基于多天线波束赋形的CRN分布式上行功率控制算法

针对工作于underlay模式的认知无线网络（CRN，Cognitive Radio Network）上行功率控制问题，本文提出一种基于多天线波束赋形，由认知基站和认知用户联合优化的分布式上行功率控制算法.联合优化的具体步骤为认知基站通过求解最大广义特征值问题完成多天线波束赋形优化；认知用户先将非线性功率优化问题转换为几何规划凸优化问题，再使用梯度法完成分布式发送功率优化；认知基站和认知用户交替优化，实现网络效用最大化.数值仿真显示，同只优化认知用户功率的上行功率控制算法相比，认知基站和认知用户联合优化的上行功率控制算法不仅能得到更大的网络效用值，而且对主用户的干扰具有鲁棒性.

CRN Distributed Uplink Power Control Algorithm with Multi-antenna Beamforming

To solve the uplink power control problem in underlay cognitive radio network (CRN),this paper proposes a distributed uplink power control algorithm based on multi-antenna beamforming,which is jointly optimized by cognitive base station and secondary users. The specific steps of joint optimization are as follows.Cognitive base station accomplishes multi-antenna beamforming optimization by solving the maximum generalized eigenvalue problem.Secondary users firstly transform the nonlinear power optimization problem into the geometric programming convex optimization problem,then implement the distributed transmitting power optimization through gradient method.The maximum network utility is realized via the alternating optimization between cognitive base station and secondary users.Numerical simulation results show that compared with the existing uplink power control algorithm which only optimizes the powers of secondary users,the proposed algorithm can not only obtain larger network utility value but also be robust to the effect of the interferences from the primary users.