doi: 10.3934/jimo.2018029

Performance evaluation and optimization of cognitive radio networks with adjustable access control for multiple secondary users

1. 

School of Computer and Communication Engineering, Northeastern University at Qinhuangdao, Qinhuangdao 066004, China

2. 

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

* Corresponding author: Yuan Zhao

Received  February 2017 Revised  July 2017 Published  February 2018

Fund Project: The reviewing process of this paper was handled by Yutaka Takahashi

In this paper, we consider a cognitive radio network with multiple secondary users (SUs). The SU packets in the system can be divided into two categories: SU1 packets and SU2 packets, where SU1 packets have transmission priority over SU2 packets. Considering the absolute priority of the primary users (PUs), the PU packets have the highest priority in the system to transmit. In order to guarantee the Quality of Service (QoS) of the network users, as well as reduce the average delay of the SU2 packets, we propose an adjustable access control scheme for the SU2 packets. A newly arriving SU2 packet can access the system with an access probability related to the total number of packets in the system. A variable factor is also introduced to adjust the access probability dynamically. Based on the working principle of the adjustable access control scheme, we build a discrete-time queueing model with a finite waiting room and an adjustable joining rate. With a steady-state analysis of the queueing model, using a three-dimensional Markov chain, we derive some performance measures, such as the total channel utilization, the interruption rate, the throughput, and the average delay of the SU2 packets. Moreover, we show the influence of the adjustment factor on different system performance measures by using numerical results. Finally, considering the trade-off between the throughput and the average delay of the SU2 packets with respect to the adjustment factor, we build a net benefit function and show an optimal algorithm to optimize the adjustment factor.

Citation: Yuan Zhao, Wuyi Yue. Performance evaluation and optimization of cognitive radio networks with adjustable access control for multiple secondary users. Journal of Industrial & Management Optimization, doi: 10.3934/jimo.2018029
References:
[1]

S. AghajeriA. Sharafat and K. Navaie, Primary service outage degradation in dynamic spectrum sharing with non-ideal spectrum sensing, IET Communications, 6 (2012), 1252-1261. doi: 10.1049/iet-com.2011.0476.

[2]

A. Alfa, Queueing Theory for Telecommunications: Discrete Time Modelling of a Single Node System ,Springer, New York, 2010.

[3]

E. P. Chong and S.${{\rm{\dot Z}}}$ak, An Introduction to Optimization, Third Edition, Wiley, Hoboken, 2008. doi: 10.1002/9781118033340.

[4]

C. DingK. 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.

[5]

A. Greenbaum, Iterative Methods for Solving Linear Systems, Society for Industrial and Applied Mathematics, Philadelphia, 1997.

[6]

S. Jin, Y. Zhao, W. Yue and Z. Saffer, Performance analysis and optimization of an adaptive admission control scheme in cognitive radio networks, Mathematical Problems in Engineering, 2013 (2013), Article ID 727310, 10 pages. doi: 10.1155/2013/727310.

[7]

Y. LeeC. Park and D. Sim, Cognitive radio spectrum access with prioritized secondary users, Applied Mathematics & Information Sciences, 6 (2012), 595S-601S.

[8]

H. Li and Z. Han, Socially optimal queuing control in cognitive radio networks subject to service interruptions: To queue or not to queue?, IEEE Transactions on Wireless Communications, 10 (2011), 1656-1666.

[9]

J. Marinho and E. Monteiro, Cognitive radio: Survey on communication protocols, spectrum decision issues, and future research directions, Wireless Networks, 18 (2012), 147-164. doi: 10.1007/s11276-011-0392-1.

[10]

M. NaeemA. AnpalaganM. Jaseemuddin and D. Lee, Resource allocation techniques in cooperative cognitive radio networks, IEEE Communications Surveys & Tutorials, 16 (2014), 729-744. doi: 10.1109/SURV.2013.102313.00272.

[11]

N. Nguyen-ThanhA. Pham and V. T. Nguyen, Medium access control design for cognitive radio networks: A survey, IEICE Transactions on Communications, E97-B (2014), 359-374. doi: 10.1587/transcom.E97.B.359.

[12]

S. SharmaT. BogaleS. ChatzinotasB. OtterstenL. Le and X. Wang, Cognitive radio techniques under practical imperfections: A survey, IEEE Communications Surveys & Tutorials, 17 (2015), 1858-1884. doi: 10.1109/COMST.2015.2452414.

[13]

N. Tian and Z. Zhang, Vacation Queueing Models: Theory and Applications, Springer, New York, 2006.

[14]

E. TragosS. ZeadallyA. Fragkiadakis and V. Siris, Spectrum assignment in cognitive radio networks: A comprehensive survey, IEEE Communications Surveys & Tutorials, 15 (2013), 1108-1135. doi: 10.1109/SURV.2012.121112.00047.

[15]

D. Willkomm and A. Wolisz, Efficient QoS support for secondary users in cognitive radio systems, IEEE Wireless Communications, 17 (2010), 16-23.

[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 IndustrialI and Management Optimization, 13 (2017), 1449-1466.

[17]

Y. Zhao and W. Yue, An adjustable access control scheme in cognitive radio networks with multiple secondary users in Proceesings of 11th International Conference on Queueing Theory and Network Applications, ACM, (2016), Article No. 10, 5 pages. doi: 10.1145/3016032.3016040.

show all references

References:
[1]

S. AghajeriA. Sharafat and K. Navaie, Primary service outage degradation in dynamic spectrum sharing with non-ideal spectrum sensing, IET Communications, 6 (2012), 1252-1261. doi: 10.1049/iet-com.2011.0476.

[2]

A. Alfa, Queueing Theory for Telecommunications: Discrete Time Modelling of a Single Node System ,Springer, New York, 2010.

[3]

E. P. Chong and S.${{\rm{\dot Z}}}$ak, An Introduction to Optimization, Third Edition, Wiley, Hoboken, 2008. doi: 10.1002/9781118033340.

[4]

C. DingK. 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.

[5]

A. Greenbaum, Iterative Methods for Solving Linear Systems, Society for Industrial and Applied Mathematics, Philadelphia, 1997.

[6]

S. Jin, Y. Zhao, W. Yue and Z. Saffer, Performance analysis and optimization of an adaptive admission control scheme in cognitive radio networks, Mathematical Problems in Engineering, 2013 (2013), Article ID 727310, 10 pages. doi: 10.1155/2013/727310.

[7]

Y. LeeC. Park and D. Sim, Cognitive radio spectrum access with prioritized secondary users, Applied Mathematics & Information Sciences, 6 (2012), 595S-601S.

[8]

H. Li and Z. Han, Socially optimal queuing control in cognitive radio networks subject to service interruptions: To queue or not to queue?, IEEE Transactions on Wireless Communications, 10 (2011), 1656-1666.

[9]

J. Marinho and E. Monteiro, Cognitive radio: Survey on communication protocols, spectrum decision issues, and future research directions, Wireless Networks, 18 (2012), 147-164. doi: 10.1007/s11276-011-0392-1.

[10]

M. NaeemA. AnpalaganM. Jaseemuddin and D. Lee, Resource allocation techniques in cooperative cognitive radio networks, IEEE Communications Surveys & Tutorials, 16 (2014), 729-744. doi: 10.1109/SURV.2013.102313.00272.

[11]

N. Nguyen-ThanhA. Pham and V. T. Nguyen, Medium access control design for cognitive radio networks: A survey, IEICE Transactions on Communications, E97-B (2014), 359-374. doi: 10.1587/transcom.E97.B.359.

[12]

S. SharmaT. BogaleS. ChatzinotasB. OtterstenL. Le and X. Wang, Cognitive radio techniques under practical imperfections: A survey, IEEE Communications Surveys & Tutorials, 17 (2015), 1858-1884. doi: 10.1109/COMST.2015.2452414.

[13]

N. Tian and Z. Zhang, Vacation Queueing Models: Theory and Applications, Springer, New York, 2006.

[14]

E. TragosS. ZeadallyA. Fragkiadakis and V. Siris, Spectrum assignment in cognitive radio networks: A comprehensive survey, IEEE Communications Surveys & Tutorials, 15 (2013), 1108-1135. doi: 10.1109/SURV.2012.121112.00047.

[15]

D. Willkomm and A. Wolisz, Efficient QoS support for secondary users in cognitive radio systems, IEEE Wireless Communications, 17 (2010), 16-23.

[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 IndustrialI and Management Optimization, 13 (2017), 1449-1466.

[17]

Y. Zhao and W. Yue, An adjustable access control scheme in cognitive radio networks with multiple secondary users in Proceesings of 11th International Conference on Queueing Theory and Network Applications, ACM, (2016), Article No. 10, 5 pages. doi: 10.1145/3016032.3016040.

Figure 1.  Diagram for the proposed adjustable access control scheme
Figure 2.  Total channel utilization $\delta$ vs. adjustment factor $\tau$
Figure 3.  Interruption rate $\gamma$ of the SU2 packets vs. adjustment factor $\tau$
Figure 4.  Throughput $\theta$ of the SU2 packets vs. adjustment factor $\tau$
Figure 5.  Average delay $\sigma$ of the SU2 packets vs. adjustment factor $\tau$
Table 1.  Optimal adjustment factor $\tau^*$ and the maximum net benefit $B(\tau^*)$
Buffer capacityArrival rates of packetsOptimal adjustment factorMaximum net benefit
$K$$\lambda_1, \lambda_{21}, \lambda_{22}$$\tau^*$$B(\tau^*)$
$5$$\lambda_1=0.1, \lambda_{21}=0.1, \lambda_{22}=0.2$0.00067.4084
$\lambda_1=0.2, \lambda_{21}=0.1, \lambda_{22}=0.2$0.11783.5230
$\lambda_1=0.2, \lambda_{21}=0.2, \lambda_{22}=0.2$0.32320.0113
$\lambda_1=0.2, \lambda_{21}=0.2, \lambda_{22}=0.3$0.58872.4589
$10$ $\lambda_1=0.1, \lambda_{21}=0.1, \lambda_{22}=0.2$0.02527.3280
$\lambda_1=0.2, \lambda_{21}=0.1, \lambda_{22}=0.2$0.13413.4942
$\lambda_1=0.2, \lambda_{21}=0.2, \lambda_{22}=0.2$0.33420.0031
$\lambda_1=0.2, \lambda_{21}=0.2, \lambda_{22}=0.3$0.60122.4494
Buffer capacityArrival rates of packetsOptimal adjustment factorMaximum net benefit
$K$$\lambda_1, \lambda_{21}, \lambda_{22}$$\tau^*$$B(\tau^*)$
$5$$\lambda_1=0.1, \lambda_{21}=0.1, \lambda_{22}=0.2$0.00067.4084
$\lambda_1=0.2, \lambda_{21}=0.1, \lambda_{22}=0.2$0.11783.5230
$\lambda_1=0.2, \lambda_{21}=0.2, \lambda_{22}=0.2$0.32320.0113
$\lambda_1=0.2, \lambda_{21}=0.2, \lambda_{22}=0.3$0.58872.4589
$10$ $\lambda_1=0.1, \lambda_{21}=0.1, \lambda_{22}=0.2$0.02527.3280
$\lambda_1=0.2, \lambda_{21}=0.1, \lambda_{22}=0.2$0.13413.4942
$\lambda_1=0.2, \lambda_{21}=0.2, \lambda_{22}=0.2$0.33420.0031
$\lambda_1=0.2, \lambda_{21}=0.2, \lambda_{22}=0.3$0.60122.4494
[1]

Yuan Zhao, Wuyi Yue. Cognitive radio networks with multiple secondary users under two kinds of priority schemes: Performance comparison and optimization. Journal of Industrial & Management Optimization, 2017, 13 (3) : 1449-1466. doi: 10.3934/jimo.2017001

[2]

Hyeon Je Cho, Ganguk Hwang. Optimal design for dynamic spectrum access in cognitive radio networks under Rayleigh fading. Journal of Industrial & Management Optimization, 2012, 8 (4) : 821-840. doi: 10.3934/jimo.2012.8.821

[3]

Jae Deok Kim, Ganguk Hwang. Cross-layer modeling and optimization of multi-channel cognitive radio networks under imperfect channel sensing. Journal of Industrial & Management Optimization, 2015, 11 (3) : 807-828. doi: 10.3934/jimo.2015.11.807

[4]

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

[5]

Haruki Katayama, Hiroyuki Masuyama, Shoji Kasahara, Yutaka Takahashi. Effect of spectrum sensing overhead on performance for cognitive radio networks with channel bonding. Journal of Industrial & Management Optimization, 2014, 10 (1) : 21-40. doi: 10.3934/jimo.2014.10.21

[6]

Seunghee Lee, Ganguk Hwang. A new analytical model for optimized cognitive radio networks based on stochastic geometry. Journal of Industrial & Management Optimization, 2017, 13 (4) : 1883-1899. doi: 10.3934/jimo.2017023

[7]

Shengzhu Jin, Bong Dae Choi, Doo Seop Eom. Performance analysis of binary exponential backoff MAC protocol for cognitive radio in the IEEE 802.16e/m network. Journal of Industrial & Management Optimization, 2017, 13 (3) : 1483-1494. doi: 10.3934/jimo.2017003

[8]

Yuan Zhao, Shunfu Jin, Wuyi Yue. Adjustable admission control with threshold in centralized CR networks: Analysis and optimization. Journal of Industrial & Management Optimization, 2015, 11 (4) : 1393-1408. doi: 10.3934/jimo.2015.11.1393

[9]

Yanming Ge. Analysis of airline seat control with region factor. Journal of Industrial & Management Optimization, 2012, 8 (2) : 363-378. doi: 10.3934/jimo.2012.8.363

[10]

Radu C. Cascaval, Ciro D'Apice, Maria Pia D'Arienzo, Rosanna Manzo. Flow optimization in vascular networks. Mathematical Biosciences & Engineering, 2017, 14 (3) : 607-624. doi: 10.3934/mbe.2017035

[11]

Guoliang Xue, Weiyi Zhang, Tie Wang, Krishnaiyan Thulasiraman. On the partial path protection scheme for WDM optical networks and polynomial time computability of primary and secondary paths. Journal of Industrial & Management Optimization, 2007, 3 (4) : 625-643. doi: 10.3934/jimo.2007.3.625

[12]

Holger Boche, Rafael F. Schaefer. Arbitrarily varying multiple access channels with conferencing encoders: List decoding and finite coordination resources. Advances in Mathematics of Communications, 2016, 10 (2) : 333-354. doi: 10.3934/amc.2016009

[13]

Sebastian Acosta. A control approach to recover the wave speed (conformal factor) from one measurement. Inverse Problems & Imaging, 2015, 9 (2) : 301-315. doi: 10.3934/ipi.2015.9.301

[14]

Giuseppe Buttazzo, Filippo Santambrogio. Asymptotical compliance optimization for connected networks. Networks & Heterogeneous Media, 2007, 2 (4) : 761-777. doi: 10.3934/nhm.2007.2.761

[15]

Michael Herty, Veronika Sachers. Adjoint calculus for optimization of gas networks. Networks & Heterogeneous Media, 2007, 2 (4) : 733-750. doi: 10.3934/nhm.2007.2.733

[16]

Klaus-Jochen Engel, Marjeta Kramar Fijavž, Rainer Nagel, Eszter Sikolya. Vertex control of flows in networks. Networks & Heterogeneous Media, 2008, 3 (4) : 709-722. doi: 10.3934/nhm.2008.3.709

[17]

Yue Qi, Zhihao Wang, Su Zhang. On analyzing and detecting multiple optima of portfolio optimization. Journal of Industrial & Management Optimization, 2018, 14 (1) : 309-323. doi: 10.3934/jimo.2017048

[18]

Ö. Uğur, G. W. Weber. Optimization and dynamics of gene-environment networks with intervals. Journal of Industrial & Management Optimization, 2007, 3 (2) : 357-379. doi: 10.3934/jimo.2007.3.357

[19]

Michael Herty. Modeling, simulation and optimization of gas networks with compressors. Networks & Heterogeneous Media, 2007, 2 (1) : 81-97. doi: 10.3934/nhm.2007.2.81

[20]

Fabio Ancona, Laura Caravenna, Annalisa Cesaroni, Giuseppe M. Coclite, Claudio Marchi, Andrea Marson. Analysis and control on networks: Trends and perspectives. Networks & Heterogeneous Media, 2017, 12 (3) : i-ii. doi: 10.3934/nhm.201703i

2017 Impact Factor: 0.994

Article outline

Figures and Tables

[Back to Top]