Pricing Policy for a Dynamic Spectrum Allocation Scheme with Batch Requests and Impatient Packets in Cognitive Radio Networks

Haixing Wu , Shunfu Jin , Wuyi Yue

Journal of Systems Science and Systems Engineering ›› 2022, Vol. 31 ›› Issue (2) : 133 -149.

PDF
Journal of Systems Science and Systems Engineering ›› 2022, Vol. 31 ›› Issue (2) : 133 -149. DOI: 10.1007/s11518-022-5521-0
Article

Pricing Policy for a Dynamic Spectrum Allocation Scheme with Batch Requests and Impatient Packets in Cognitive Radio Networks

Author information +
History +
PDF

Abstract

In cognitive radio networks (CRNs), multiple secondary users may send out requests simultaneously and one secondary user may send out multiple requests at one time, i.e., request arrivals usually show an aggregate manner. Moreover, a secondary user packet waiting in the buffer may leave the system due to impatience before it is transmitted, and this impatient behavior inevitably has an impact on the system performance. Aiming to investigate the influence of the aggregate behavior of requests and the likelihood of impatience on a dynamic spectrum allocation scheme in CRNs, in this paper a batch arrival queueing model with possible reneging and potential transmission interruption is established. By constructing a Markov chain and presenting a transition rate matrix, the steady-state distribution of the queueing model along with a dynamic spectrum allocation scheme is derived to analyze the stochastic behavior of the system. Accordingly, some important performance measures such as the loss rate, the balk rate and the average delay of secondary user packets are given. Moreover, system experiments are carried out to show the change trends of the performance measures with respect to batch arrival rates of secondary user packets for different impatience parameters, different batch sizes of secondary user packets, and different arrival rates of primary user packets. Finally, a pricing policy for secondary users is presented and the dynamic spectrum allocation scheme is socially optimized.

Keywords

Cognitive radio networks / dynamic spectrum scheme / batch arrival / impatient packets / Markov chain / social optimization

Cite this article

Download citation ▾
Haixing Wu, Shunfu Jin, Wuyi Yue. Pricing Policy for a Dynamic Spectrum Allocation Scheme with Batch Requests and Impatient Packets in Cognitive Radio Networks. Journal of Systems Science and Systems Engineering, 2022, 31(2): 133-149 DOI:10.1007/s11518-022-5521-0

登录浏览全文

4963

注册一个新账户 忘记密码

References

[1]

Domenico A D, Strinati E C, Benedetto M G D. A survey on MAC strategies for cognitive radio networks. IEEE Communications Surveys & Tutorials, 2012, 14(1): 21-44.

[2]

Farraj AK. Queue model analysis for spectrum sharing cognitive systems under outage probability constraint. Wireless Personal Communications, 2013, 73(3): 1021-1035.

[3]

Geirhofer S, Lang T, Sadler B M. Cognitive radios for dynamic spectrum access-dynamic spectrum access in the time domain: Modeling and exploiting white space. IEEE Communication Magazine, 2007, 45(5): 66-72.

[4]

Ghosh G, Das P, Chatterjee S. Cognitive radio and dynamic spectrum access-Astudy. International Journal of Next-Generation Networks, 2014, 6(1): 43-60.

[5]

Hassin R, Haviv M. To Queue or Not to Queue: Equilibrium Behavior in Queueing Systems, 2003, New York: Kluwer Academic Publishers.

[6]

Jin S, Ge S, Yue W (2015). Performance evaluation for an opportunistic spectrum access mechanism with impatience behavior and imperfect sensing results. Proceedings of the Seventh International Conference on Ubiquitous and Future Networks. Sapporo, Japan, July 7–10, 2015.

[7]

Jin S, Yue W (2016). A dynamic spectrum allocation strategy in CRNs and its performance evaluation. Proceedings of the 17th International Symposium on Knowledge and Systems Sciences. Kobe, Japan, November 4–6, 2016.

[8]

Jin S, Hao S, Qie X. A virtual machine scheduling strategy with a speed switch and a multi-sleep mode in cloud data centers. Journal of Systems Science and Systems Engineering, 2019, 28(2): 194-210.

[9]

Li H, Han Z. Socially optimal queuing control in cognitive radio networks subject to service interruptions: To queue or not to queue?. IEEE Transactions on Wireless Communications, 2011, 10(5): 1656-1666.

[10]

Liu C, Wang J, Liu X, Liang Y. Deep CM-CNN for spectrum sensing in cognitive radio. IEEE Journal on Selected Areas in Communications, 2019, 37(10): 2306-2321.

[11]

Liu C, Wang J, Liu X, Liang Y. Maximum eigenvalue-based goodness-of-fit detection for spectrum sensing in cognitive radio. IEEE Transactions on Vehicular Technology, 2019, 68(8): 7747-7760.

[12]

Martinez-Bauset J, Popescu A, Pla V, Popescu A (2012). Cognitive radio networks with elastic traffic. Proceedings of the 8th Euro-NF Conference on Next Generation Internet. Karlskrona, Sweden, June 25–27, 2012.

[13]

Oklander B, Sidi M. On cognitive processes in cognitive radio networks. Wireless Networks, 2014, 20(2): 319-330.

[14]

Rajesh G, Raajini XM, Sagayam K, Bhushan B, Kse U. Fuzzy genetic based dynamic spectrum allocation approach for cognitive radio sensor networks. Turkish Journal of Electrical Engineering and Computer Sciences, 2020, 28(5): 2416-2432.

[15]

Ramzan M R, Qadri N N, Ahmed A, Naeem M. Multi-objective optimization for spectrum sharing in cognitive radio networks: A review. Pervasive and Mobile Computing, 2017, 41: 106-131.

[16]

Ratnaparkhi S C, Venkatesan M, Kulkarni A.V (2016). Realization of dynamic spectrum allocation using PSO. Proceedings of the Conference on Advances in Signal Processing. Pune, India, June 9–11, 2016.

[17]

Saha RK. Licensed countrywide full-spectrum allocation: A new paradigm for millimeter-wave mobile systems in 5G/6G era. IEEE Access, 2020, 8: 166612-166629.

[18]

Shekhar C, Varshney S, Kumar A. Matrix-geometric solution of multi-server queueing systems with Bernoulli scheduled modified vacation and retention of reneged customers: A meta-heuristic approach. Quality Technology & Quantitative Management, 2021, 18(1): 39-66.

[19]

Wang B, Liu KJR. Advances in cognitive radio networks: A survey. IEEE Journal of Selected Topics in Signal Processing, 2011, 5(1): 5-23.

[20]

Wang J, Zhang F. Equilibrium analysis of the observable queues with balking and delayed repairs. Applied Mathematics and Computation, 2011, 218(6): 2716-2729.

[21]

Wang J, Huang A, Wang W, Quek T. Admission control in cognitive radio networks with finite queue and user impatience. IEEE Wireless Communications Letters, 2013, 2(2): 175-178.

[22]

Wang J, Zhang Y, Li W. Strategic joining and optimal pricing in the cognitive radio system with delay-sensitive secondary users. IEEE Transactions on Cognitive Communications and Networking, 2017, 3(3): 298-312.

[23]

Wu Z, Xu H, Ke GY. The strategy of third-party mediation based on the option prioritization in the graph model. Journal of Systems Science and Systems Engineering, 2019, 28(4): 399-414.

[24]

Xie J, Liu C, Liang Y, Fang J. Activity pattern aware spectrum sensing: A CNN-based deep learning approach. IEEE Communications Letters, 2019, 23(6): 1025-1028.

[25]

Yadav P, Chaterjee S, Bhattacharya P P. A survey on dynamic spectrum access techniques in cognitive radio. International Journal of Next-Generation Networks, 2012, 4(1): 27-46.

[26]

Yang Y, Zhang Q, Wang Y, Emoto T, Akutagawa M, Konaka S. Multi-strategy dynamic spectrum access in cognitive radio networks: Modeling, analysis and optimization. China Communications, 2019, 16(3): 103-121.

[27]

Zhang W, Sun Y, Deng L, Yeo C, Yang L. Dynamic spectrum allocation for heterogeneous cognitive radio networks with multiple channels. IEEE Systems Journal, 2019, 13(1): 53-64.

[28]

Zeng Z, Liu M, Wang J, Lan D. Non-cooperative spectrum access strategy based on impatient behavior of secondary users in cognitive radio networks. Electronics, 2019, 8(9): 995-1008.

AI Summary AI Mindmap
PDF

121

Accesses

0

Citation

Detail

Sections
Recommended

AI思维导图

/