J4 ›› 2010, Vol. 37 ›› Issue (2): 186-191.doi: 10.3969/j.issn.1001-2400.2010.02.002

• 研究论文 • 上一篇    下一篇

认知网络中快速自适应功率控制算法

李建东;薛富国;杨春刚;李维英;石华   

  1. (西安电子科技大学 综合业务网理论及关键技术国家重点实验室,陕西 西安  710071)
  • 收稿日期:2008-12-04 出版日期:2010-04-20 发布日期:2010-06-03
  • 通讯作者: 李建东
  • 作者简介:李建东(1962-),男,教授,博士,E-mail: jdli@mail.xidian.edu.cn.
  • 基金资助:

    国家杰出青年科学基金资助项目(60725105);国家973计划课题资助项目(2009CB320404);国家自然科学基金资助项目(60572146);高等学校博士学科点专项科研基金资助项目(20050701007);高等学校优秀青年教师教学科研奖励计划资助项目;教育部科学技术研究重点项目资助项目(107103);高等学校创新引智计划资助项目(B08038);“863”计划资助项目(2007AA01Z288)

Fast adaptive power control approach for cognitive radio networks

LI Jian-dong;XUE Fu-guo;YANG Chun-gang;LI Wei-ying;SHI Hua   

  1. (State Key Lab. of Integrated Service Networks, Xidian Univ., Xi'an  710071, China)
  • Received:2008-12-04 Online:2010-04-20 Published:2010-06-03
  • Contact: LI Jian-dong

摘要:

采用sigmoid函数设计了一种自适应效用函数,该函数仅与认知用户的信干噪比门限和当前获得的信干噪比相关,因此可以通过自适应改变信干噪比门限实现认知用户的机会频谱接入,并快速满足其服务质量要求.同时,兼顾用户公平性重新设计代价函数来改善纳什均衡功率解的帕累托有效性.在此基础上基于非合作博弈论提出一种功率控制模型,经证明该模型符合超模博弈,从理论上保证了纳什均衡解的存在性和惟一性.并提出一种自适应功率控制算法,仿真结果表明该算法可以保证约10次迭代收敛,与参考算法相比节省约8%的功耗,在获得的效用上有近15%改善.

关键词: 自适应效用函数, 认知无线电, 非合作博弈功率控制, 纳什均衡

Abstract:

An adaptive utility function based on the sigmoid function is proposed, which is related to the signal-to-interference plus noise (SINR) threshold and the obtained SINR, so that it will help to implement the opportunistic spectrum access by adaptively adjusting the SINR threshold, and it will satisfy the QoS requirement of multiple secondary users (SUs) fast. Meanwhile, considering the fairness among SUs, we rebuilt the pricing function to improve the Pareto optimality of the Nash equilibrium solution (NES). Based on all this, a power control model is investigated from the perspective of the non-cooperative game theory. The model is proved to be the super-modular game, and it can guarantee the existence and uniqueness of the NES. Simulation results show that the proposed power control algorithm can lead to converfence after 10 iterations, and that compared to the referenced algorithm it can save 10% power consumption and achieve mostly 15% improvement on the final utility.

Key words: adaptive utility function, cognitive radio, non-cooperative game power control, Nash equilibrium