摘要
研究了认知MIMO无线网络中基于博弈论的动态频谱接入技术,以使得具有不同风险偏好的次级用户(买家)可以动态地选择竞标策略,同时主用户也可以根据系统情况,自适应地调整拍卖机制。基于非合作博弈设计了一个有限离散博弈模型,该博弈至少有一个混合策略的纳什均衡。基于自动学习机的概念,设计了一个有限反馈的分布式随机学习算法。仿真结果表明,所设计的算法具有良好的性能,与传统的固定竞拍机制和随机的选择竞价策略相比,该算法能够帮助主用户获得更高的利润,且让次级用户根据自身的风险偏好,选择一个合理的竞拍策略。
A game theoretic framework of the auction based dynamic spectrum access for multi-type buyers ( i. e. , the secondary users that have different risk preferences) in cognitive MIMO networks was presented in this paper where the auction mechanisms of the primary user are parameterized and can be adaptively designed. The proposed game is a discrete game which must have at least one mixed or pure strategy Nash equilibrium. Based on the con-cept of learning automata, a distributed algorithm was proposed to learn the Nash equilibrium of the proposed game with limited feedback. Simulation results show that our proposed algorithm is efficient and can achieve much higher performance than the traditional fixed auction mechanism schemes and the random algorithm.
出处
《解放军理工大学学报(自然科学版)》
EI
北大核心
2014年第1期1-6,共6页
Journal of PLA University of Science and Technology(Natural Science Edition)
基金
国家自然科学基金青年基金资助项目(61201218)
江苏省自然科学基金青年基金资助项目(BK2012056)
关键词
认知MIMO
动态频谱接入
拍卖机制
学习自动机
cognitive MIMO radio
dynamic spectrum access
auction mechanism
learning automata