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doi: 10.3934/jimo.2020144
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Performance analysis and optimization research of multi-channel cognitive radio networks with a dynamic channel vacation scheme

Zhanyou Ma 1,, , Wenbo Wang 1,2, , Wuyi Yue 3, and  Yutaka Takahashi 4,

1. 

School of Science, Yanshan University, Qinhuangdao 066004, China
2. 

First Experimental Primary School of Tongzhou District, Beijing Academy of Educational Sciences, Beijing 101100, China
3. 

Department of Intelligence and Informatics, Konan University, Kobe 658-8501, Japan
4. 

The Kyoto College of Graduate Studies for Informatics, Kyoto 600-8216, Japan

*Corresponding author: Zhanyou Ma

Received  October 2018 Revised  July 2020 Early access September 2020

In order to resolve the issues of channel scarcity and low channel utilization rates in cognitive radio networks (CRNs), some researchers have proposed the idea of "secondary utilization" for licensed channels. In "secondary utilization", secondary users (SUs) opportunistically take advantage of unused licensed channels, thus guaranteeing the transmission performance and quality of service (QoS) of the system. Based on the channel vacation scheme, we analyze a preemptive priority queueing system with multiple synchronization working vacations. Under this discipline, we build a three-dimensional Markov process for this queueing model. Through the analysis of performance measures, we obtain the average queueing length for the two types of users, the mean busy period and the channel utility. By analyzing several numerical experiments, we demonstrate the effect of the parameters on the performance measures. Finally, in order to optimize the system individually and socially, we establish utility functions and provide some optimization results for PUs and SUs.

Keywords: Channel allocation scheme, three-dimensional markov process, preemptive priority, queueing model, optimization.
Mathematics Subject Classification: Primary: 60K25; Secondary: 90B22.
Citation: Zhanyou Ma, Wenbo Wang, Wuyi Yue, Yutaka Takahashi. Performance analysis and optimization research of multi-channel cognitive radio networks with a dynamic channel vacation scheme. Journal of Industrial & Management Optimization, doi: 10.3934/jimo.2020144
References:
[1]

Y. Chen, P. Liao and Y. Wang, A channel-hopping scheme for continuous rendezvous and data delivery in cognitive radio network, Peer-to-Peer Networking and Applications, 9 (2016), 16-27.  doi: 10.1007/s12083-014-0308-9.  Google Scholar

[2]

L. Chouhan and A. Trivedi, Performance study of a CSMA based multi-user MAC protocol for cognitive radio networks: Analysis of channel utilization and opportunity perspective, Wireless Networks, 22 (2016), 33-47.  doi: 10.1007/s11276-015-0947-7.  Google Scholar

[3]

S. Jin, X. Yao and Z. Ma, A novel spectrum allocation strategy with channel bonding and channel reservation, KSII Transactions on Internet and Information Systems, 9 (2015), 4034-4053.  doi: 10.3837/tiis.2015.10.015.  Google Scholar

[4]

H. Katayama, H. Masuyama, S. Kasahara and Y. Takahashi, Effect of spectrum sensing overhead on performance for cognitive radio networks with channel bonding, Journal of Industrial and Management Optimization, 10 (2014), 21-40.  doi: 10.3934/jimo.2014.10.21.  Google Scholar

[5]

P. Kaur, A. Khosla and M. Uddin, Markovian queuing model for dynamic spectrum allocation in centralized architecture for cognitive radios, IACSIT International Journal of Engineering and Technology, 3 (2011), 96-101.  doi: 10.7763/IJET.2011.V3.206.  Google Scholar

[6]

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

[7]

P. Kolodzy, Spectrum policy task force: Finding and recommendations, International Symposium on Advanced Radio Technologies, 96 (2003), 392-393.   Google Scholar

[8]

S. Lee and G. Hwang, A new analytical model for optimized cognitive radio networks based on stochastic geometry, Journal of Industrial and Management Optimization, 13 (2017), 1883-1899.  doi: 10.3934/jimo.2017023.  Google Scholar

[9]

M. Neuts, Matrix-Geometric Solutions in Stochastic Models: An Algorithmic Approach, The Johns Hopkins Universit Press, Baltimore, MD, 1981.  Google Scholar

[10]

V. Tumuluru, P. Wang and D. Niyato, A novel spectrum-scheduling scheme for multi-channel cognitive radio network and performance analysis, IEEE Transactions on Vehicular Technology, 60 (2011), 1849-1858.  doi: 10.1109/TVT.2011.2114682.  Google Scholar

[11]

W. Wang, Z. Ma, W. Yue and Y. Takahashi, Performance analysis of a dynamic channel vacation scheme in cognitive radio networks, In Proceedings of the 13th International Conference on Queueing Theory and Network Applications, (2018), 183-190. doi: 10.1007/978-3-319-93736-6_14.  Google Scholar

[12]

K. Wu, W. Wang, H. Luo, G. Yu and Z. Zhang, Optimal resource allocation for cognitive radio networks with imperfect spectrum sensing, 2010 IEEE 71st Vehicular Technology Conference, 9 (2010), 1-4.  doi: 10.1109/VETECS.2010.5493676.  Google Scholar

[13]

H. Yu, W. Tang and S. Li, Joint optimal sensing time and power allocation for multi-channel cognitive radio networks considering sensing-channel selection, Science China Information Sciences, 57 (2014), 1-8.  doi: 10.1007/s11432-013-4813-x.  Google Scholar

[14]

Y. Zhao, S. Jin and W. Yue, Performance optimization of a dynamic channel bonding strategy in cognitive radio networks, Pacific Journal of Optimization, 9 (2013), 679-696.   Google Scholar

[15]

Y. Zhao and W. Yue, Performance evaluation and optimization of cognitive radio networks with adjustable access control for multiple secondary users, Journal of Industrial and Management Optimization, 15 (2019), 1-14.  doi: 10.3934/jimo.2018029.  Google Scholar

[16]

Y. Zhao and W. Yue, Cognitive radio networks with multiple secondary users under two kinds of priority schemes: Performance comparison and optimization, Journal of Industrial and Management Optimization, 13 (2017), 1475-1492.  doi: 10.3934/jimo.2017001.  Google Scholar

show all references

References:
[1]

Y. Chen, P. Liao and Y. Wang, A channel-hopping scheme for continuous rendezvous and data delivery in cognitive radio network, Peer-to-Peer Networking and Applications, 9 (2016), 16-27.  doi: 10.1007/s12083-014-0308-9.  Google Scholar

[2]

L. Chouhan and A. Trivedi, Performance study of a CSMA based multi-user MAC protocol for cognitive radio networks: Analysis of channel utilization and opportunity perspective, Wireless Networks, 22 (2016), 33-47.  doi: 10.1007/s11276-015-0947-7.  Google Scholar

[3]

S. Jin, X. Yao and Z. Ma, A novel spectrum allocation strategy with channel bonding and channel reservation, KSII Transactions on Internet and Information Systems, 9 (2015), 4034-4053.  doi: 10.3837/tiis.2015.10.015.  Google Scholar

[4]

H. Katayama, H. Masuyama, S. Kasahara and Y. Takahashi, Effect of spectrum sensing overhead on performance for cognitive radio networks with channel bonding, Journal of Industrial and Management Optimization, 10 (2014), 21-40.  doi: 10.3934/jimo.2014.10.21.  Google Scholar

[5]

P. Kaur, A. Khosla and M. Uddin, Markovian queuing model for dynamic spectrum allocation in centralized architecture for cognitive radios, IACSIT International Journal of Engineering and Technology, 3 (2011), 96-101.  doi: 10.7763/IJET.2011.V3.206.  Google Scholar

[6]

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

[7]

P. Kolodzy, Spectrum policy task force: Finding and recommendations, International Symposium on Advanced Radio Technologies, 96 (2003), 392-393.   Google Scholar

[8]

S. Lee and G. Hwang, A new analytical model for optimized cognitive radio networks based on stochastic geometry, Journal of Industrial and Management Optimization, 13 (2017), 1883-1899.  doi: 10.3934/jimo.2017023.  Google Scholar

[9]

M. Neuts, Matrix-Geometric Solutions in Stochastic Models: An Algorithmic Approach, The Johns Hopkins Universit Press, Baltimore, MD, 1981.  Google Scholar

[10]

V. Tumuluru, P. Wang and D. Niyato, A novel spectrum-scheduling scheme for multi-channel cognitive radio network and performance analysis, IEEE Transactions on Vehicular Technology, 60 (2011), 1849-1858.  doi: 10.1109/TVT.2011.2114682.  Google Scholar

[11]

W. Wang, Z. Ma, W. Yue and Y. Takahashi, Performance analysis of a dynamic channel vacation scheme in cognitive radio networks, In Proceedings of the 13th International Conference on Queueing Theory and Network Applications, (2018), 183-190. doi: 10.1007/978-3-319-93736-6_14.  Google Scholar

[12]

K. Wu, W. Wang, H. Luo, G. Yu and Z. Zhang, Optimal resource allocation for cognitive radio networks with imperfect spectrum sensing, 2010 IEEE 71st Vehicular Technology Conference, 9 (2010), 1-4.  doi: 10.1109/VETECS.2010.5493676.  Google Scholar

[13]

H. Yu, W. Tang and S. Li, Joint optimal sensing time and power allocation for multi-channel cognitive radio networks considering sensing-channel selection, Science China Information Sciences, 57 (2014), 1-8.  doi: 10.1007/s11432-013-4813-x.  Google Scholar

[14]

Y. Zhao, S. Jin and W. Yue, Performance optimization of a dynamic channel bonding strategy in cognitive radio networks, Pacific Journal of Optimization, 9 (2013), 679-696.   Google Scholar

[15]

Y. Zhao and W. Yue, Performance evaluation and optimization of cognitive radio networks with adjustable access control for multiple secondary users, Journal of Industrial and Management Optimization, 15 (2019), 1-14.  doi: 10.3934/jimo.2018029.  Google Scholar

[16]

Y. Zhao and W. Yue, Cognitive radio networks with multiple secondary users under two kinds of priority schemes: Performance comparison and optimization, Journal of Industrial and Management Optimization, 13 (2017), 1475-1492.  doi: 10.3934/jimo.2017001.  Google Scholar

Figure 1.  The dynamic channel vacation scheme proposed in this paper
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Figure 2.  The running mode of the system
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Figure 3.  The relation of $ E({L_1}) $ to $ {\mu _2} $ and $ c $
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Figure 4.  The relation of $ E({L_2}) $ to $ {\mu _2} $ and $ \theta $
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Figure 5.  The relation of $ {P_d} $ to $ {\lambda _2} $ and $ c $
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Figure 6.  The relation of $ {P_b} $ to $ {\lambda _2} $ and $ c $
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Figure 7.  The relation of $ {P_{wv}} $ to $ {\lambda _2} $ and $ c $
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Figure 8.  The relation of $ {P_u} $ to $ {\mu _2} $ and $ c $
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Figure 9.  The relation of $ {U_{I1}} $ to $ \mu_2 $ and $ \theta $
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Figure 10.  The relation of $ {U_{I2}} $ to $ \mu_2 $ and $ \theta $
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Figure 11.  The relation of $ {U_{s}} $ to $ {\lambda _2} $ and $ c $
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Figure 12.  The relation of $ {U_{s}} $ to $ {\mu_2} $ and $ \theta $
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Table 1.  The Relation of $ E(B) $ to $ {\lambda _2} $ and $ c $
$ c $ $ \lambda _2 =6 $ $ \lambda _2 =7 $ $ \lambda _2=8 $ $ \lambda _2=9 $ $ \lambda _2=10 $
3 0.4900 0.4983 0.5052 0.5109 0.5156
4 0.4678 0.4793 0.4891 0.4975 0.5046
5 0.4594 0.4731 0.4852 0.4957 0.5049
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$ c $ $ \lambda _2 =6 $ $ \lambda _2 =7 $ $ \lambda _2=8 $ $ \lambda _2=9 $ $ \lambda _2=10 $
3 0.4900 0.4983 0.5052 0.5109 0.5156
4 0.4678 0.4793 0.4891 0.4975 0.5046
5 0.4594 0.4731 0.4852 0.4957 0.5049
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