# American Institute of Mathematical Sciences

• Previous Article
A simple and efficient technique to accelerate the computation of a nonlocal dielectric model for electrostatics of biomolecule
• JIMO Home
• This Issue
• Next Article
Admission control for finite capacity queueing model with general retrial times and state-dependent rates
doi: 10.3934/jimo.2018197

## An imperfect sensing-based channel reservation strategy in CRNs and its performance evaluation

 1 School of Information Science and Engineering, Yanshan University, Qinhuangdao, China 2 Hebei Normal University of Science and Technology, Qinhuangdao, China 3 Science and Technology on Communication Networks Laboratory, Shijiazhuang, China

*Corresponding author: Shunfu Jin

Received  October 2017 Revised  January 2018 Published  December 2018

Channel reservation strategy in CRNs is an effective technology for conserving communication resources. In this paper, using the imperfect sensing of secondary user (SU) packets, and considering the patience degree of SU packets, we propose a channel reservation strategy in a CRN. Aligned with the proposed channel reservation strategy, we establish a continuous-time Markov chain model to capture the stochastic behavior of the two types of user packets. Then, in order to obtain the steady-state probability distribution for the system model, we present a new algorithm for solving the quasi-birth-and-death (QBD) process. At last, based on the energy detection method, we evaluate the system performance in terms of the throughput of SU packets, the average latency of SU packets, the switching rate of SU packets and the channel utilization in relation to the energy detection threshold and the number of reserved channels.

Citation: Jianping Liu, Shunfu Jin. An imperfect sensing-based channel reservation strategy in CRNs and its performance evaluation. Journal of Industrial & Management Optimization, doi: 10.3934/jimo.2018197
##### References:
 [1] M. R. Abedi, N. Mokari, M. R. Javan and H. Yanikomeroglu, Secure communication in OFDMA-Based cognitive radio networks: An incentivized secondary network coexistence approach, IEEE Transactions on Vehicular Technology, 66 (2017), 1171-1185. Google Scholar [2] S. Behera and D. Seth, Efficient resource allocation in cognitive radio network under imperfect spectrum sensing and unsecured environment, Proceedings of the IEEE International Conference on Electrical, Electronics, Signals, Communication and Optimization, (2015), 1-5.Google Scholar [3] T. Chakraborty and I. Misra, Design and analysis of channel reservation scheme in cognitive radio networks, Computer Electric Engeering, 42 (2015), 148-167. Google Scholar [4] C. Guo, C. Feng and Z. Zeng, Cognitive Radio Network Technologies and Applications, Publishing House of Electronics Industry, Beijing, 2010 (in Chinese).Google Scholar [5] F. Hu and Y. Jin, Research on the selection of the optimal relaxation factor selection method for SOR method, Journal of Southwest Normal University, 33 (2008), 43-36. Google Scholar [6] S. Jin, W. Yue and Sh iying Ge, Equilibrium analysis of an opportunistic spectrum access mechanism with imperfect sensing results, Management Optimization, 13 (2017), 1255-1271. doi: 10.3934/jimo.2016071. Google Scholar [7] J. Liu, S. Jin and W. Yue, Performance evaluation and system optimization of green cognitive radio networks with amultiple-sleep mode, Annals of Operations Research, 2018, doi.org/10.1007/s10479-018-3086-6.Google Scholar [8] K. Muthumeenakshi and S. Radha, Distributed cognitive radio spectrum access with imperfect sensing using CTMC, International Journal of Distributed Sensor Networks, 2 (2013), 213-235. Google Scholar [9] R. V. Rao and V. D. Kalyankar, Parameter optimization of modern machining processes using teaching-learning-based optimization algorithm, Engineering Applications of Articial Intelligence, 26 (2013), 524-531. Google Scholar [10] A. Rehman, L. Yang and L. Hanzo, Performance of cognitive hybrid automatic repeat request: Go-Back-N, Proceedings of the IEEE Vehicular Technology Conference, (2016), 1-5.Google Scholar [11] O. Salameh, K. D. Turck, H. Bruneel, C. Blondia and S. Wittevrongel, Analysis of secondary user performance in cognitive radio networks with reactive spectrum handoff, Telecommunication Systems, 65 (2017), 539-550. Google Scholar [12] Z. Salami, M. Ahmadian-Attari, H. Jannati and M. R. Aref, A location privacy-preserving method for spectrum sharing in database-driven cognitive radio networks, Wireless Personal Communications, 95 (2017), 3687-3711. Google Scholar [13] S. Wang, Z. Zhou, M. Ge and C. Wang, Resource allocation for heterogeneous cognitive radio networks with imperfect spectrum sensing, IEEE Journal on Selected Areas in Communications, 31 (2013), 464-475. Google Scholar [14] R. Xie, F. Yu and H. Ji, Dynamic resource allocation for heterogeneous services in cognitive radio networks with imperfect channel sensing, IEEE Transactions on Vehicular Technology, 61 (2012), 770-780. Google Scholar [15] C. Xu, M. Zheng, W. Liang, H. Yu and Y. C. Liang, End-to-end throughput maximization for underlay multi-hop cognitive radio networks with RF energy harvesting, IEEE Transactions on Wireless Communications, 16 (2017), 3561-3572. Google Scholar [16] L. Zappaterra, J. Gomes, A. Arora and H. Choi, Resource discovery algorithms for channel aggregation in cognitive radio networks, Proceedings of the IEEE Wireless Communications and Networking Conference, (2013), 309-314.Google Scholar [17] Y. Zhao, S. Jin and W. Yue, An adjustable channel bonding strategy in centralized cognitive radio networks and its performance optimization, Quality Technology and Quantitative Management, 12 (2015), 291-310. Google Scholar

show all references

##### References:
 [1] M. R. Abedi, N. Mokari, M. R. Javan and H. Yanikomeroglu, Secure communication in OFDMA-Based cognitive radio networks: An incentivized secondary network coexistence approach, IEEE Transactions on Vehicular Technology, 66 (2017), 1171-1185. Google Scholar [2] S. Behera and D. Seth, Efficient resource allocation in cognitive radio network under imperfect spectrum sensing and unsecured environment, Proceedings of the IEEE International Conference on Electrical, Electronics, Signals, Communication and Optimization, (2015), 1-5.Google Scholar [3] T. Chakraborty and I. Misra, Design and analysis of channel reservation scheme in cognitive radio networks, Computer Electric Engeering, 42 (2015), 148-167. Google Scholar [4] C. Guo, C. Feng and Z. Zeng, Cognitive Radio Network Technologies and Applications, Publishing House of Electronics Industry, Beijing, 2010 (in Chinese).Google Scholar [5] F. Hu and Y. Jin, Research on the selection of the optimal relaxation factor selection method for SOR method, Journal of Southwest Normal University, 33 (2008), 43-36. Google Scholar [6] S. Jin, W. Yue and Sh iying Ge, Equilibrium analysis of an opportunistic spectrum access mechanism with imperfect sensing results, Management Optimization, 13 (2017), 1255-1271. doi: 10.3934/jimo.2016071. Google Scholar [7] J. Liu, S. Jin and W. Yue, Performance evaluation and system optimization of green cognitive radio networks with amultiple-sleep mode, Annals of Operations Research, 2018, doi.org/10.1007/s10479-018-3086-6.Google Scholar [8] K. Muthumeenakshi and S. Radha, Distributed cognitive radio spectrum access with imperfect sensing using CTMC, International Journal of Distributed Sensor Networks, 2 (2013), 213-235. Google Scholar [9] R. V. Rao and V. D. Kalyankar, Parameter optimization of modern machining processes using teaching-learning-based optimization algorithm, Engineering Applications of Articial Intelligence, 26 (2013), 524-531. Google Scholar [10] A. Rehman, L. Yang and L. Hanzo, Performance of cognitive hybrid automatic repeat request: Go-Back-N, Proceedings of the IEEE Vehicular Technology Conference, (2016), 1-5.Google Scholar [11] O. Salameh, K. D. Turck, H. Bruneel, C. Blondia and S. Wittevrongel, Analysis of secondary user performance in cognitive radio networks with reactive spectrum handoff, Telecommunication Systems, 65 (2017), 539-550. Google Scholar [12] Z. Salami, M. Ahmadian-Attari, H. Jannati and M. R. Aref, A location privacy-preserving method for spectrum sharing in database-driven cognitive radio networks, Wireless Personal Communications, 95 (2017), 3687-3711. Google Scholar [13] S. Wang, Z. Zhou, M. Ge and C. Wang, Resource allocation for heterogeneous cognitive radio networks with imperfect spectrum sensing, IEEE Journal on Selected Areas in Communications, 31 (2013), 464-475. Google Scholar [14] R. Xie, F. Yu and H. Ji, Dynamic resource allocation for heterogeneous services in cognitive radio networks with imperfect channel sensing, IEEE Transactions on Vehicular Technology, 61 (2012), 770-780. Google Scholar [15] C. Xu, M. Zheng, W. Liang, H. Yu and Y. C. Liang, End-to-end throughput maximization for underlay multi-hop cognitive radio networks with RF energy harvesting, IEEE Transactions on Wireless Communications, 16 (2017), 3561-3572. Google Scholar [16] L. Zappaterra, J. Gomes, A. Arora and H. Choi, Resource discovery algorithms for channel aggregation in cognitive radio networks, Proceedings of the IEEE Wireless Communications and Networking Conference, (2013), 309-314.Google Scholar [17] Y. Zhao, S. Jin and W. Yue, An adjustable channel bonding strategy in centralized cognitive radio networks and its performance optimization, Quality Technology and Quantitative Management, 12 (2015), 291-310. Google Scholar
The transmission process of the user packets in the system
Throughput $\rho_{su}$ of SU packets vs. the energy detection threshold $\tau$
Average latency $\beta_{su}$ of SU packets vs. the energy detection threshold $\tau$
Switching rate $\omega_{su}$ of SU packets vs. the energy detection threshold $\tau$
Channel utilization $\sigma$ vs. the energy detection threshold $\tau$
Throughput $\rho_{su}$ of SU packets vs. the number $N$ of reserved channels
Average latency $\beta_{su}$ of SU packets vs. the number $N$ of reserved channels
Switching rate $\omega_{su}$ of SU packets vs. the number $N$ of reserved channels
Channel utilization $\sigma$ vs. the number $N$ of reserved channels
 [1] Yuan Zhao, Wuyi Yue. Performance analysis and optimization for cognitive radio networks with a finite primary user buffer and a probability returning scheme. Journal of Industrial & Management Optimization, 2017, 13 (5) : 1-16. doi: 10.3934/jimo.2018195 [2] 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 [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] 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 [5] 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, 2019, 15 (1) : 1-14. doi: 10.3934/jimo.2018029 [6] 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 [7] 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 [8] 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 [9] Hsin-Yi Liu, Hsing Paul Luh. Kronecker product-forms of steady-state probabilities with $C_k$/$C_m$/$1$ by matrix polynomial approaches. Numerical Algebra, Control & Optimization, 2011, 1 (4) : 691-711. doi: 10.3934/naco.2011.1.691 [10] Zhenzhen Zheng, Ching-Shan Chou, Tau-Mu Yi, Qing Nie. Mathematical analysis of steady-state solutions in compartment and continuum models of cell polarization. Mathematical Biosciences & Engineering, 2011, 8 (4) : 1135-1168. doi: 10.3934/mbe.2011.8.1135 [11] Shin-Yi Lee, Shin-Hwa Wang, Chiou-Ping Ye. Explicit necessary and sufficient conditions for the existence of a dead core solution of a p-laplacian steady-state reaction-diffusion problem. Conference Publications, 2005, 2005 (Special) : 587-596. doi: 10.3934/proc.2005.2005.587 [12] 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 [13] Federica Di Michele, Bruno Rubino, Rosella Sampalmieri. A steady-state mathematical model for an EOS capacitor: The effect of the size exclusion. Networks & Heterogeneous Media, 2016, 11 (4) : 603-625. doi: 10.3934/nhm.2016011 [14] Mei-hua Wei, Jianhua Wu, Yinnian He. Steady-state solutions and stability for a cubic autocatalysis model. Communications on Pure & Applied Analysis, 2015, 14 (3) : 1147-1167. doi: 10.3934/cpaa.2015.14.1147 [15] Orazio Muscato, Wolfgang Wagner, Vincenza Di Stefano. Properties of the steady state distribution of electrons in semiconductors. Kinetic & Related Models, 2011, 4 (3) : 809-829. doi: 10.3934/krm.2011.4.809 [16] Zhanyou Ma, Wuyi Yue, Xiaoli Su. Performance analysis of a Geom/Geom/1 queueing system with variable input probability. Journal of Industrial & Management Optimization, 2011, 7 (3) : 641-653. doi: 10.3934/jimo.2011.7.641 [17] Theodore Kolokolnikov, Michael J. Ward, Juncheng Wei. The stability of steady-state hot-spot patterns for a reaction-diffusion model of urban crime. Discrete & Continuous Dynamical Systems - B, 2014, 19 (5) : 1373-1410. doi: 10.3934/dcdsb.2014.19.1373 [18] Tomoyuki Miyaji, Yoshio Tsutsumi. Steady-state mode interactions of radially symmetric modes for the Lugiato-Lefever equation on a disk. Communications on Pure & Applied Analysis, 2018, 17 (4) : 1633-1650. doi: 10.3934/cpaa.2018078 [19] Ken Shirakawa. Stability for steady-state patterns in phase field dynamics associated with total variation energies. Discrete & Continuous Dynamical Systems - A, 2006, 15 (4) : 1215-1236. doi: 10.3934/dcds.2006.15.1215 [20] Wing-Cheong Lo. Morphogen gradient with expansion-repression mechanism: Steady-state and robustness studies. Discrete & Continuous Dynamical Systems - B, 2014, 19 (3) : 775-787. doi: 10.3934/dcdsb.2014.19.775

2018 Impact Factor: 1.025

## Tools

Article outline

Figures and Tables

[Back to Top]