İmmün Plazma Algoritması ile Bilişsel Radyo Ağlarında Çok Kullanıcılı Kanal Atama Probleminin Çözülmesi

Öz

Aralık 2019’da Çin’de başlayan ve tüm dünyayı etkisi altına alan koronavirüs (COVID-19) pandemisi olarak bilinen sağlık krizi, her ne kadar Tıbbi alanlarla doğrudan alakalı olsa da mevcut teşhis ve tedavi yöntemleri üzerine yapılan çalışmalar, bilgisayar bilimlerinden araştırmacıların da bu tedavi yöntemlerinden esinlenerek çalışmalar yapmasını mümkün kılmıştır. Bu çalışmada immün plazma tedavisinin temel işlem adımlarını dayanak noktası alan İmmün Plazma algoritması, bilişsel radyo ağlarında aynı zaman diliminde birden fazla bilişsel radyo kullanıcısının yayın yaptığı kanal atama probleminin özellikleri dikkate alınarak modifiye edilmiş ve bilişsel radyo kullanıcılarının aynı zaman dilimi içerisinde farklı kanalları kullanmalarını sağlamak amacıyla bir yöntem geliştirilerek bahsedilen problemin çözümündeki performansı incelenmiştir. Ayrıca, önerilen IPA temelli yöntem ile elde edilen sonuçlar Kaba Kuvvet arama ile elde edilen sonuçlar kullanılarak karşılaştırılmıştır. Karşılaştırma sonuçları IP algoritmasının bahsedilen arama yöntemine kıyasla daha başarılı sonuçları elde edebileceğini göstermiştir.

Anahtar Kelimeler

• Meta-sezgiseller
• İmmün Plazma algoritması
• kanal atama

Solving Multiuser Channel Assignment Problem in Cognitive Radio Networks with Immune Plasma Algorithm

Abstract

The health crisis, known as the coronavirus (COVID-19) pandemic, which started in China in December 2019 and affected the whole world, although it is directly related to the medical fields, studies on current diagnosis and treatment methods suggest that researchers from computer sciences can also use this treatment enabled him to work inspired by his methods In this study, the Immune Plasma algorithm, which is based on the basic process steps of immune plasma therapy, has been modified by considering the characteristics of the channel assignment problem in which more than one cognitive radio user broadcasts in the same time slot. A method is used to enable cognitive radio users to use different channels in the same time period developed and its performance in solving the mentioned problem was examined. In addition, the results obtained with the proposed IPA-based method are compared using the results obtained by Brute Force search. The comparison results showed that the IP algorithm can achieve more successful results compared to the mentioned search method.

Keywords

• Meta-heuristics
• Immune Plasma algorithm
• channel assignment

Kaynakça

1. Mitola, J., and Maguire, G. Q., "Cognitive Radio: Making Software Radios More Personal", IEEE Personal Communications, vol. 6, no. 4, pp. 13-18, 1999.
2. Haykin, S., "Cognitive Radio: Brain-empowered Wireless Communications", IEEE Journal on Selected Areas in Communications, vol. 23, no. 2, pp. 201–220, 2005.
3. Akyildiz, I.F., Lee, W.Y., Vuran, M.C., and Mohanty, S., "Next Generation/Dynamic Spectrum Access/Cognitive Radio Wireless Networks: A Survey", Computer Networks, vol. 50, no. 13, pp. 2127–2159, 2006.
4. Lazos, L., Liu, S., and Krunz, M., "Spectrum Opportunity-based Control Channel Assignment in Cognitive Radio Networks", in 6th Annual IEEE Communications Society Conference on Sensor, Mesh and Adhoc Communications and Networks, pp. 1-9, 2009.
5. Salameh, H. B. (2010, December). Rate-maximization channel assignment scheme for cognitive radio networks. In 2010 IEEE Global Telecommunications Conference GLOBECOM 2010 (pp. 1-5). IEEE.
6. Bayhan, S., and Alagoz, F., "Scheduling in Centralized Cognitive Radio Networks for Energy Efficiency", IEEE Transactions on Vehicular Technology, vol. 62, no. 2, pp. 582-595, 2012.
7. Ahmed, E., Gani, A., Abolfazli, S., Yao, L. J., and Khan, S. U., "Channel Assignment Algorithms in Cognitive Radio Networks: Taxonomy, Open Issues, and Challenges", IEEE Communications Surveys & Tutorials, vol. 18, no. 1, pp. 795-823, 2014.
8. Tang, F., & Li, J. (2016). Joint rate adaptation, channel assignment and routing to maximize social welfare in multi-hop cognitive radio networks. IEEE Transactions on Wireless Communications, 16(4), 2097-2110.

9. Gupta, N., and Dhurandher, S. K., "Cross-layer Perspective for Channel Assignment in Cognitive Radio Networks: A Survey", International Journal of Communication Systems, vol. 33, no. 5, 2020.
10. Akan, O.B., Karli, O.B. and Ergul, O., 2009. Cognitive Radio Sensor Networks.Netwrk. Mag. of Global Internetwkg., 23(4):34–40.
11. Dokuz, Y., "Bilişsel Radyo Uygulamaları için En Uygun Frekansın Zamansal ve Mekansal Olarak Yapay Arı Koloni Algoritması ile Belirlenmesi", Erciyes Üniversitesi Fen Bilimleri Enstitüsü, pp. 1-82, 2014.
12. Qin, Y., Zhong, X., Yang, Y., Li, L., and Ye, Y., "Combined Channel Assignment and Network Coded Opportunistic Routing in Cognitive Radio Networks", Computers & Electrical Engineering, vol. 52, pp. 293-306, 2016.
13. Wei, Z. H., and Hu, B. J., "A Fair Multi-channel Assignment Algorithm with Practical Implementation in Distributed Cognitive Radio Networks", IEEE Access, vol. 6, pp. 14255-14267, 2018.
14. Demirci, S., and Gözüpek, D., "Switching Cost-Aware Joint Frequency Assignment and Scheduling for Industrial Cognitive Radio Networks", IEEE Transactions on Industrial Informatics, vol. 16, no. 7, pp. 4365-4377, 2019.
15. Salameh, H. B., Shraideh, S., and Alshamali, A., "Joint ChannelAssignment and Adaptive Mode Selection in MIMO-based Cognitive Radio Networks", Arabian Journal for Science and Engineering, vol. 45, no. 212, pp. 10233-10244, 2020.
16. Raju, V. A., Srilatha, P., Anumandla, K. K., Patnaik, S., and Prabu, A. V., "Grasshopper Algorithm based Channel Assignment for Cognitive Radio Networks", Materials Today: Proceedings, pp. 1-6, 2020.
17. Musa, A., Bany Salameh, H., Abu Sannad, N., Halloush, R., and Darabkh, K., "Spectrum Management with Simultaneous Power-controlled Assignment Decisions in Cognitive Radio Networks", Concurrency and Computation: Practice and Experience, vol. 32, no. 21, 2020.
18. Latif, S., Akraam, S., Malik, A. J., Abbasi, A. A., Habib, M., and Lim, S., "Improved Channel Allocation Scheme for Cognitive Radio Networks", Intelligent Automation and Soft Computing, vol. 27, no. 1, pp. 103-114, 2021.
19. Sanka, S. N., Yarram, T. R., Yenumala, K. C., Anumandla, K. K., and Dabbakuti, J. K., "Dragonfly Algorithm based Spectrum Assignment for Cognitive Radio Networks", Materials Today: Proceedings, pp. 1-4, 2021.
20. Tlouyamma, J., and Velempini, M., "Channel Selection Algorithm Optimized for Improved Performance in Cognitive Radio Networks", Wireless Personal Communications, pp. 1-18, 2021.
21. Aslan, S., and Demirci, S., "Immune Plasma Algorithm: A Novel Meta-Heuristic for Optimization Problems", IEEE Access, vol. 8, pp. 220227- 220245, 2020.
22. Devi, M., Sarma, N., & Deka, SK (2021). Bilişsel Radyo Ağlarında Çok Kazanan Spektrum Tahsisi: Sıralı Teklif Verme ile Tek Taraflı Açık Artırma Teorik Modelleme Yaklaşımı. Elektronik, 10 (5), 602.
23. Yilmazel, R., & Inanç, N. (2021). A Novel Approach for Channel Allocation In OFDM Based Cognitive Radio Technology. Wireless Personal Communications, 1-15.
24. Goyal, K. ve Patra, M. (2021). Bilişsel-Radyo Destekli Araçların İnternetinde Dağıtılmış Kanal Atama. Gelen Intelligent Systems (s. 217-227). Springer, Singapur.
25. Agarwal, S., Vijay, S., & Bagwari, A. (2021). An Enhanced Spectrum Allocation Algorithm for Secondary Users in Cognitive Radio Networks.