A stochastic game framework for joint frequency and power allocation in dynamic decentralized cognitive radio networks

被引：12
作者：
Liu, Xiu [1 ,2 ]
Ding, Guoru [1 ]
Yang, Yang [1 ]
Wu, Qihui [1 ]
Wang, Jinlong [1 ]
机构：
[1] PLA Univ Sci & Technol, Coll Commun Engn, Nanjing 210007, Jiangsu, Peoples R China
[2] Guangzhou Mil Area, Comprehens Training Base, Guilin 541002, Guangxi, Peoples R China
来源：
AEU-INTERNATIONAL JOURNAL OF ELECTRONICS AND COMMUNICATIONS | 2013年 / 67卷 / 10期
基金：
美国国家科学基金会;
关键词：
Cognitive radio networks; Frequency allocation; Power control; Stochastic game; Multi-agent learning; OPPORTUNISTIC SPECTRUM ACCESS; WIRELESS MESH NETWORKS; AD-HOC NETWORKS; THEORETIC APPROACH; ENVIRONMENT; CHANNEL;
D O I：
10.1016/j.aeue.2013.04.002
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
Cognitive radio networks (CRNs) have been recognized as a promising solution to improve the radio spectrum utilization. This article investigates a novel issue of joint frequency and power allocation in decentralized CRNs with dynamic or time-varying spectrum resources. We firstly model the interactions between decentralized cognitive radio links as a stochastic game and then proposed a strategy learning algorithm which effectively integrates multi-agent frequency strategy learning and power pricing. The convergence of the proposed algorithm to Nash equilibrium is proofed theoretically. Simulation results demonstrate that the throughput performance of the proposed algorithm is very close to that of the centralized optimal learning algorithm, while the proposed algorithm could be implemented distributively and reduce information exchanges significantly. (C) 2013 Elsevier GmbH. All rights reserved.
引用
收藏
页码：817 / 826
页数：10
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