基于无人机中继的星地认知网络波束成形算法

doi:10.11959/j.issn.1000-0801.2021202

电信科学 ›› 2021, Vol. 37 ›› Issue (8): 27-37.doi: 10.11959/j.issn.1000-0801.2021202

基于无人机中继的星地认知网络波束成形算法

李艳丽¹, 林志², 王子宁¹, 许拔³, 程铭¹, 欧阳键¹

¹ 南京邮电大学通信与信息工程学院，江苏南京 210003
² 国防科技大学电子对抗学院，安徽合肥 230037
³ 国防科技大学六十三研究所, 江苏南京 210003

修回日期:2021-08-15 出版日期:2021-08-20 发布日期:2021-08-01
作者简介:李艳丽（1996− ），女，南京邮电大学硕士生，主要研究方向为无线通信、通信信号处理
林志（1992− ），男，国防科技大学讲师，主要研究方向为卫星通信、阵列信号处理、凸优化理论等
王子宁（1997− ），男，南京邮电大学博士生，主要研究方向为通信信号处理、无线通信
许拔（1981− ），男，国防科技大学六十三研究所高级工程师，主要研究方向为通信抗干扰技术、通信信号处理技术
程铭（1991− ），男，南京邮电大学讲师，主要研究方向为毫米波通信、大规模MIMO和智能反射面应用
欧阳键（1983− ），男，南京邮电大学副教授，主要研究方向为无人机通信、物理层安全
基金资助:
国家自然科学基金资助项目(61801234);复杂电子系统仿真实验室基础研究课题(DXZT-JC-ZZ-2019-009)

Beamforming algorithm for cognitive satellite and terrestrial network based on UAV relay

Yanli LI¹, Zhi LIN², Zining WANG¹, Ba XU³, Ming CHENG¹, Jian OUYANG¹

¹ College of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
² Institute of Electronic Countermeasure, National University of Defense Technology, Hefei 230037, China
³ 63 Institute of National University of Defense Technology, Nanjing 210003, China

Revised:2021-08-15 Online:2021-08-20 Published:2021-08-01
Supported by:
The National Natural Science Foundation of China(61801234);Research Project of Complex Electronic Sys-tem Simulation Laboratory(DXZT-JC-ZZ-2019-009)

摘要/Abstract

摘要：

针对基于无人机中继的星地认知网络，提出了两种波束成形（beamforming, BF）算法，通过对各种干扰进行抑制，实现系统间的频谱共享。具体而言，在基于无人机中继的卫星网络作为次级网络、地面网络作为主网络的情况下，以无人机最大发射功率和主用户所受干扰为约束条件，建立次级用户信干噪比最大化准则的优化问题；接下来在已知次级用户统计信道状态信息的条件下，提出一种基于迭代的BF算法对优化问题进行求解；更进一步，为了降低迭代算法的实现复杂度，提出了一种基于迫零的BF算法。最后，计算机仿真验证了所提两种波束成形方案的正确性与有效性。

关键词: 星地认知网络, 统计信道状态信息, 波束成形, 迫零

Abstract:

Two beamforming (BF) schemes to achieve spectrum sharing by suppressing inter-system interferences in cognitive satellite and terrestrial networks were proposed, where the satellite and UAV cooperative network was used as the secondary network, while the terrestrial network was used as the primary network.Specifically, considering that only the statistical channel state information was available, a constrained optimization problem was formulated to maximize the signal-to- interference-plus-noise ratio of the secondary user under the constraints of the maximum transmit power at the UAV and the interference power of the primary user.Then, an iteration-based BF scheme was proposed to solve the constrained optimization problem.To reduce the computational complexity of the iterative algorithm, a zero-forcing based BF scheme was further proposed.Finally, computer simulations verify the correctness and effectiveness of the proposed BF schemes.

Key words: cognitive satellite and terrestrial network, statistical channel state information, beamforming, ze-ro-forcing

中图分类号:

TN925

李艳丽, 林志, 王子宁, 许拔, 程铭, 欧阳键. 基于无人机中继的星地认知网络波束成形算法[J]. 电信科学, 2021, 37(8): 27-37.

Yanli LI, Zhi LIN, Zining WANG, Ba XU, Ming CHENG, Jian OUYANG. Beamforming algorithm for cognitive satellite and terrestrial network based on UAV relay[J]. Telecommunications Science, 2021, 37(8): 27-37.

图/表 6

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参数	取值
干扰数	3
中继天线数	8
天线间距离	0.5λ
工作频率f/GHz	18
无人机距离地面距离/m	500
中继站发射功率最大值/ dB	3
主用户干扰功率门限值/dB	-10

基于无人机中继的星地认知网络波束成形算法

Beamforming algorithm for cognitive satellite and terrestrial network based on UAV relay

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