基于无人机认知网络的上行链路波束成形算法  

Beamforming Algorithm for the Uplink of UAV-Based Cognitive Networks

作　　者：李艳丽 王子宁 徐启钊 张健[1] 陈爱庆 淡振雷 Li Yan-li;Wang Zi-ning;Xu Qi-zhao;Zhang Jian;Chen Ai-qing;Dan Zhen-lei(College of Telecommunications and Information Engineering,Nanjing University of Posts and Telecommunications,Jiangsu Nanjing 210003;Bell Honors School,Nanjing University of Posts and Telecommunications,Jiangsu Nanjing 210023)

机构地区：[1]南京邮电大学通信与信息工程学院,江苏南京210003 [2]南京邮电大学贝尔英才学院,江苏南京210023

出　　处：《电子质量》2020年第12期18-21,24,共5页Electronics Quality

基　　金：江苏省研究生科研实践创新计划项目(SJCX19_0237)资助课题。

摘　　要：针对认知(Cognitive radio, CR)网络中无人机(Unmanned Aerial Vehicle, UAV)通信系统上行链路受到多个卫星地面终端干扰的场景,该文在已知干扰准确位置和大概位置的条件下,分别提出了两种抗干扰波束成形(Beamforming, BF)算法。具体而言,首先,在干扰位置准确已知条件下,建立了目标用户信号功率最大化和干扰功率为零的优化问题,并利用拉格朗日乘子法得到接收权矢量的闭式解。其次,在仅已知干扰大概位置的条件下,建立了以目标用户信号功率最大化为目标和干扰功率小于门限值为约束的优化问题,利用角度离散化和凸优化求解接收权矢量。最后,仿真结果表明,该文所提两种算法均能一定程度地减缓共存网络之间的干扰,从而有效地提升系统的抗干扰性能。Considering the uplink of a UAV-based cognitive communication system interfered by multiple satellite earth terminals this paper proposes two anti-jamming beamforming algorithms under the conditions of known exact and approximate location of interference.First,under the condition that the interference location is accurately known,the optimization problem of maximizing the signal power of the target user and zero interference power is established,and the Lagrangian multiplier method is used to obtain the closed-form solution of the receiving weight vector.Secondly,under the condition that only the approximate position of the interference is known,an optimization problem is established with the goal of maximizing the signal power of the target user and the interference power being less than the threshold as the constraint,and the receiving weight vector is solved by angle discretization and convex optimization.Finally,the simulation results show that the two algorithms proposed in this paper can alleviate the interference between coexisting networks to a certain extent,thereby effectively improving the anti-interference performance of the system.

关 键 词：无人机认知网络 角度离散化 波束成形 抗干扰 

分 类 号：V279[航空宇航科学与技术—飞行器设计]