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Abstrakty
We consider the downlink of an orthogonal frequency division multiplexing (OFDM) based cell that accommodates calls from different service-classes with different resource requirements. We assume that calls arrive in the cell according to a quasi-random process, i.e., calls are generated by a finite number of sources. To calculate the most important performance metrics in this OFDM-based cell, i.e., congestion probabilities and resource utilization, we model it as a multirate loss model, show that the steady-state probabilities have a product form solution (PFS) and propose recursive formulas which reduce the complexity of the calculations. In addition, we study the bandwidth reservation (BR) policy which can be used in order to reserve subcarriers in favor of calls with high subcarrier requirements. The existence of the BR policy destroys the PFS of the steady-state probabilities. However, it is shown that there are recursive formulas for the determination of the various performance measures. The accuracy of the proposed formulas is verified via simulation and found to be satisfactory.
Słowa kluczowe
Rocznik
Tom
Strony
215--223
Opis fizyczny
Bibliogr. 42 poz., rys.
Twórcy
autor
- Dept. Informatics & Telecommunications, University of Peloponnese, 221 31 Tripolis, Greece
autor
- Dept. Informatics & Telecommunications, University of Peloponnese, 221 31 Tripolis, Greece
autor
- Dept. Informatics & Telecommunications Engineering,University of Western Macedonia, 501 00 Kozani, Greece
autor
- Institute of Mechanics and Applied Computer Science, Kazimierz Wielki University, 85-064 Bydgoszcz, Poland
autor
- Dept. Electrical & Computer Engineering,University of Patras, 265 04 Patras, Greece
Bibliografia
- [1] I. Moscholios and M. Logothetis, Efficient Multirate Teletraffic Loss Models Beyond Erlang, Wiley & IEEE Press, 2019.
- [2] B. Filipowicz and J. Kwiecien, “Queueing systems and networks. Models and Applications”, Bull. Pol. Ac.: Tech. 56 (4), 379–390 (2008).
- [3] W. Oniszczuk, “Loss tandem networks with blocking – a semi-Markov approach”, Bull. Pol. Ac.: Tech. 58 (4), 673–681 (2010).
- [4] S. Shioda, “Fundamental trade-offs between resource separation and resource share for quality of service guarantees”, IET Networks 3 (1), 4–15, (2014).
- [5] G. Bouloukakis, I. Moscholios, N. Georgantas, and V. Issarny, “Performance Modeling of the Middleware Overlay Infrastructure of Mobile Things”, Proc. IEEE ICC, Paris, France, May 2017.
- [6] O. Tikhonenko and M. Ziolkowski, “Single-server queueing system with external and internal customers”, Bull. Pol. Ac.: Tech. 66 (4), 539–551 (2018).
- [7] O. Tikhonenko, M. Ziolkowski, and M. Kurkowski, “M/G/n/(0,V) Erlang queueing system with non-homogeneous customers, non-identical servers and limited memory space”, Bull. Pol. Ac.: Tech. 67 (3), 489–500 (2019).
- [8] A. Abbasi, H. Jin, and S. Wu, “A software-defined cloud resource management framework”, Proc. Asia-Pacific Services Computing Conf., Bangkok, Thailand, Dec. 2015.
- [9] A. Abbasi and H. Jin, “v-Mapper: an application-aware resource consolidation scheme for cloud data centers”, Future Internet 10, 90 (2018). doi: 10.3390/fi10090090.
- [10] I. Widjaja and H. La Roche, “Sizing X2 bandwidth for interconnected eNBs”, Proc. IEEE VTC Fall, Alaska, USA, Sept. 2009.
- [11] M. Glabowski and M. Stasiak, “Internal blocking probability calculation in switching networks with additional inter-stage links and mixture of Erlang and Engset traffic”, Image Proc. & Commun. 17 (1-2), 67–80 (2012).
- [12] S. Atmaca, A. Karahan, C. Ceken, and I. Erturk, “A new MAC protocol for broadband wireless communications and its performance evaluation”, Telecommun. Syst. 57 (1), 13–23 (2014).
- [13] J. Vardakas, N. Zorba, and C. Verikoukis, “Power demand control scenarios for smart grid applications with finite number of appliances”, Applied Energy 162, 83–98 (2016).
- [14] I. Moscholios, V. Vassilakis, M. Logothetis, and A. Boucouvalas, “State-dependent bandwidth sharing policies for wireless multirate loss networks”, IEEE Trans. Wireless Commun. 16 (8), 5481–5497 (2017).
- [15] P. Efstratiou and I. Moscholios, “A Multirate Loss Model for a 5G Cellular Network with Mobile Users and Quasi-random Traffic”, Proc. RTUWO, Riga, Latvia, Nov. 2018.
- [16] C. Paik and Y. Suh, “Generalized queueing model for call blocking probability and resource utilization in OFDM wireless networks”, IEEE Commun. Letters 15 (7), 767–769 (2011).
- [17] V. Pla, J. Martinez-Bauset, and V. Casares-Giner, “Comments on call blocking probability and bandwidth utilization of OFDM subcarrier allocation in next-generation wireless networks”, IEEE Commun. Letters 12 (5), 349 (2008).
- [18] J. Chen and W. Chen, “Call blocking probability and bandwidth utilization of OFDM subcarrier allocation in next-generation wireless networks”, IEEE Commun. Letters 10 (2), 82–84 (2006).
- [19] I. Moscholios, V. Vassilakis, P. Panagoulias, and M. Logothetis, “On call blocking probabilities and resource utilization in OFDM wireless networks”, Proc. CSNDSP, Budapest, Hungary, July 2018.
- [20] M. Glabowski, A. Kaliszan, and M. Stasiak, ‘ ‘Asymmetric convolution algorithm for blocking probability calculation in full-availability group with bandwidth reservation”, IET Circuits, Devices & Systems 2 (1), 87–94 (2008).
- [21] Q. Huang, K. Ko, and V. Iversen, “A new convolution algorithm for loss probability analysis in multiservice networks”, Perf. Eval. 68 (1), 76–87 (2011).
- [22] L. Brewka, V. Iversen, and G. Kardaras, “Integrated service resource reservation using queueing networks theory”, IET Netw. 3 (1), 16–21 (2014).
- [23] S. Sagkriotis, S. Pantelis, I. Moscholios, and V. Vassilakis, “Call blocking probabilities in a two-link multi rate loss system for Poisson traffic”, IET Netw. 7 (4), 233–241 (2018).
- [24] I. Moscholios, V. Vassilakis, N. Sagias, and M. Logothetis, “On channel sharing policies in LEO mobile satellite systems”, IEEE Trans. Aerospace and Electronic Syst. 54 (4), 1628–1640 (2018).
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- [35] V. Casares-Giner, “Some teletraffic issues in optical burst switching with burst segmentation”, Electr. Letters 52 (11), 941–943 (2016).
- [36] I. Moscholios, V. Vassilakis, M. Logothetis, and A. Boucouvalas, “A probabilistic threshold-based bandwidth sharing policy for wireless multirate loss networks”, IEEE Wireless Commun. Letters 5 (3), 304–307 (2016).
- [37] M. Glabowski, A. Kaliszan, and M. Stasiak, “Modelling over-flow systems with distributed secondary resources”, Comp. Netw. 108, 171–183 (2016).
- [38] M. Glabowski and M. Sobieraj, “Analytical modelling of multiservice switching networks with multiservice sources and resource management mechanisms”, Telecommun. Syst. 66 (3), 559–578 (2017).
- [39] S. Hanczewski, M. Stasiak, and J. Weissenberg, “Non-full-available queueing model of an EON node”, Optical Switch. Netw. 33, 131–142 (2019).
- [40] P. Panagoulias and I. Moscholios, “Congestion probabilities in the X2 link of LTE Networks”, Telecommun. Syst. 71 (4), 585–599 (2019).
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Uwagi
PL
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-36e67f21-a5cb-4c38-81f7-c21250e54fc4