Optimal Price-Based Power Control Algorithm with Quality of Service Constraints in Cognitive Radio Networks

WANG Zhengqiang; JIANG Lingge; HE Chen

doi:10.1049/cje.2015.04.028

Volume 24 Issue 2

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WANG Zhengqiang, JIANG Lingge, HE Chen, “Optimal Price-Based Power Control Algorithm with Quality of Service Constraints in Cognitive Radio Networks,” Chinese Journal of Electronics, vol. 24, no. 2, pp. 393-397, 2015, doi: 10.1049/cje.2015.04.028

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WANG Zhengqiang, JIANG Lingge, HE Chen, “Optimal Price-Based Power Control Algorithm with Quality of Service Constraints in Cognitive Radio Networks,” Chinese Journal of Electronics, vol. 24, no. 2, pp. 393-397, 2015, doi: 10.1049/cje.2015.04.028

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Optimal Price-Based Power Control Algorithm with Quality of Service Constraints in Cognitive Radio Networks

doi: 10.1049/cje.2015.04.028

1.
Department of Electronic Engineering, Shanghai Jiaotong University, Shanghai 200240, China;
2.
Department of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China

Funds: This work is supported by the National High Technology Research and Development Program of China (863 Program) (No.2014AA01A701) and the National Natural Science Foundation of China (No.61172067, No.61371086).

Publish Date: 2015-04-10

Abstract

Abstract

In price-based cognitive radio networks, the Primary user (PU) can allow the Secondary users (SUs) to access by pricing if their interference power is under the Interference power constraint (IPC). The interaction between the PU and the SUs is modeled as a Stackelberg game with the consideration of the Quality of service (QoS) of the SUs. The revenue maximization problem of PU is expressed as an equivalent convex optimization problem if the minimum Signal-to-interference and noise ratio (SINR) constraints for the SUs are greater than or equal to 0dB. An optimal pricing algorithm is proposed based on this equivalent convex optimization problem. Simulation results show that the proposed pricing algorithm outperforms the non-uniform pricing algorithm in terms of the revenue of the PU, the sum rate of SUs and the number of admitted SUs.
- Power control,
- Cognitive radio,
- Price,
- Quality of service (QoS)

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References(15)

References

Y.C. Liang, K.C. Chen, G. Li, et al., “Cognitive radio networking and communications: An overview”, IEEE Transactions on Vehicular Technology, Vol.60, No.7, pp.3386-3407, 2011.

A. Rabbachin, T. Quek, H. Shin, et al., “Cognitive network interference”, IEEE Journal on Selected Areas in Communications, Vol.29, No.2, pp.480-493, 2011.

L.B. Le and E. Hossain, “Resource allocation for spectrum underlay in cognitive radio networks”, IEEE Transactions on Wireless Communications, Vol.7, No.12, pp.5306-5315, 2008.

R. Zhang, “On peak versus average interference power constraints for protecting primary users in cognitive radio networks”, IEEE Transactions on Wireless Communications, Vol.8, No.4, pp.2112-2120, 2009.

B. Wang, Y. Wu and K.J.R. Liu, “Game theory for cognitive radio networks: An overview”, Computer Networks, Vol.54, No.14, pp.2537-2561, 2010.

H. Yu, L. Gao, Z. Li, et al.,“Pricing for uplink power control in cognitive radio networks”, IEEE Transactions on Vehicular Technology, Vol.59, No.4, pp.1769-1778, 2010.

Y. Wu, T. Zhang and D. Tsang, “Joint pricing and power allocation for dynamic spectrum access networks with stackelberg game model”, IEEE Transactions on Wireless Communications, Vol.10, No.1, pp.12-19, 2011.

X. Kang, R. Zhang and M. Motani, “Price-based resource allocation for spectrum-sharing femtocell networks: A stackelberg game approach”, IEEE Journal on Selected Areas in Communications, Vol.30, No.3, pp.538-549, 2012.

Z.Q. Wang, L.G. Jiang and C. He, “A novel price-based power control algorithm in cognitive radio networks”, IEEE Communications Letters, Vol.17, No.1, pp.43-46, 2013.

Z.Q. Luo and W. Yu, “An introduction to convex optimization for communications and signal processing”, IEEE Journal on Selected Areas in Communications, Vol.24, No.8, pp.1426-1438, 2006.

H. Liang and X.H. Zhao, “Dynamic programming based power control algorithm with primary user QoS guarantee for cognitive radio networks”, Chinese Journal of Electronics, Vol.22 No.2, pp.353-358, 2013.

S. Boyd and L. Vandenberge, Convex Optimization, Cambridge University Press, New York, USA, 2004.

H.Y. Gu, C.Y. Yang and B. Fong, “Low-complexity centralized joint power and admission control in cognitive radio networks”, IEEE Communications Letters, Vol.13, No.6, pp.420-422, 2009.

J. Tadrous, A. Sultan and M. Nafie, “Admission and power control for spectrum sharing cognitive radio networks”, IEEE Transactions on Wireless Communications, Vol.10, No.6, pp.1945-1955, 2011.

T. Li, N.B. Mandayam and A. Reznik, “A framework for distributed resource allocation and admission control in a cognitive digital nome”, IEEE Transactions on Wireless Communications, Vol.12, No.3, pp.984-995, 2013.

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