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April  2016, 12(2): 687-702. doi: 10.3934/jimo.2016.12.687

Analysis and optimization of a gated polling based spectrum allocation mechanism in cognitive radio networks

Shunfu Jin 1, , Wuyi Yue 2, and  Zsolt Saffer 3,

1. 

School of Information Science and Engineering, Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province, Yanshan University, Qinhuangdao 066004
2. 

Department of Intelligence and Informatics, Konan University, Kobe 658-8501
3. 

Department of Telecommunications, Budapest University of Technology and Economics, Budapest

Received  October 2014 Revised  March 2015 Published  June 2015

In Cognitive Radio Networks the licensed users and the cognitive users are called Primary Users and Secondary Users, respectively. The Primary Users enjoy preemptive priority during the spectrum usage, while the Secondary Users are allowed to access the unused parts of the spectrum opportunistically. In this paper we focus on the problem of improving the fairness of spectrum usage for real-time applications. We propose a novel centralized spectrum allocation mechanism with a gated polling strategy, which we model by a gated polling system with a non-zero switchover times. The approximate analysis of this polling model is performed. We derive formulas for estimating the system measures in terms of throughput of the system, average latency and delay jitter of the Secondary Users packets as well as the spectrum switching ratio and the spectrum utility. Numerical results based on the analysis and the simulation are provided to validate the analytical results and to investigate the impact of different parameters on the system performance. Finally we discuss the optimal system design by the help of building an appropriate cost function.
Keywords: non-zero switchover procedure, service interruption., spectrum allocation, gated polling strategy, CRNs.
Mathematics Subject Classification: Primary: 68M10, 68M20; Secondary: 60J1.
Citation: Shunfu Jin, Wuyi Yue, Zsolt Saffer. Analysis and optimization of a gated polling based spectrum allocation mechanism in cognitive radio networks. Journal of Industrial & Management Optimization, 2016, 12 (2) : 687-702. doi: 10.3934/jimo.2016.12.687
References:
[1]

A. S. Alfa, Queueing Theory for Telecommunications-Discrete Time Modelling of a Single Node System, Springer, New York, 2010. doi: 10.1007/978-1-4419-7314-6.  Google Scholar

[2]

C. Ding, K. Wang and S. Lai, Channel coordination mechanism with retailers having fairness preference an improved quantity discount mechanism, Journal of Industrial and Management Optimization, 9 (2013), 967-982. doi: 10.3934/jimo.2013.9.967.  Google Scholar

[3]

C. Do, N. Tran and C. Hong, Throughput maximization for the secondary user over multi-channel cognitive radio networks, in Proceedings of International Conference on Information Networking, (2012), 65-69. doi: 10.1109/ICOIN.2012.6164351.  Google Scholar

[4]

D. Domenico, E. Strinati and D. Benedetto, A survey on MAC strategies for cognitive radio networks, IEEE Communications Surveys and Tutorials, 14 (2012), 21-44. doi: 10.1109/SURV.2011.111510.00108.  Google Scholar

[5]

Z. Htike, J. Lee and C. Hong, A MAC protocol for cognitive radio networks with reliable control channels assignment, in Proceedings of International Conference on Information Networking, (2012), 81-85. doi: 10.1109/ICOIN.2012.6164354.  Google Scholar

[6]

S. Jin, W. Yue and Z. Saffer, Performance analysis of the gate-polling spectrum access strategy in cognitive radio networks, in Proceedings of the 9th International Conference on Queueing Theory and Network Applications, (2014), 36-41. Google Scholar

[7]

J. Juan, L. Mario, V. Javier and G. Joan, Bandwidth reservation as a coexistence strategy in opportunistic spectrum access environments, IEEE Journal on Selected Areas in Communications, 32 (2014), 478-488. Google Scholar

[8]

K. Kim, T-preemptive priority queue and its application to the analysis of an opportunistic spectrum access in cognitive radio networks, Computers and Operations Research, 39 (2012), 1394-1401. doi: 10.1016/j.cor.2011.08.008.  Google Scholar

[9]

Y. Li and A. Nosratinia, Hybrid opportunistic scheduling in cognitive radio networks, IEEE Transactions on Wireless Communications, 11 (2012), 328-337. doi: 10.1109/TWC.2011.110811.110722.  Google Scholar

[10]

Z. Ma, W. Yue and N. Tian, Performance and cost analysis of a Geom/G/1 (G, SV) system, Optimization and Engineering, 10 (2009), 239-251. doi: 10.1007/s11081-008-9074-y.  Google Scholar

[11]

Y. Mihov, Cross-layer analysis and performance evaluation of cognitive radio networks, in Proceedings of the 6th International Conference on Systems and Networks Communications, (2011), 99-104. Google Scholar

[12]

T. Nguyen, A. Pham and V. Nguyen, Medium access control design for cognitive radio networks: A survey, IEICE Transactions on Communications, 97 (2014), 359-374. Google Scholar

[13]

H. Takagi, Analysis of Polling Systems, MIT Press, Boston, 1986. doi: 10.1016/0166-5316(85)90016-1.  Google Scholar

[14]

H. Takagi, Analysis and Application of Polling Models, Lecture Notes in Computer Science: Performance Evaluation: Origins and Directions, 1769 (2000), 423-442. doi: 10.1007/3-540-46506-5_18.  Google Scholar

[15]

D. Treeumnuk and D. Popescu, Adaptive sensing for increased spectrum utilization in dynamic cognitive radio systems, in Proceedings of IEEE Radio and Wireless Symposium, (2012), 319-322. doi: 10.1109/RWS.2012.6175336.  Google Scholar

[16]

Y. Wu, B. Wang and T. C. Clancy, Anti-jamming games in multi-channel cognitive radio networks, IEEE Journal on Selected Areas in Communications, 30 (2012), 4-15. doi: 10.1109/JSAC.2012.120102.  Google Scholar

[17]

Y. Zhao, S. Jin and W. Yue, Performance evaluation of the centralized spectrum access strategy with multiple input streams in cognitive radio networks, IEICE Transactions on Communications, E97-B (2014), 334-342. doi: 10.1587/transcom.E97.B.334.  Google Scholar

show all references

References:
[1]

A. S. Alfa, Queueing Theory for Telecommunications-Discrete Time Modelling of a Single Node System, Springer, New York, 2010. doi: 10.1007/978-1-4419-7314-6.  Google Scholar

[2]

C. Ding, K. Wang and S. Lai, Channel coordination mechanism with retailers having fairness preference an improved quantity discount mechanism, Journal of Industrial and Management Optimization, 9 (2013), 967-982. doi: 10.3934/jimo.2013.9.967.  Google Scholar

[3]

C. Do, N. Tran and C. Hong, Throughput maximization for the secondary user over multi-channel cognitive radio networks, in Proceedings of International Conference on Information Networking, (2012), 65-69. doi: 10.1109/ICOIN.2012.6164351.  Google Scholar

[4]

D. Domenico, E. Strinati and D. Benedetto, A survey on MAC strategies for cognitive radio networks, IEEE Communications Surveys and Tutorials, 14 (2012), 21-44. doi: 10.1109/SURV.2011.111510.00108.  Google Scholar

[5]

Z. Htike, J. Lee and C. Hong, A MAC protocol for cognitive radio networks with reliable control channels assignment, in Proceedings of International Conference on Information Networking, (2012), 81-85. doi: 10.1109/ICOIN.2012.6164354.  Google Scholar

[6]

S. Jin, W. Yue and Z. Saffer, Performance analysis of the gate-polling spectrum access strategy in cognitive radio networks, in Proceedings of the 9th International Conference on Queueing Theory and Network Applications, (2014), 36-41. Google Scholar

[7]

J. Juan, L. Mario, V. Javier and G. Joan, Bandwidth reservation as a coexistence strategy in opportunistic spectrum access environments, IEEE Journal on Selected Areas in Communications, 32 (2014), 478-488. Google Scholar

[8]

K. Kim, T-preemptive priority queue and its application to the analysis of an opportunistic spectrum access in cognitive radio networks, Computers and Operations Research, 39 (2012), 1394-1401. doi: 10.1016/j.cor.2011.08.008.  Google Scholar

[9]

Y. Li and A. Nosratinia, Hybrid opportunistic scheduling in cognitive radio networks, IEEE Transactions on Wireless Communications, 11 (2012), 328-337. doi: 10.1109/TWC.2011.110811.110722.  Google Scholar

[10]

Z. Ma, W. Yue and N. Tian, Performance and cost analysis of a Geom/G/1 (G, SV) system, Optimization and Engineering, 10 (2009), 239-251. doi: 10.1007/s11081-008-9074-y.  Google Scholar

[11]

Y. Mihov, Cross-layer analysis and performance evaluation of cognitive radio networks, in Proceedings of the 6th International Conference on Systems and Networks Communications, (2011), 99-104. Google Scholar

[12]

T. Nguyen, A. Pham and V. Nguyen, Medium access control design for cognitive radio networks: A survey, IEICE Transactions on Communications, 97 (2014), 359-374. Google Scholar

[13]

H. Takagi, Analysis of Polling Systems, MIT Press, Boston, 1986. doi: 10.1016/0166-5316(85)90016-1.  Google Scholar

[14]

H. Takagi, Analysis and Application of Polling Models, Lecture Notes in Computer Science: Performance Evaluation: Origins and Directions, 1769 (2000), 423-442. doi: 10.1007/3-540-46506-5_18.  Google Scholar

[15]

D. Treeumnuk and D. Popescu, Adaptive sensing for increased spectrum utilization in dynamic cognitive radio systems, in Proceedings of IEEE Radio and Wireless Symposium, (2012), 319-322. doi: 10.1109/RWS.2012.6175336.  Google Scholar

[16]

Y. Wu, B. Wang and T. C. Clancy, Anti-jamming games in multi-channel cognitive radio networks, IEEE Journal on Selected Areas in Communications, 30 (2012), 4-15. doi: 10.1109/JSAC.2012.120102.  Google Scholar

[17]

Y. Zhao, S. Jin and W. Yue, Performance evaluation of the centralized spectrum access strategy with multiple input streams in cognitive radio networks, IEICE Transactions on Communications, E97-B (2014), 334-342. doi: 10.1587/transcom.E97.B.334.  Google Scholar

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