Channel Assignment and Power Control Based on Stochastic Learning Game in Cognitive Radio Networks

被引：0

作者：

Wang Z.-Y. ^{[1
,2
]}

Zhang H.-Y. ^{[1
]}

Xu N. ^{[1
]}

Hao S. ^{[1
]}

机构：

[1] School of Computer, Wuhan University, Wuhan, 430072, Hubei

[2] College of Computer Science and Technology, Hubei University of Science and Technology, Xianning, 437100, Hubei

来源：

Zhang, Hu-Yin (zhy2536@whu.edu.cn) | 2018年 / Chinese Institute of Electronics卷 / 46期

关键词：

Channel assignment; Cognitive radio networks; Game theory; Power control; Stochastic learning;

D O I：

10.3969/j.issn.0372-2112.2018.12.008

中图分类号：

学科分类号：

摘要：

Traditional cognitive radio spectrum allocation algorithms tend to ignore the influence of transmission power on network interference and have the drawback of high interaction cost between nodes.In response to these problems, by quantifying transmission power levels, we formulate the channel assignment and power control problem as a distributed non-cooperative game, in which each second user's purpose is to maximize the elastic traffic rewards.Formally, the formulated game is proved to be an exact potential game and converges to Nash equilibrium (NE) point.Furthermore, introducing the stochastic learning theory into game model, we propose a strategy selection algorithm based on stochastic learning, then the sufficient condition and strict proof for the convergence of this algorithm to pure strategy NE point are given.Finally, Simulation results show that the proposed algorithm can achieve high system throughput and improve users' satisfaction with a small amount of interactions. © 2018, Chinese Institute of Electronics. All right reserved.

引用

页码：2870 / 2877

页数：7

共 17 条

[1]

Lai J., Dutkiewicz E., Liu R.P., Et al., Opportunistic spectrum access with two channel sensing in cognitive radio networks, IEEE Transactions on Mobile Computing, 14, 1, pp. 126-138, (2015)

[2]

Audhya G.K., Sinha K., Ghosh S.C., Et al., A survey on the channel assignment problem in wireless networks, Wireless Communications & Mobile Computing, 11, 5, pp. 583-609, (2011)

[3]

Chaudhry A.U., Hafez R.H.M., Chinneck J.W., On the impact of interference models on channel assignment in multi-radio multi-channel wireless mesh networks, Ad Hoc Networks, 27, C, pp. 68-80, (2015)

[4]

Jia J., Li Y., Et al., Channel allocation and power control based on differential evolution algorithm in cognitive radio mesh network, Acta Electronica Sinica, 41, 1, pp. 62-67, (2013)

[5]

Wang X., Huang L., Leng B., Et al., Joint channel and sink assignment for data collection in cognitive wireless sensor networks, International Journal of Communication Systems, 30, 5, pp. 1-15, (2017)

[6]

Jia J., Wang X., Chen J., A genetic approach on cross-layer optimization for cognitive radio wireless mesh network under SINR model, Ad Hoc Networks, 27, C, pp. 57-67, (2015)

[7]

Haddad M., Hayel Y., Habachi O., Spectrum coordination in energy efficient cognitive radio networks, IEEE Transactions on Vehicular Technology, 64, 5, pp. 2112-2122, (2012)

[8]

Duarte P.B.F., Fadlullah Z.M., Vasilakos A.V., Et al., On the partially overlapped channel assignment on wireless mesh network backbone: a game theoretic approach, IEEE Journal on Selected Areas in Communications, 30, 1, pp. 119-127, (2012)

[9]

Zheng J., Cai Y., Xu Y., Et al., Distributed channel selection for interference mitigation in dynamic environment: a game-theoretic stochastic learning solution, IEEE Transactions on Vehicular Technology, 63, 9, pp. 4757-4762, (2014)

[10]

Gao Z., Chen J., Xu Y., Opportunistic spectrum access with discrete feedback in unknown and dynamic environment, KSII Transactions on Internet & Information Systems, 9, 10, pp. 3867-3886, (2015)

← 1 2 →