认知无线电网络中基于随机学习博弈的信道分配与功率控制

doi:10.3969/j.issn.0372-2112.2018.12.008

电子学报 ›› 2018, Vol. 46 ›› Issue (12): 2870-2877.DOI: 10.3969/j.issn.0372-2112.2018.12.008

认知无线电网络中基于随机学习博弈的信道分配与功率控制

汪志勇^1,2, 张沪寅¹, 徐宁¹, 郝圣¹

1. 武汉大学计算机学院, 湖北武汉 430072;
2. 湖北科技学院计算机科学与技术学院, 湖北咸宁 437100

收稿日期:2017-06-15 修回日期:2018-04-15 出版日期:2018-12-25
- 通讯作者:
- 张沪寅
- 作者简介:
- 汪志勇 男,1978年出生于湖北武汉,博士研究生,主要研究方向为认知无线电网络、上下文认知计算.E-mail:zywang_whu@whu.edu.cn
- 基金资助:
- 国家自然科学基金 (No.61772386）; 广东省科技计划项目 (No.2015B010131007）

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

WANG Zhi-yong^1,2, ZHANG Hu-yin¹, XU Ning¹, HAO Sheng¹

1.School of Computer, Wuhan University, Wuhan, Hubei 430072, China;
2.College of Computer Science and Technology, Hubei University of Science and Technology, Xianning, Hubei 437100, China

Received:2017-06-15 Revised:2018-04-15 Online:2018-12-25 Published:2018-12-25
- Corresponding author:
- ZHANG Hu-yin
- Supported by:
- National Natural Science Foundation of China (No.61772386); Science and Technology Project of Guangdong Province (No.2015B010131007)

摘要/Abstract

摘要： 传统的认知无线电频谱分配算法往往忽略节点的传输功率对网络干扰的影响，且存在节点间交互成本高的问题.为此，通过量化传输功率等级，以最大化弹性用户收益为目标，构建联合频谱分配与功率控制非合作博弈模型，证明了该博弈为严格潜在博弈且收敛到纳什均衡点.进一步，将随机学习理论引入博弈模型，提出了基于随机学习的策略选择算法，并给出了该算法收敛到纯策略纳什均衡点的充分条件及严格证明.仿真结果表明，所提算法在少量信息交互前提下能获得较高的传输速率，并提升用户满意度.

关键词: 认知无线电网络, 随机学习, 博弈论, 信道分配, 功率控制

Abstract: 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.

Key words: cognitive radio networks, stochastic learning, game theory, channel assignment, power control

中图分类号:

TP393

汪志勇, 张沪寅, 徐宁, 等. 认知无线电网络中基于随机学习博弈的信道分配与功率控制[J]. 电子学报, 2018, 46(12): 2870-2877.

WANG Zhi-yong, ZHANG Hu-yin, XU Ning, et al. Channel Assignment and Power Control Based on Stochastic Learning Game in Cognitive Radio Networks[J]. Acta Electronica Sinica, 2018, 46(12): 2870-2877.

参考文献

[1] Lai J,Dutkiewicz E,Liu R P,et al.Opportunistic spectrum access with two channel sensing in cognitive radio networks[J].IEEE Transactions on Mobile Computing,2015,14(1):126-138.
[2] Audhya G K,Sinha K,Ghosh S C,et al.A survey on the channel assignment problem in wireless networks[J].Wireless Communications & Mobile Computing,2011,11(5):583-609.
[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[J].Ad Hoc Networks,2015,27(C):68-80.
[4] 贾杰,李燕燕,等.认知无线网状网中基于差分演化的功率控制与信道分配[J].电子学报,2013,41(1):62-67. Jia J,Li Y,et al.Channel allocation and power control based on differential evolution algorithm in cognitive radio mesh network[J].Acta Electronica Sinica,2013,41(1):62-67.(in Chinese)
[5] Wang X,Huang L,Leng B,et al.Joint channel and sink assignment for data collection in cognitive wireless sensor networks[J].International Journal of Communication Systems,2017,30(5):1-15.
[6] Jia J,Wang X,Chen J.A genetic approach on cross-layer optimization for cognitive radio wireless mesh network under SINR model[J].Ad Hoc Networks,2015,27(C):57-67.
[7] Haddad M,Hayel Y,Habachi O.Spectrum coordination in energy efficient cognitive radio networks[J].IEEE Transactions on Vehicular Technology,2012,64(5):2112-2122.
[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[J].IEEE Journal on Selected Areas in Communications,2012,30(1):119-127.
[9] Zheng J,Cai Y,Xu Y,et al.Distributed channel selection for interference mitigation in dynamic environment:a game-theoretic stochastic learning solution[J].IEEE Transactions on Vehicular Technology,2014,63(9):4757-4762.
[10] Gao Z,Chen J,Xu Y.Opportunistic spectrum access with discrete feedback in unknown and dynamic environment[J].KSⅡ Transactions on Internet & Information Systems,2015,9(10):3867-3886.
[11] Tseng L C,Chien F T,Chang R Y,et al.Distributed channel assignment for network MIMO:game-theoretic formulation and stochastic learning[J].Wireless Networks,2015,21(4):1-16.
[12] Wu Q,Wu D,Xu Y,Wang J.Demand-aware multichannel opportunistic spectrum access:a local interaction game approach with reduced information exchange[J].IEEE Transactions on Vehicular Technology,2015,64(10):4899-4904.
[13] 徐宁,张沪寅,等.认知Ad Hoc网络中基于信道相似度的分簇算法研究[J].电子学报,2016,44(10):2323-2329. Xu N,Zhang H,et al.Channel similarity based clustering algorithm in cognitive Ad Hoc network[J].Acta Electronica Sinica,2016,44(10):2323-2329.(in Chinese)
[14] Babadi B,Tarokh V.GADIA:A greedy asynchronous distributed interference avoidance algorithm[J].Transactions on Information Theory IEEE,2011,56(12):6228-6252.
[15] Hu S,Yao Y D,Yang Z.MAC protocol identification using support vector machines for cognitive radio networks[J].IEEE Wireless Communications,2014,21(1):52-60.
[16] Noroozoliaee M J,Hamdaoui B,Tumer K.Efficient objective functions for coordinated learning in large-scale distributed OSA systems[J].IEEE Transactions on Mobile Computing,2013,12(5):931-944.
[17] Sastry P S,Phansalkar V V,Thathachar M.Decentralized learning of Nash equilibria in multi-person stochastic games with incomplete information[J].IEEE Transactions on Systems Man & Cybernetics,1994,24(5):769-777.

认知无线电网络中基于随机学习博弈的信道分配与功率控制

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

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