doi: 10.3934/jimo.2020144

Performance analysis and optimization research of multi-channel cognitive radio networks with a dynamic channel vacation scheme

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 Published  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.

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. ChenP. 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. JinX. 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. KatayamaH. MasuyamaS. 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. KaurA. 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. TumuluruP. 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. WuW. WangH. LuoG. 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. YuW. 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. ZhaoS. 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. ChenP. 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. JinX. 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. KatayamaH. MasuyamaS. 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. KaurA. 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. TumuluruP. 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. WuW. WangH. LuoG. 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. YuW. 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. ZhaoS. 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
Figure 2.  The running mode of the system
Figure 3.  The relation of $ E({L_1}) $ to $ {\mu _2} $ and $ c $
Figure 4.  The relation of $ E({L_2}) $ to $ {\mu _2} $ and $ \theta $
Figure 5.  The relation of $ {P_d} $ to $ {\lambda _2} $ and $ c $
Figure 6.  The relation of $ {P_b} $ to $ {\lambda _2} $ and $ c $
Figure 7.  The relation of $ {P_{wv}} $ to $ {\lambda _2} $ and $ c $
Figure 8.  The relation of $ {P_u} $ to $ {\mu _2} $ and $ c $
Figure 9.  The relation of $ {U_{I1}} $ to $ \mu_2 $ and $ \theta $
Figure 10.  The relation of $ {U_{I2}} $ to $ \mu_2 $ and $ \theta $
Figure 11.  The relation of $ {U_{s}} $ to $ {\lambda _2} $ and $ c $
Figure 12.  The relation of $ {U_{s}} $ to $ {\mu_2} $ and $ \theta $
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
$ 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
[1]

Nouressadat Touafek, Durhasan Turgut Tollu, Youssouf Akrour. On a general homogeneous three-dimensional system of difference equations. Electronic Research Archive, , () : -. doi: 10.3934/era.2021017

[2]

Irena PawŃow, Wojciech M. Zajączkowski. Global regular solutions to three-dimensional thermo-visco-elasticity with nonlinear temperature-dependent specific heat. Communications on Pure & Applied Analysis, 2017, 16 (4) : 1331-1372. doi: 10.3934/cpaa.2017065

[3]

Cheng Wang. Convergence analysis of Fourier pseudo-spectral schemes for three-dimensional incompressible Navier-Stokes equations. Electronic Research Archive, , () : -. doi: 10.3934/era.2021019

[4]

Yao Nie, Jia Yuan. The Littlewood-Paley $ pth $-order moments in three-dimensional MHD turbulence. Discrete & Continuous Dynamical Systems, 2021, 41 (7) : 3045-3062. doi: 10.3934/dcds.2020397

[5]

Omer Gursoy, Kamal Adli Mehr, Nail Akar. Steady-state and first passage time distributions for waiting times in the $ MAP/M/s+G $ queueing model with generally distributed patience times. Journal of Industrial & Management Optimization, 2021  doi: 10.3934/jimo.2021078

[6]

Shangzhi Li, Shangjiang Guo. Permanence and extinction of a stochastic SIS epidemic model with three independent Brownian motions. Discrete & Continuous Dynamical Systems - B, 2021, 26 (5) : 2693-2719. doi: 10.3934/dcdsb.2020201

[7]

Reza Lotfi, Yahia Zare Mehrjerdi, Mir Saman Pishvaee, Ahmad Sadeghieh, Gerhard-Wilhelm Weber. A robust optimization model for sustainable and resilient closed-loop supply chain network design considering conditional value at risk. Numerical Algebra, Control & Optimization, 2021, 11 (2) : 221-253. doi: 10.3934/naco.2020023

[8]

Wenjuan Zhao, Shunfu Jin, Wuyi Yue. A stochastic model and social optimization of a blockchain system based on a general limited batch service queue. Journal of Industrial & Management Optimization, 2021, 17 (4) : 1845-1861. doi: 10.3934/jimo.2020049

[9]

Lu Xu, Chunlai Mu, Qiao Xin. Global boundedness of solutions to the two-dimensional forager-exploiter model with logistic source. Discrete & Continuous Dynamical Systems, 2021, 41 (7) : 3031-3043. doi: 10.3934/dcds.2020396

[10]

Jan Rychtář, Dewey T. Taylor. Moran process and Wright-Fisher process favor low variability. Discrete & Continuous Dynamical Systems - B, 2021, 26 (7) : 3491-3504. doi: 10.3934/dcdsb.2020242

[11]

Ashkan Ayough, Farbod Farhadi, Mostafa Zandieh, Parisa Rastkhadiv. Genetic algorithm for obstacle location-allocation problems with customer priorities. Journal of Industrial & Management Optimization, 2021, 17 (4) : 1753-1769. doi: 10.3934/jimo.2020044

[12]

Ajay Jasra, Kody J. H. Law, Yaxian Xu. Markov chain simulation for multilevel Monte Carlo. Foundations of Data Science, 2021, 3 (1) : 27-47. doi: 10.3934/fods.2021004

[13]

Alexander Tolstonogov. BV solutions of a convex sweeping process with a composed perturbation. Evolution Equations & Control Theory, 2021  doi: 10.3934/eect.2021012

[14]

Caifang Wang, Tie Zhou. The order of convergence for Landweber Scheme with $\alpha,\beta$-rule. Inverse Problems & Imaging, 2012, 6 (1) : 133-146. doi: 10.3934/ipi.2012.6.133

[15]

Roberto Civino, Riccardo Longo. Formal security proof for a scheme on a topological network. Advances in Mathematics of Communications, 2021  doi: 10.3934/amc.2021009

[16]

Xiaohong Li, Mingxin Sun, Zhaohua Gong, Enmin Feng. Multistage optimal control for microbial fed-batch fermentation process. Journal of Industrial & Management Optimization, 2021  doi: 10.3934/jimo.2021040

[17]

José Antonio Carrillo, Martin Parisot, Zuzanna Szymańska. Mathematical modelling of collagen fibres rearrangement during the tendon healing process. Kinetic & Related Models, 2021, 14 (2) : 283-301. doi: 10.3934/krm.2021005

[18]

Jinsen Guo, Yongwu Zhou, Baixun Li. The optimal pricing and service strategies of a dual-channel retailer under free riding. Journal of Industrial & Management Optimization, 2021  doi: 10.3934/jimo.2021056

[19]

Rafael G. L. D'Oliveira, Marcelo Firer. Minimum dimensional Hamming embeddings. Advances in Mathematics of Communications, 2017, 11 (2) : 359-366. doi: 10.3934/amc.2017029

[20]

Liqiang Jin, Yanqing Liu, Yanyan Yin, Kok Lay Teo, Fei Liu. Design of probabilistic $ l_2-l_\infty $ filter for uncertain Markov jump systems with partial information of the transition probabilities. Journal of Industrial & Management Optimization, 2021  doi: 10.3934/jimo.2021070

2019 Impact Factor: 1.366

Article outline

Figures and Tables

[Back to Top]