Journals / Afyon Kocatepe Üniversitesi Fen ve Mühendislik Bilimleri Dergisi / 2024 / Cilt: 24 Sayı: 6

Improving the Call Performance of Secondary Users by Utilizing a Buffer-Based Channel Allocation Approach in Cognitive Radio Networks

Bilişsel Radyo Ağlarında Arabellek Tabanlı Kanal Tahsis Yaklaşımı ile İkincil Kullanıcıların Çağrı Başarımlarının İyileştirilmesi

Sedat Atmaca(MUĞLA SITKI KOÇMAN ÜNİVERSİTESİ)0000-0003-0229-4893
Pages
1346–1354
Publication Date
2024-12-02
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AI Summary & Key Takeaways

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

According to the obtained results, when the SU arrival rate is 2, there is approximately a 55% improvement in SU call blocking probability, a 61% improvement in SU call dropping probability, and approximately a 50% improvement in SU spectrum handover probability.

📌 3 Key Takeaways

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Objective & Hypothesis:In this presented work, a Cognitive Radio (CR) network model where Primary Users (PUs) and Secondary Users (SUs) coexist within the same communication area and SUs utilize the licensed spectrum opportunistically was developed, analyzed, and its performance was comparatively evaluated with a peer network model.
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Key Findings & Data:The performance of the proposed CR network model was analyzed using a three-dimensional continuous-time Markov chain.
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Conclusion & Impact:According to the obtained results, when the SU arrival rate is 2, there is approximately a 55% improvement in SU call blocking probability, a 61% improvement in SU call dropping probability, and approximately a 50% improvement in SU spectrum handover probability.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Bilişsel radyo · kaynak tahsisi · Ortam erişim kontrol

Abstract

In this presented work, a Cognitive Radio (CR) network model where Primary Users (PUs) and Secondary Users (SUs) coexist within the same communication area and SUs utilize the licensed spectrum opportunistically was developed, analyzed, and its performance was comparatively evaluated with a peer network model. In the proposed CR network model, the buffer usage is envisaged for SUs whose calls are blocked due to limited spectrum and for SUs whose connections drop due to incoming PU calls. The performance of the proposed CR network model was analyzed using a three-dimensional continuous-time Markov chain. Additionally, a Monte Carlo simulation was developed for the CR network model, and the results obtained from the simulation model were validated by the results obtained from the analytical model. In the proposed model, SU call blocking probability, SU call dropping probability, and SU spectrum handover probability were considered as performance metrics. The results obtained from the proposed model were compared with those obtained from a peer model not utilizing buffers, presented in the literature. According to the obtained results, when the SU arrival rate is 2, there is approximately a 55% improvement in SU call blocking probability, a 61% improvement in SU call dropping probability, and approximately a 50% improvement in SU spectrum handover probability.

Özet

Bu makalede sunulan çalışmada, Birincil Kullanıcıların (BK) ve İkincil Kullanıcıların (İK) aynı iletişim alanı içerisinde birlikte bulunduğu ve İK’ların lisanslı spektrumu fırsatçı bir yaklaşımla kullandığı bir Bilişsel Radyo (BR) ağ modeli geliştirilmiş, analiz edilmiş ve başarımı eş bir ağ modeli ile karşılaştırmalı değerlendirilmiştir. Önerilen ağ modelinde spektrum yetersizliğinden çağrıları engellenen İK’lar ve yeni gelen BK çağrılarından dolayı bağlantısı düşen İK’lar için arabellek kullanımı öngörülmüştür. Önerilen BR ağ modelinin başarımı üç boyutlu sürekli Markov zinciri kullanılarak analiz edilmiştir. Ayrıca, geliştirilen BR ağ modelinin Monte-Carlo benzetimi de gerçekleştirilerek elde edilen sonuçlarla, analitik modelden elde edilen sonuçlar doğrulanmıştır. Önerilen modelde İK çağrı engelleme olasılığı, İK çağrı düşme olasılığı ve İK spektrum el-değiştirme olasılığı başarım metrikleri olarak benimsenmiştir. Karşılaştırmalı başarım sonuçlara göre, İK varış hızı 2 olduğunda, İK çağrı engelleme olasılığında %55, İK çağrı düşme olasılığında %61 ve İK spektrum el-değiştirme olasılığında yaklaşık %50 oranında iyileştirme elde edilmiştir.

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Cite This Article

Atmaca, S. (2024). Improving the Call Performance of Secondary Users by Utilizing a Buffer-Based Channel Allocation Approach in Cognitive Radio Networks. Afyon Kocatepe Üniversitesi Fen ve Mühendislik Bilimleri Dergisi, Cilt: 24 Sayı: 6, 1346–1354. https://doi.org/10.35414/akufemubid.1433084

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References (17)

  1. Bayrakdar, M. E., and Çalhan, A., 2015. Bilişsel radyo ağlarında spektrum el değiştirme. Sakarya University Journal of Science, 19(3), 291-302. https://doi.org/10.16984/saufenbilder.81445
  2. Goel, S., Kulshrestha, R., 2022. Queueing based spectrum management in cognitive radio networks with retrial and heterogeneous service classes. Journal of Ambient Intelligence and Humanized Computing, 13, 2429–2437. https://doi.org/10.1007/s12652-021-03442-z
  3. Hassani, M. M., and Berangi, R., 2019. Impact of the primary user on the secondary user blocking probability in cognitive radio sensor networks. Turkish Journal of Electrical Engineering and Computer Sciences, 27(3), 2081-2092. https://doi.org/10.3906/elk-1706-292
  4. Haykin, S., 2005. Cognitive radio: brain‒empowered wireless communications. IEEE Journal of Selected Areas in Communications, 23(2), 201–220. https://doi.org/10.1109/JSAC.2004.839380
  5. Kulkarni, K., and Adrish, B., 2017. On Optimal Spectrum Access of Cognitive Relay with Finite Packet Buffer. IEEE Transactions on Vehicular Technology, 66, 7584-7588.
  6. Lee, Y., Park, G. C., Sim, D.B., 2012. Cognitive radio spectrum access with prioritized secondary users, Applied Mathematics & Information Sciences, 6(2), 595–601.
  7. Mitola, J., and Maguire, G. Q., 1999. Cognitive radio: making software radios more personal. IEEE Personal Communications, 6(4), 13-18. https://doi.org/10.1109/98.788210
  8. Qiming, T., Chuan, M., Guanding, Y., and Aiping, H., 2010. Analysis of cognitive radio spectrum access with finite primary users and infinite secondary users. International Conference on Wireless Communications & Signal Processing (WCSP), China, pp. 1-5. https://doi.org/10.1109/WCSP.2010.5633740
  9. Qing, H., Shaoyi, X., and Xiaojun J., 2011. Performance evaluation of secondary users in Dynamic Spectrum Access system, 2011. IEEE 22nd International Symposium on Personal, Indoor and Mobile Radio Communications, Toronto, Canada, pp. 710-714. https://www.doi.org/10.1109/PIMRC.2011.6140057
  10. Salameh, O., Bruneel, H., and Wittevrongel, S., 2020. Performance evaluation of cognitive radio networks with imperfect spectrum sensing and bursty primary user traffic. Mathematical Problems in Engineering, 64, 1-11. https://doi.org/10.1155/2020/4102046
  11. Salameh, O., Turck, K. D., and Bruneel, H., 2017. Analysis of secondary user performance in cognitive radio networks with reactive spectrum handoff. Telecommunication Systems, 65, 539-550. https://doi.org/10.1007/s11235-016-0250-7
  12. Shruti and Kulshrestha, R., 2022. Analysis of Spectrum Sensing and Spectrum Access in Cognitive Radio Networks with Heterogeneous Traffic and p-Retry Buffering, in IEEE Transactions on Mobile Computing. 21(7), pp. 2318-2331. https://www.doi.org/10.1109/TMC.2020.3042836
  13. Tang, W., Yu, H., Han, Y., and Li, S., 2012. An analytical performance model considering access strategy of opportunistic spectrum sharing system. Concurrency and Computation: Practice and Experience, 24(11), 1200-1212. https://doi.org/10.1002/cpe.1890
  14. Telex, M. N. N., Shou. Dong and Atta S. Attahiru, Analysis of cognitive radio networks with channel assembling, buffering, and imperfect sensing, 2013. IEEE Wireless Communications and Networking Conference (WCNC), Shanghai, China, pp. 952-957. https://www.doi.org/10.1109/WCNC.2013.6554692
  15. Toukhey, A.T.E, Mohsen M. T., and Ibrahim F. T, 2016. QoS-Driven Channel Allocation Schemes Based on Secondary Users' Priority in Cognitive Radio Networks. International Journal of Wireless and Mobile Computing 11(2), 91-99.
  16. Wang, Z., and Zhang, Y., Analysis of cognitive radio spectrum access with heterogeneous traffic under buffer constraints, 2013. 3rd International Conference on Consumer Electronics, Communications and Networks, Xianning, China, pp. 137-140. https://www.doi.org/10.1109/CECNet.2013.6703291
  17. Zhao, Q., and Sadler, B. M., 2007. A survey of dynamic spectrum access. IEEE Signal Processing Magazine, 24(3), 79-89. https://doi.org/10.1109/MSP.2007.361604