认知无线电网络中接入控制和功率波束形成的联合优化

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

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L_0-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L_0-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。

In cognitive radio networks, for the robust joint optimization problem of muhiuser 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.