电子学报 ›› 2016, Vol. 44 ›› Issue (3): 718-724.DOI: 10.3969/j.issn.0372-2112.2016.03.033

• 学术论文 • 上一篇    下一篇

能量有效的认知多小区协同波束赋形算法

张秀秀1, 陈东华1, 谢维波2   

  1. 1. 华侨大学信息科学与工程学院, 福建厦门 361021;
    2. 华侨大学计算机科学与技术学院, 福建厦门 361021
  • 收稿日期:2014-09-01 修回日期:2015-01-30 出版日期:2016-03-25 发布日期:2016-03-25
  • 通讯作者: 陈东华
  • 作者简介:张秀秀 女,1988年9月出生于河南濮阳,华侨大学硕士研究生,主要研究方向为协作多点传输技术管理. E-mail:292573449@qq.com;谢维波 男,1964年10月出生于福建泉州,现为华侨大学教授,主要研究方向为智能信号与信息处理、图像处理、无线移动自组织网络等. E-mail:xwblxf@ hqu.edu.cn
  • 基金资助:

    国家自然科学基金(No.61271383)

Energy Efficient Coordinated Beamforming for Cognitive Multi-cell Systems

ZHANG Xiu-xiu1, CHEN Dong-hua1, XIE Wei-bo2   

  1. 1. College of Information Science and Engineering, Huaqiao University, Xiamen, Fujian 361021, China;
    2. College of Computer Science and Technology, Huaqiao University, Xiamen, Fujian 361021, China
  • Received:2014-09-01 Revised:2015-01-30 Online:2016-03-25 Published:2016-03-25

摘要:

针对认知多小区多用户下行传输链路,提出了一种基于能量效率最大化准则的协同波束赋形优化方法.该方法采用迫零消除小区内用户间干扰,在保证用户最小速率需求及认知干扰约束的同时,实现了能量效率和频谱效率的同步改善.为了分布式求解优化问题,通过约束泄露干扰并利用半定松弛,将其转换为凸问题,在此基础上,采用部分对偶分解方法将多小区联合优化问题分解为一组单小区优化问题,从而实现了分布式求解.仿真结果表明,该方法不仅实现了能量效率和频谱效率的有效折中,而且达到了集中式算法的性能.

关键词: 认知多小区, 波束赋形, 能量效率, 对偶分解, 凸优化

Abstract:

A cooperative beamforming algorithm based on the maximize of energy efficiency(EE) was proposed for the cognitive multi-cell multiuser downlinks.By using zero-forcing algorithm to eliminate intra-cell interference,the proposed scheme makes a simultaneous improvement in energy efficiency and spectrum efficiency(SE) while guaranteeing minimum rate for the secondary users and the cognitive interference constraints. To implement a decentralized algorithm,the original problem was firstly transformed to a convex one via semi-definite relaxation and the leakage interference constraints,and then was decomposed into a group of sub-problems on the basis of each cell by using dual decomposition,which admits a distributed computation of the problem.Simulation results show that the proposed scheme not only makes a good tradeoff between the EE and SE,but also attains the same performance as the centralized algorithm.

Key words: cognitive multi-cell, beamforming, energy efficiency, dual decomposition, convex optimization

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