae, C.-B., Guo, W., A Comprehensive Survey of Recent Advancements in Molecular Communication, IEEE Communications Surveys Tutorials, 18, 3, 2014.
  • 9. Huang, X., Fang, Y., Noel, A., Yang, N., Channel characterization for 1-D molecular communication with two absorbing receivers. IEEE Commun. Lett. 24, 1150–1154, 2020.
  • 10. Kumar, S,. Nanomachine Localization in a Diffusive Molecular Communication System. IEEE Syst. J. 14, 3011–3014, 2020.
  • 11. Harvey L., Arnold B., S Lawrence Z., Paul M., David B., J. D., Molecular Cell Biology, 29, 2000.
  • 12. Kitano, H., Computational systems biology, Nature, 420, 206–210, 2002.
  • 13. Okaie, Y., Ishiyama, S., Hara, T., Leader-Follower-Amplifier Based Mobile Molecular Communication Systems for Cooperative Drug Delivery, IEEE Glob. Commun. Conf. GLOBECOM 2018 - Proc. 1–6, 2018.
  • 14. Lin, L., Wu, Q., Ma, M., Yan, H., Concentration-based demodulation scheme for mobile receiver in molecular communication, Nano Commun. Network, 20, 11–19, 2019.
  • 15. Barros, M. T., Silva, W., Regis, C. D. M., The Multi-Scale Impact of the Alzheimer’s Disease in the Topology Diversity of Astrocytes Molecular Communications Nanonetworks., IEEE Accsess, 1–16, 2018.
  • 16. Bi, D., Almpanis, A., Noel, A., Deng, Y., Schober, R., A Survey of Molecular Communication in Cell Biology: Establishing a New Hierarchy for Interdisciplinary Applications, IEEE Commun. Surv. Tutorials, 1–53, 2021.
  • 17. Chouhan, L., Sharma, P. K., Molecular communication in three-dimensional diffusive channel with mobile nanomachines. Nano Commun. Netw. 24, 100296, 2020.
  • 18. Li, B., Sun, M., Wang, S., Guo, W., Zhao, C. Local Convexity Inspired Low-Complexity Noncoherent Signal Detector for Nanoscale Molecular Communications, IEEE Trans. Commun, 64, 2079–2091, 2016.
  • 19. Farsad, N., Eckford, A. W., Hiyama, S., Moritani, Y., On-chip molecular communication: Analysis and design. IEEE Trans. Nanobioscience, 11, 304–314, 2012.
  • 20. Balevi, E., Akan, O. B., Physical Channel Model for Nanoscale Neuro-Spike Communications, IEEE Transactions on Communications, 61, 1178–1187, 2013.
  • 21. Normal distribution, https://en.wikipedia.org/wiki/Normal_distribution, Erişim tarihi Kasım 15, 2019.
  • 22. Generalized extreme value distribution. https://en.wikipedia.org/wiki/Generalized_extreme_value_distribution, Erişim tarihi Eylül 15, 2022.
  • 23. Peel, D., Robust mixture modelling using the t distribution, Statistics and Computing, 10, 339–348, 2000.
  • 24. Taylor, P. et al,. Robust Statistical Modeling Using the t Distribution, Journal of the American Statistical Association, 84, 37–41, 1989.
  • 25. Fernanda L. Schumacher, Larissa A. Matos, Celso R. B. Cabral, Canonical fundamental skew-t linear mixed models, arXiv:2109.12152, 2021.
  • 26. Student t distribution. https://en.wikipedia.org/wiki/Student%27s_t-distribution, Erişim tarihi Eylül 25, 2021.
  • 27. Bercher, J., Tsallis distribution as a standard maximum entropy solution with ‘ tail ’ constraint, 372, 5657–5659, 2008.
  • 28. Ates, A., Akpamukcu, M., Modified monarch butterfly optimization with distribution functions and its application for 3 DOF Hover flight system. Neural Comput. Appl. 34, 3697–3722, 2022.
  • 29. Seyyarer E., Karci A., Ateş A., Effects of the stochastic and deterministic movements in the optimization processes, Journal of the Faculty of Engineering and Architecture of Gazi University, 37 (2), 949–965, 2022.
  • 30. Moore, M. J., Suda, T., Oiwa, K., Molecular Communication : Modeling Noise Effects on Information Rate, 8, 169-180, 2009.
  • 31. Yilmaz, H. B., Chae, C., Simulation Modelling Practice and Theory Simulation study of molecular communication systems with an absorbing receiver, Simul. Model. Pract. Theory, 49, 136–150, 2014.
  • 32. Akkaya, A., Yilmaz, H. B., Chae, C. B., Tugcu, T., Effect of receptor density and size on signal reception in molecular communication via diffusion with an absorbing receiver, IEEE Commun. Letter, 19, 155–158, 2015.
  • 33. Iwasaki, S., Yang, J. & Nakano, T. A Mathematical Model of Non-Diffusion-Based Mobile Molecular Communication Networks, IEEE Commun. Letter, 21, 1969–1972, 2017.
  • 34. Walsh, F., Protocols for Molecular Communication, Waterford Institute of Technology, Doktora tezi, 2013.
There are 34 citations in total.

Details

Primary Language Turkish
Subjects Engineering
Journal Section Makaleler
Authors

İbrahim Işık 0000-0003-1355-9420

Esme Işık 0000-0002-6179-5746

Abdullah Ateş 0000-0002-4236-6794

Early Pub Date May 17, 2024
Publication Date May 20, 2024
Submission Date May 12, 2023
Acceptance Date November 12, 2023
Published in Issue Year 2024 Volume: 39 Issue: 4

Cite

APA Işık, İ., Işık, E., & Ateş, A. (2024). Difüzyon yolu ile moleküler haberleşme modelinin birikimli dağılım fonksiyonları ile analizi. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, 39(4), 2353-2362. https://doi.org/10.17341/gazimmfd.1296267
AMA Işık İ, Işık E, Ateş A. Difüzyon yolu ile moleküler haberleşme modelinin birikimli dağılım fonksiyonları ile analizi. GUMMFD. May 2024;39(4):2353-2362. doi:10.17341/gazimmfd.1296267
Chicago Işık, İbrahim, Esme Işık, and Abdullah Ateş. “Difüzyon Yolu Ile moleküler haberleşme Modelinin Birikimli dağılım Fonksiyonları Ile Analizi”. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi 39, no. 4 (May 2024): 2353-62. https://doi.org/10.17341/gazimmfd.1296267.
EndNote Işık İ, Işık E, Ateş A (May 1, 2024) Difüzyon yolu ile moleküler haberleşme modelinin birikimli dağılım fonksiyonları ile analizi. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi 39 4 2353–2362.
IEEE İ. Işık, E. Işık, and A. Ateş, “Difüzyon yolu ile moleküler haberleşme modelinin birikimli dağılım fonksiyonları ile analizi”, GUMMFD, vol. 39, no. 4, pp. 2353–2362, 2024, doi: 10.17341/gazimmfd.1296267.
ISNAD Işık, İbrahim et al. “Difüzyon Yolu Ile moleküler haberleşme Modelinin Birikimli dağılım Fonksiyonları Ile Analizi”. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi 39/4 (May 2024), 2353-2362. https://doi.org/10.17341/gazimmfd.1296267.
JAMA Işık İ, Işık E, Ateş A. Difüzyon yolu ile moleküler haberleşme modelinin birikimli dağılım fonksiyonları ile analizi. GUMMFD. 2024;39:2353–2362.
MLA Işık, İbrahim et al. “Difüzyon Yolu Ile moleküler haberleşme Modelinin Birikimli dağılım Fonksiyonları Ile Analizi”. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, vol. 39, no. 4, 2024, pp. 2353-62, doi:10.17341/gazimmfd.1296267.
Vancouver Işık İ, Işık E, Ateş A. Difüzyon yolu ile moleküler haberleşme modelinin birikimli dağılım fonksiyonları ile analizi. GUMMFD. 2024;39(4):2353-62.