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. AbediN. MokariM. 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. JinW. 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. SalamehK. D. TurckH. BruneelC. 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. SalamiM. Ahmadian-AttariH. 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. WangZ. ZhouM. 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. XieF. 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. XuM. ZhengW. LiangH. 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. ZhaoS. 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. AbediN. MokariM. 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. JinW. 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. SalamehK. D. TurckH. BruneelC. 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. SalamiM. Ahmadian-AttariH. 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. WangZ. ZhouM. 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. XieF. 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. XuM. ZhengW. LiangH. 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. ZhaoS. 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

Figure 1.  The transmission process of the user packets in the system
Figure 2.  Throughput $ \rho_{su} $ of SU packets vs. the energy detection threshold $ \tau $
Figure 3.  Average latency $ \beta_{su} $ of SU packets vs. the energy detection threshold $ \tau $
Figure 4.  Switching rate $ \omega_{su} $ of SU packets vs. the energy detection threshold $ \tau $
Figure 5.  Channel utilization $ \sigma $ vs. the energy detection threshold $ \tau $
Figure 6.  Throughput $ \rho_{su} $ of SU packets vs. the number $ N $ of reserved channels
Figure 7.  Average latency $ \beta_{su} $ of SU packets vs. the number $ N $ of reserved channels
Figure 8.  Switching rate $ \omega_{su} $ of SU packets vs. the number $ N $ of reserved channels
Figure 9.  Channel utilization $ \sigma $ vs. the number $ N $ of reserved channels
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