Araştırma Makalesi
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QoS-driven pricing policy for cognitive radio networks

Yıl 2017, , 637 - 642, 01.08.2017
https://doi.org/10.16984/saufenbilder.276721

Öz

Recently, a large amount of
spectrum and bandwidth are demanded by mobile network operators (MNOs) in order
to obtain the high data rates quality of service (QoS). For optimal spectrum
utilization for better efficiency, MNO should handle unused spectrums through a
convenient spectrum management. Significantly, MNOs should trade-off among the
proposed QoS, service pricing and secondary users’ (SUs) satisfaction. In this
study, adaptive spectrum management based on the requesting SUs’ (RSUs) QoS
requirement is proposed in cognitive radio network (CRN). QoS-driven pricing
policy is developed so that MNO charges RSUs fairly while improving spectrum
utilization and network revenue (NR) efficiency in the long term. Simulation
results illustrate the RSUs charging strategy based on dynamic switch system in
off-peak and peak hours.

Kaynakça

  • [1] G. I. Tsiropoulos, O. A. Dobre, and M. H. Ahmed, “Radio resource allocation techniques for efficient spectrum access in cognitive radio networks,” IEEE Communications Surveys & Tutorials, vol. 18, no. 1, pp. 824-847, firstquarter 2016.
  • [2] F. Akyildiz, W. Lee, M. C. Vuran, S. Mohanty, “Next generation/dynamic spectrum access/cognitive radio wireless networks: a survey,” Computer Networks, vol. 50, pp. 2127–2159, 2006.
  • [3] E. Z. Tragos, S. Zeadally, A. G. Fragkiadakis, and V. A. Siris, “Spectrum assignment in cognitive radio networks: a comprehensive survey,” IEEE Communications Surveys & Tutorials, vol.15, no.3, pp. 1108-1135, 2013.
  • [4] Y.-X. Yang, L. T. Park, N. B. Mandayam, I. Seskar, A. L. Glass, and N. Sinha, “Prospect pricing in cognitive radio networks,” IEEE Transactions on Cognitive Communications and Networking, vol. 1, no. 1, March 2015.
  • [5] I. Alqerm and B. Shihada, “Adaptive decision-making scheme for cognitive radio networks,” in IEEE 28th International Conference on Advanced Information Networking and Applications (AINA 2014), Victoria, Canada, May 13-16, 2014.
  • [6] W. Ibrahim, J. W. Chinneck, and S. Periyalwar. “QoS satisfaction based charging and resource management policy for next generation wireless networks,” in International Conference on Wireless Communications, Networking And Mobile Computing (WCNM’05), Wuhan, China, June 13-16 2005, pp. 868-873.
  • [7] Y. Wu, and W.-Z. Song, “Cooperative resource sharing and pricing for proactive dynamic spectrum access via Nash bargaining solution,” IEEE Transactions on Parallel and Distributed Systems, vol. 25, no.11, pp. 2804-2817, November 2014.
  • [8] H.-X. Nguyen, and B. Northcote, “User spectral efficiency: combining spectral efficiency with user experience,” in IEEE International Conference on Communication (ICC 2016), May 22-27 2016.
  • [9] T. Martin and K.-C. Chang, “Assessing user decision behaviors for dynamic spectrum sharing and pricing models,” in 19th International Conference on Information Fusion (FUSION), Heidelberg, Germany, July 5-8 2016, pp. 1011–1018.
  • [10] T. Çavdar, E. Güler, and Z. Sadreddini, “Instant overbooking framework for cognitive radio networks,” Computer Networks, vol. 76, pp. 227– 241, 15 January 2015.
  • [11] Z. Sadreddini, T. Çavdar, and E. Güler, “Performance analysis of the dynamic switch system based on user activity in cognitive radio network,” in 39th International Conference on Telecommunications and Signal Processing (TSP 2016), Vienna, Austria, June 27-29 2016, pp. 145-148.
  • [12] R. L. Philips, “Pricing And Revenue Optimization,” Stanford University Press, 2005.
  • [13] K. T. Talluri, K. T. Ryzin, “The Theory and Practice of Revenue Management,” Springer Science + Business Media, Inc., 2004.
  • [14] A. Sulistio, K.-H. Kim, and R. Buyya, “Managing cancellations and no-shows of reservations with overbooking to increase resource revenue,” in IEEE 8th International Symposium on Cluster Computing and the Grid (CCGRID’08), Lyon, France, May 19-22 2008.

Bilişsel radyo ağlar için servis kalitesini esas alan fiyat politikası

Yıl 2017, , 637 - 642, 01.08.2017
https://doi.org/10.16984/saufenbilder.276721

Öz

Son zamanlarda, gezgin ağ
operatörleri (MNO), yüksek veri hızı ve servis kalitesi (QoS) sağlamak için
yüksek miktarda spektrum ve bantgenişliğine ihtiyaç duymaktadırlar. Spektrumun
daha etkin ve optimum kullanımı için MNO, uygun bir spektrum yönetimi üzerinden
kullanılmayan bantları sevk ve idare eder. MNO, önerilen servis kalitesi,
servis fiyatı ve ikincil kullanıcıların memnuniyeti arasında bir denge kurması
çok önemlidir. Bu çalışmada, bilişsel radyo ağlar için, spektrum isteğinde
bulunan ikincil kullanıcıların (RSU) servis kalitesine dayalı olan uyarlanır
bir servis yönetimi önerilmektedir. MNO, uzun vadede kendi ağ gelirini ve
spektrum kullanımını iyileştirirken RSUlar arasında da spektrum kullanımına
bağlı olarak adil bir ücretlendirme yapmasını sağlayan QoS-esas alan bir fiyat
politikası geliştirilmiştir. Yoğun ve yoğun olmayan saatlerde dinamik
anahtarlama sistemine dayalı RSU ücretlendirme stratejilerinin benzetim sonuçları
verilmiştir.

Kaynakça

  • [1] G. I. Tsiropoulos, O. A. Dobre, and M. H. Ahmed, “Radio resource allocation techniques for efficient spectrum access in cognitive radio networks,” IEEE Communications Surveys & Tutorials, vol. 18, no. 1, pp. 824-847, firstquarter 2016.
  • [2] F. Akyildiz, W. Lee, M. C. Vuran, S. Mohanty, “Next generation/dynamic spectrum access/cognitive radio wireless networks: a survey,” Computer Networks, vol. 50, pp. 2127–2159, 2006.
  • [3] E. Z. Tragos, S. Zeadally, A. G. Fragkiadakis, and V. A. Siris, “Spectrum assignment in cognitive radio networks: a comprehensive survey,” IEEE Communications Surveys & Tutorials, vol.15, no.3, pp. 1108-1135, 2013.
  • [4] Y.-X. Yang, L. T. Park, N. B. Mandayam, I. Seskar, A. L. Glass, and N. Sinha, “Prospect pricing in cognitive radio networks,” IEEE Transactions on Cognitive Communications and Networking, vol. 1, no. 1, March 2015.
  • [5] I. Alqerm and B. Shihada, “Adaptive decision-making scheme for cognitive radio networks,” in IEEE 28th International Conference on Advanced Information Networking and Applications (AINA 2014), Victoria, Canada, May 13-16, 2014.
  • [6] W. Ibrahim, J. W. Chinneck, and S. Periyalwar. “QoS satisfaction based charging and resource management policy for next generation wireless networks,” in International Conference on Wireless Communications, Networking And Mobile Computing (WCNM’05), Wuhan, China, June 13-16 2005, pp. 868-873.
  • [7] Y. Wu, and W.-Z. Song, “Cooperative resource sharing and pricing for proactive dynamic spectrum access via Nash bargaining solution,” IEEE Transactions on Parallel and Distributed Systems, vol. 25, no.11, pp. 2804-2817, November 2014.
  • [8] H.-X. Nguyen, and B. Northcote, “User spectral efficiency: combining spectral efficiency with user experience,” in IEEE International Conference on Communication (ICC 2016), May 22-27 2016.
  • [9] T. Martin and K.-C. Chang, “Assessing user decision behaviors for dynamic spectrum sharing and pricing models,” in 19th International Conference on Information Fusion (FUSION), Heidelberg, Germany, July 5-8 2016, pp. 1011–1018.
  • [10] T. Çavdar, E. Güler, and Z. Sadreddini, “Instant overbooking framework for cognitive radio networks,” Computer Networks, vol. 76, pp. 227– 241, 15 January 2015.
  • [11] Z. Sadreddini, T. Çavdar, and E. Güler, “Performance analysis of the dynamic switch system based on user activity in cognitive radio network,” in 39th International Conference on Telecommunications and Signal Processing (TSP 2016), Vienna, Austria, June 27-29 2016, pp. 145-148.
  • [12] R. L. Philips, “Pricing And Revenue Optimization,” Stanford University Press, 2005.
  • [13] K. T. Talluri, K. T. Ryzin, “The Theory and Practice of Revenue Management,” Springer Science + Business Media, Inc., 2004.
  • [14] A. Sulistio, K.-H. Kim, and R. Buyya, “Managing cancellations and no-shows of reservations with overbooking to increase resource revenue,” in IEEE 8th International Symposium on Cluster Computing and the Grid (CCGRID’08), Lyon, France, May 19-22 2008.
Toplam 14 adet kaynakça vardır.

Ayrıntılar

Konular Bilgisayar Yazılımı
Bölüm Araştırma Makalesi
Yazarlar

Tuğrul Çavdar

Zhaleh Sadreddini Bu kişi benim

Yayımlanma Tarihi 1 Ağustos 2017
Gönderilme Tarihi 12 Aralık 2016
Kabul Tarihi 26 Nisan 2017
Yayımlandığı Sayı Yıl 2017

Kaynak Göster

APA Çavdar, T., & Sadreddini, Z. (2017). QoS-driven pricing policy for cognitive radio networks. Sakarya University Journal of Science, 21(4), 637-642. https://doi.org/10.16984/saufenbilder.276721
AMA Çavdar T, Sadreddini Z. QoS-driven pricing policy for cognitive radio networks. SAUJS. Ağustos 2017;21(4):637-642. doi:10.16984/saufenbilder.276721
Chicago Çavdar, Tuğrul, ve Zhaleh Sadreddini. “QoS-Driven Pricing Policy for Cognitive Radio Networks”. Sakarya University Journal of Science 21, sy. 4 (Ağustos 2017): 637-42. https://doi.org/10.16984/saufenbilder.276721.
EndNote Çavdar T, Sadreddini Z (01 Ağustos 2017) QoS-driven pricing policy for cognitive radio networks. Sakarya University Journal of Science 21 4 637–642.
IEEE T. Çavdar ve Z. Sadreddini, “QoS-driven pricing policy for cognitive radio networks”, SAUJS, c. 21, sy. 4, ss. 637–642, 2017, doi: 10.16984/saufenbilder.276721.
ISNAD Çavdar, Tuğrul - Sadreddini, Zhaleh. “QoS-Driven Pricing Policy for Cognitive Radio Networks”. Sakarya University Journal of Science 21/4 (Ağustos 2017), 637-642. https://doi.org/10.16984/saufenbilder.276721.
JAMA Çavdar T, Sadreddini Z. QoS-driven pricing policy for cognitive radio networks. SAUJS. 2017;21:637–642.
MLA Çavdar, Tuğrul ve Zhaleh Sadreddini. “QoS-Driven Pricing Policy for Cognitive Radio Networks”. Sakarya University Journal of Science, c. 21, sy. 4, 2017, ss. 637-42, doi:10.16984/saufenbilder.276721.
Vancouver Çavdar T, Sadreddini Z. QoS-driven pricing policy for cognitive radio networks. SAUJS. 2017;21(4):637-42.