Price-Based Power Control Algorithm in Cognitive Radio Networks Based on Monotone Optimization

doi:10.1007/s12204-015-1673-0

Journal of Shanghai Jiao Tong University(Science) ›› 2015, Vol. 20 ›› Issue (6): 654-659.doi: 10.1007/s12204-015-1673-0

• Research article • Previous Articles Next Articles

Price-Based Power Control Algorithm in Cognitive Radio Networks Based on Monotone Optimization

Zheng-qiang WANG^1,^*(), Ling-ge2 JIANG², Chen HE²

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

Received:2014-03-17 Online:2015-12-20 Published:2020-10-09
Contact: Zheng-qiang WANG E-mail:wangzq@cqupt.edu.cn
Supported by:
The National Natural Science Foundation of China (Nos.61172067 and 61371086);The National High Technology Research and Development Program (863) of China (No.2014AA01A701)

Abstract

Abstract:

This paper considers a price-based power control problem in the cognitive radio networks (CRNs). The primary user (PU) can admit secondary users (SUs) to access if their interference powers are all under the interference power constraint. In order to access the spectrum, the SUs need to pay for their interference power. The PU first decides the price for each SU to maximize its revenue. Then, each SU controls its transmit power to maximize its revenue based on a non-cooperative game. The interaction between the PU and the SUs is modeled as a Stackelberg game. Using the backward induction, a revenue function of the PU is expressed as a non-convex function of the transmit power of the SUs. To find the optimal price for the PU, we rewrite the revenue maximization problem of the PU as a monotone optimization by variable substitution. Based on the monotone optimization, a novel price-based power control algorithm is proposed. Simulation results show the convergence and the effectiveness of the proposed algorithm compared to the non-uniform pricing algorithm.

Key words: power control|cognitive radio|monotone optimization|price

CLC Number:

TN 929.53

Zheng-qiang WANG, Ling-ge2 JIANG, Chen HE. Price-Based Power Control Algorithm in Cognitive Radio Networks Based on Monotone Optimization[J]. Journal of Shanghai Jiao Tong University(Science), 2015, 20(6): 654-659.

References 13

[1]	Federal Communications Commission. Spectrum policy task force report, 02-155 [R]. Washington DC, USA: Federal Communications Commission, 2002.
[2]	Liang Y C, Chen K C, Li G Y,et al.Cognitive radio networking and communications: An overview[J]. IEEE Transactions on Vehicular Technology, 2011, 60(7): 3386-3407.
[3]	Zhao Q, Sadler B M.A survey of dynamic spectrum access[J]. IEEE Signal Processing Magazine, 2007, 24: 79-89.
[4]	Y$\"{u}$cek T, Arslan H.A survey of spectrum sensing algorithms for cognitive radio applications[J]. IEEE Communications Surveys & Tutorials, 2009, 11(1): 116-130.
[5]	Haykin S.Cognitive radio: Brain-empowered wireless communications[J]. IEEE Journal on Selected Areas in Communications, 2005, 23(2): 201-220.
[6]	Wang F, Krunz M, Cui S G.Price-based spectrum management in cognitive radio networks[J]. IEEE Journal of Selected Topics in Signal Processing, 2008, 2(1): 74-87.
[7]	Yu H, Gao L, Li Z,et al.Pricing for uplink power control in cognitive radio networks[J]. IEEE Transactions on Vehicular Technology, 2010, 59(4): 1769-1778.
[8]	Wu Y, Zhang T, Tsang D H K. Joint pricing and power allocation for dynamic spectrum access networks with Stackelberg game model[J]. IEEE Transactions on Wireless Communications, 2011, 10(1): 12-19.
[9]	Wang Z Q, Jiang L G, He C.A novel price-based power control algorithm in cognitive radio networks[J]. IEEE Communications Letters, 2013, 17(1): 43-46.
[10]	Kang X, Zhang R, Motani M.Price-based resource allocation for spectrum-sharing femtocell networks: A Stackelberg game approach[J]. IEEE Journal on Selected Areas in Communications, 2012, 30(3): 538-549.
[11]	Fudenberg D, Tirole J.Game theory [M]. Cambridge England: The MIT Press, 1993.
[12]	Boyd S, Vandenberghe L.Convex optimization [M]. Cambridge England: Cambridge University Press, 2003.
[13]	Tuy H.Monotonic optimization: Problems and solution approaches[J]. SIAM Journal on Optimization, 2000, 11(2): 464-494.

[1]	YU Xinyi (禹鑫燚), WU Jiaxin (吴加鑫), XU Chengjun (许成军), LUO Huizhen (罗惠珍), OU Linlin∗ (欧林林). Adaptive Human-Robot Collaboration Control Based on Optimal Admittance Parameters [J]. J Shanghai Jiaotong Univ Sci, 2022, 27(5): 589-601.
[2]	YU Hao, ZHANG Zhemeng, PENG Sui, ZHANG Zhiqiang, REN Wanxin, LI Canbing. Comparative Analysis of Technical Standards for Offshore Wind Power via VSC-HVDC [J]. Journal of Shanghai Jiao Tong University, 2022, 56(4): 403-412.
[3]	ZHANG Liang, QU Gang, LI Huixing, JIN Haochun. Research and Application of Key Technologies of Network Security Situation Awareness for Smart Grid Power Control Systems [J]. Journal of Shanghai Jiao Tong University, 2021, 55(S2): 103-109.
[4]	XU Haiyu, LUO Kai, HUANG Chuang, ZUO Zhenhao, GU Jianxiao. Variation Characteristics of Formation and Development of Ventilated Supercavity at Low Froude Numbers [J]. Journal of Shanghai Jiao Tong University, 2021, 55(8): 934-941.
[5]	YAO Shengjian, YAN Guozheng, WANG Zhiwu, ZHOU Zerun, JIANG Pingping, DING Zifan, HUA Fangfang, ZHAO Kai, HAN Ding. Low-Power Consumption Design and Optimization of New Artificial Anal Sphincter [J]. Journal of Shanghai Jiao Tong University, 2021, 55(7): 899-906.
[6]	HE Jinhui, LI Mingguang, CHEN Jinjian, XIA Xiaohe. Particle-Fluid Coupling Algorithm Considering Dynamic Fluid Mesh [J]. Journal of Shanghai Jiao Tong University, 2021, 55(6): 645-651.
[7]	CHEN Feier (陈飞儿), ZHAO Qiyuan (赵祺源), CAO Mingming (曹明明), CHEN Jiayi (陈嘉屹), FU Guiyuan (傅桂元). Adaptive Agent-Based Modeling Framework for Collective Decision-Making in Crowd Building Evacuation [J]. J Shanghai Jiaotong Univ Sci, 2021, 26(4): 522-533.
[8]	WU Lihong, FENG Xisheng, YE Zuolin, LI Yiping. Physics-Based Simulation of AUV Forced Diving by Self-Propulsion [J]. Journal of Shanghai Jiao Tong University, 2021, 55(3): 290-296.
[9]	TAN Jia, LI Zhiyi, YANG Huan, ZHAO Rongxiang, JU Ping. A Multi-Level Collaborative Load Forecasting Method for Distribution Networks Based on Distributed Optimization [J]. Journal of Shanghai Jiao Tong University, 2021, 55(12): 1544-1553.
[10]	LÜ Xiangmei, LIU Tianqi, LIU Xuan, HE Chuan, NAN Lu, ZENG Hong. Low-Carbon Economic Dispatch of Multi-Energy Park Considering High Proportion of Renewable Energy [J]. Journal of Shanghai Jiao Tong University, 2021, 55(12): 1586-1597.
[11]	WEI Lishen, FENG Yuang, FANG Jiakun, AI Xiaomeng, WEN Jinyu. Impact of Renewable Energy Integration on Market-Clearing Results in Spot Market Environment [J]. Journal of Shanghai Jiao Tong University, 2021, 55(12): 1631-1639.
[12]	HUANG Ningning (黄宁宁), MA Yixin (马艺馨), ZHANG Mingzhu (张明珠), GE Hao (葛浩), WU Huawei (吴华伟). Finite Element Modeling of Human Thorax Based on MRI Images for EIT Image Reconstruction [J]. J Shanghai Jiaotong Univ Sci, 2021, 26(1): 33-39.
[13]	CHEN Longsheng, WANG Qi, HE Guoyi. Adaptive Control of Non-Affine Pure Feedback Nonlinear Switching Systems [J]. Journal of Shanghai Jiaotong University, 2020, 54(9): 981-986.
[14]	MEI Hantong, MA Lijuan, WU Guanghui, SHAO Xiang, XU Pengya. Adaptive Backstepping Attitude Control System Design of Long-range Missile [J]. Air & Space Defense, 2020, 3(3): 118-123.
[15]	ZHAO Ting, WANG Shentao, NIU Lin, XI Peili, CAI Yunze. Detection Algorithm of Ship Wake in SAR Images [J]. Journal of Shanghai Jiao Tong University, 2020, 54(12): 1259-1268.

Price-Based Power Control Algorithm in Cognitive Radio Networks Based on Monotone Optimization

RichHTML

Knowledge

Abstract

Cite this article

share this article

References 13

Related Articles 15

Recommended Articles

Metrics

Comments