基于多天线波束赋形的CRN分布式上行功率控制算法

doi:10.3969/j.issn.0372-2112.2019.12.004

电子学报 ›› 2019, Vol. 47 ›› Issue (12): 2472-2479.DOI: 10.3969/j.issn.0372-2112.2019.12.004

基于多天线波束赋形的CRN分布式上行功率控制算法

季中恒, 季新生, 黄开枝, 陈亚军

国家数字交换系统工程技术研究中心, 河南郑州 450002

收稿日期:2018-05-22 修回日期:2019-01-03 出版日期:2019-12-25
- 作者简介:
- 季中恒 男,1971年9月出生,江苏丹阳人.博士、副研究员,主要研究方向为无线通信理论及技术应用.E-mail:jzh719@163.com;季新生 男,1968年6月出生,江苏南通人.教授、博导,主要研究方向为移动通信网络、拟态安全等.现任"十三五"国家重点研发计划网络空间安全重点专项专家.E-mail:jxs@ndsc.com.cn;黄开枝 女,1973年9月出生,安徽滁州人.教授、博导,主要研究方向为移动通信网络和物理层安全.;陈亚军 男,1988年12月出生,河南商丘人.博士、助理研究员,主要研究方向为无线物理层安全、D2D通信技术和无线定位技术.
- 基金资助:
- 国家自然科学基金 (No.61471396，No.61521003）; 国家重点研发计划 (No.2017YFB0801903）

CRN Distributed Uplink Power Control Algorithm with Multi-antenna Beamforming

JI Zhong-heng, JI Xin-sheng, HUANG Kai-zhi, CHEN Ya-jun

National Digital Switching Systems Engineering & Technological Research Center, Zhengzhou, Henan 450002, China

Received:2018-05-22 Revised:2019-01-03 Online:2019-12-25 Published:2019-12-25
- Supported by:
- National Natural Science Foundation of China (No.61471396, No.61521003); National Key Research and Development Program of China (No.2017YFB0801903)

摘要/Abstract

摘要： 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.

关键词: 认知无线网络, 上行功率控制, 多天线波束赋形, 联合优化, 网络效用

Abstract: 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.

Key words: cognitive radio network, uplink power control, multi-antenna beamforming, joint optimization, network utility

中图分类号:

TN92

季中恒, 季新生, 黄开枝, 等. 基于多天线波束赋形的CRN分布式上行功率控制算法[J]. 电子学报, 2019, 47(12): 2472-2479.

JI Zhong-heng, JI Xin-sheng, HUANG Kai-zhi, et al. CRN Distributed Uplink Power Control Algorithm with Multi-antenna Beamforming[J]. Acta Electronica Sinica, 2019, 47(12): 2472-2479.

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基于多天线波束赋形的CRN分布式上行功率控制算法

CRN Distributed Uplink Power Control Algorithm with Multi-antenna Beamforming

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