Journal of Computer Applications ›› 2017, Vol. 37 ›› Issue (7): 1830-1836.DOI: 10.11772/j.issn.1001-9081.2017.07.1830

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Joint optimization of admission control and power beamforming algorithm in cognitive radio network

ZHU Jiang, DU Qingmin, BA Shaowei   

  1. Chongqing Key Laboratory of Mobile Communications Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
  • Received:2016-12-14 Revised:2017-03-08 Online:2017-07-10 Published:2017-07-18
  • Supported by:
    This work is partially supported by the National Natural Science Foundation of China (61102062, 61271260), the Natural Science Foundation of Chongqing Science and Technology Commission (cstc2015jcyjA40050).


朱江, 杜清敏, 巴少为   

  1. 重庆邮电大学 重庆市移动通信重点实验室, 重庆 400065
  • 通讯作者: 杜清敏
  • 作者简介:朱江(1977-),男,湖北荆州人,副教授,博士,主要研究方向:通信理论与技术、信息安全;杜清敏(1990-),女,河北石家庄人,硕士研究生,主要研究方向:认知无线电;巴少为(1991-),女,湖北天门人,硕士研究生,主要研究方向:认知无线电。
  • 基金资助:

Abstract: In cognitive radio networks, for the robust joint optimization problem of multiuser admission control and power beamforming, a joint optimization scheme based on smooth approximation of entropy function was proposed. Firstly, the two optimization problems of admission control and transmit power beams were converted into a joint optimization problem by L0-norm minimization. Secondly, the method of smoothing approximation based on entropy function was used to optimize the non-convexity and discontinuity of L0-norm. Finally, since the objective function was smooth, differentiable and unimodal function, the problem was transformed into the Lagrange function, and Armijo gradient descent method was used to get the optimal solution. The numerical results show that by using the proposed algorithm, the number of admitted uses is not significantly increased when the Signal-to-Interference-plus-Noise Ratio (SINR) is relatively low, but the transmission power consumption is decreased and the number of admitted uses is increased when SINR is relatively high. The uncertain Channel State Information (CSI) of model is analyzed, which can make the network better adapt to the changes of the outside world and improve the reliability of the network. The proposed algorithm can effectively realize the optimal allocation of the network resources and improve the network performance.

Key words: cognitive radio network, admission control, transmission power beamforming, augmented Lagrange function, Armijo gradient descent method

摘要: 在认知无线电网络中,针对鲁棒性的多用户接入控制和发射功率波束形成的联合优化问题,提出了基于熵函数光滑近似的联合优化方案。首先,利用L0-范数最小化将接入控制和发射功率波束两个优化问题转化为一个联合优化问题;然后,利用基于熵函数光滑近似的方法对L0-范数的非凸性及不连续性问题加以优化;最后,由于光滑可微的目标函数为单峰函数,将问题变形为增广Lagrange函数,利用Armijo梯度下降法得到问题的最优解。数值结果分析表明:新算法在信干噪比(SINR)较低时虽然所提算法的接入量无明显提高,但是在SINR较高时所提算法能显著降低发射功率并提高次用户的接入量。模型中对不确定的信道状态信息(CSI)加以分析,可以使网络更好地适应外界的变化,提高网络的可靠性,所提算法可以有效地实现网络资源的优化配置,提高网络性能。

关键词: 认知无线电网络, 接入控制, 发射功率波束形成, 增广Lagrange函数, Armijo梯度下降法

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