QoS-driven pricing policy for cognitive radio networks

Year 2017, Volume: 21 Issue: 4, 637 - 642, 01.08.2017

Tuğrul Çavdar , Zhaleh Sadreddini

https://doi.org/10.16984/saufenbilder.276721

Abstract

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.

Keywords

Cognitive Radio Networks, Spectrum Management, Pricing Policy

Bilişsel radyo ağlar için servis kalitesini esas alan fiyat politikası

Abstract

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.

Keywords

Bilişsel Radyo Ağlar, Spektrum Yönetimi, Ücretlendirme Politikası

References

• [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.