Abstract
Cognitive radio (CR) is currently one of the most promising information transmission technologies to deal with the problem of spectrum scarcity and spectrum underutilization in wireless communications. CR networks aim to enhance spectrum efficiency to meet the ever-increasing demands of end users. The principle is to provide the opportunity for unlicensed users (secondary users, SUs) to temporarily and dynamically access the unused or sparsely used bandwidth while ensuring that it never interferes or degrades the performance of the incumbent license holders, commonly called primary users (PUs). This raises several challenges to be addressed in CR networks and performance of secondary users is one of the critical issues tackled in this paper. That is, we propose to devise CR networks as a retrial queueing system where PUs have preemptive priority over SUs. To calculate performance measures of the devised model under quite general assumptions about the model parameters, analytical methods are known to require hard calculations and the obtained results are generally not exploitable. For this reason, simulation modeling becomes the last resort to assess the dependability indicators. To this extend, we build the simulation model of the queueing system using Timed Stochastic Colored Petri Nets. Various useful results will be hence drawn while varying network conditions. Both exponential and Erlang distributions are considered for modeling service time of SUs. The obtained results with restrictive assumptions fit the analytical outcomes experienced for quite similar queuing models, which demonstrate the effectiveness of the proposed STCPN simulation model.
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References
Atmaca, T., Begin, T., Brandwajn, A., & Castel-Taleb, H. (2016). Performance evaluation of cloud computing centers with general arrivals and service. IEEE Transactions on Parallel and Distributed Systems, 27(8), 2341–2348.
Balsamo, S., & Marin, A. (2007). On representing multiclass M/M/K queues by generalized stochastic Petri nets. In Proceedings of ECMS/ASMTA-2007 conference (pp. 121–128). Citeseer.
Boukredera, D., Aknine, S., & Maamri, R. (2012). Modeling temporal aspects of contract net protocol using timed colored Petri nets. In STAIRS (pp. 83–94). IOS Press.
Boukredera, D., Maamri, R., & Aknine, S. (2013). Modeling and analysis of reliable contract net protocol using timed colored Petri nets. In Proceedings of the 2013 IEEE/WIC/ACM international joint conferences on web intelligence (WI) and intelligent agent technologies (IAT)-Volume 02 (pp. 17–24). IEEE Computer Society.
Boukredera, D., Maamri, R., & Aknine, S. (2016). Stochastic Petri net-based modeling and formal analysis of fault tolerant contract net protocol. In Web intelligence (vol. 14, pp. 245–271). IOS Press.
Chuku, E. E., & Kouvatsos, D. D. (2018). Impact of scalability on the performance of secured cognitive radio networks. Electronic Notes in Theoretical Computer Science, 340, 123–135.
CPN Tools. CPN tools homepage. Retrieved January, 2019 from http://www.cpntools.org.
Dudin, A. N., Lee, M. H., Dudina, O., & Lee, S. K. (2017). Analysis of priority retrial queue with many types of customers and servers reservation as a model of cognitive radio system. IEEE Transactions on Communications, 65(1), 186–199.
Ejike, C., & Kouvatsos, D. (2017). Combined sensing, performance and security trade-offs in cognitive radio networks. In 2017 IEEE 16th international symposium on Network computing and applications (NCA) (pp. 1–4). IEEE.
Gao, S. (2015). A preemptive priority retrial queue with two classes of customers and general retrial times. Operational Research, 15(2), 233–251.
Gharbi, N., Dutheillet, C., & Ioualalen, M. (2009). Colored stochastic Petri nets for modelling and analysis of multiclass retrial systems. Mathematical and Computer Modelling, 49(7–8), 1436–1448.
Gharbi, N., Nemmouchi, B., Mokdad, L., & Ben-Othman, J. (2014). The impact of breakdowns disciplines and repeated attempts on performances of small cell networks. Journal of Computational Science, 5(4), 633–644.
Harper, R. (2001). Programming in standard ml. Working draft, Carnegie Mellon University Spring Semester, June 2001.
Ikhlef, L., Lekadir, O., & Aıssani, D. (2014). Performance analysis of M/G/1 retrial queue with finite source population using markov regenerative stochastic Petri nets. In Proceedings of PNSE 2014 (international workshop on Petri nets and software engineering), co-located with 35 th international conference on application and theory of Petri nets and concurrency Tunis, Tunisia. Citeseer.
Jensen, K., & Kristensen, L. M. (2009). Coloured Petri nets: Modelling and validation of concurrent systems. Berlin: Springer.
Jensen, K., Kristensen, L. M., & Wells, L. (2007). Coloured Petri nets and cpn tools for modelling and validation of concurrent systems. International Journal on Software Tools for Technology Transfer, 9(3–4), 213–254.
Kaur, P., Khosla, A., & Uddin, M. (2011). Markovian queuing model for dynamic spectrum allocation in centralized architecture for cognitive radios. IACSIT International Journal of Engineering and Technology, 3(1), 96–101.
Lakaour, L., Aïssani, D., Adel-Aissanou, K., & Barkaoui, K. (2018). M/M/1 retrial queue with collisions and transmission errors. Methodology and Computing in Applied Probability, 21, 1–12.
Li, J., Cui, Y., & Ma, Y. (2015). Modeling message queueing services with reliability guarantee in cloud computing environment using colored Petri nets. Mathematical problems in Engineering, 2015, 20.
Mitola, J. (2000). Cognitive radio: An integrated agent architecture for software defined radio. Trita-IT.: AVH, p. 304.
Palunčić, F., Alfa, A. S., Maharaj, B. T., & Tsimba, H. M. (2018). Queueing models for cognitive radio networks: A survey. IEEE Access, 6, 50801–50823.
Paul, S., & Phung-Duc, T. (2018). Retrial queueing model with two-way communication, unreliable server and resume of interrupted call for cognitive radio networks. In Information technologies and mathematical modelling. Queueing theory and applications, (pp. 213–224). Springer.
Pinna, B., Babykina, G., Brinzei, N., & Pétin, J-F. (2013). Deterministic and stochastic dependability analysis of industrial systems using coloured Petri nets approach. In Annual conference of the European safety and reliability association, ESREL 2013 (pp. 2969–2977). Taylor & Francis Group, ISBN 978-1-138-00123-7.
Rattaro, C., & Belzarena, P. (2018). Cognitive radio networks: Analysis of a paid-sharing approach based on admission control decisions. Wireless Personal Communications, 101(4), 2053–2083.
Rogge-Solti, A., & Weske, M. (2013). Prediction of remaining service execution time using stochastic Petri nets with arbitrary firing delays. In International conference on service-oriented computing (pp. 389–403). Springer.
Saleem, Y., & Rehmani, M. H. (2014). Primary radio user activity models for cognitive radio networks: A survey. Journal of Network and Computer Applications, 43, 1–16.
Steenkiste, P., Sicker, D., Minden, G., & Raychaudhuri, D. (2009). Future directions in cognitive radio network research. In NSF workshop report (Vol. 4, pp. 1–2).
Suliman, I., & Lehtomaki, J. (2009). Queueing analysis of opportunistic access in cognitive radios. In 2009 second international workshop on cognitive radio and advanced spectrum management (pp. 153–157). IEEE.
Sun, B., Lee, M. H., Dudin, S. A., & Dudin, A. N. (2014). Analysis of multiserver queueing system with opportunistic occupation and reservation of servers. Mathematical Problems in Engineering, 2014, 13.
Tragos, E. Z., Zeadally, S., Fragkiadakis, A. G., & Siris, V. A. (2013). Spectrum assignment in cognitive radio networks: A comprehensive survey. IEEE Communications Surveys & Tutorials, 15(3), 1108–1135.
Van der Aalst, W. M. P., Stahl, C., & Westergaard, M. (2013). Strategies for modeling complex processes using colored Petri nets. In Transactions on Petri nets and other models of concurrency vii, (pp. 6–55). Springer.
Van der Aalst, W. M. P., Van Hee, K. M., & Reijers, H. A. (2000). Analysis of discrete-time stochastic Petri nets. Statistica Neerlandica, 54(2), 237–255.
Vinayak, R., Dharmaraja, S., & Arunachalam, V. (2014). On the study of simultaneous service by random number of servers with retrial and preemptive priority. International Journal of Operational Research, 20(1), 68–90.
Wang, B., & Ray Liu, K. J. (2011). Advances in cognitive radio networks: A survey. IEEE Journal of Selected Topics in Signal Processing, 5(1), 5–23.
Wang, F., Wang, J., & Li, W. W. (2016). Game-theoretic analysis of opportunistic spectrum sharing with imperfect sensing. EURASIP Journal on Wireless Communications and Networking, 2016(1), 141.
Zhang, J., Yang, J., Zhang, Y., & Zhang, S. (2017). A dynamic spectrum allocation algorithm for a maritime cognitive radio communication system based on a queuing model. Information, 8(4), 119.
Zhao, Y., & Yue, W. (2018). Performance analysis and optimization of cognitive radio networks with retransmission control. Optimization Letters, 12(6), 1281–1300.
Zirem, D., Boualem, M., Adel-Aissanou, K., & Aïssani, D. (2018). Analysis of a single server batch arrival unreliable queue with balking and general retrial time. Quality Technology & Quantitative Management, 16, 1–24.
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Boukredera, D., Adel-Aissanou, K. Modeling and Performance Analysis of Cognitive Radio Networks Using Stochastic Timed Colored Petri Nets. Wireless Pers Commun 112, 1659–1687 (2020). https://doi.org/10.1007/s11277-020-07121-8
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DOI: https://doi.org/10.1007/s11277-020-07121-8