电子学报 ›› 2016, Vol. 44 ›› Issue (6): 1406-1412.DOI: 10.3969/j.issn.0372-2112.2016.06.022

• 学术论文 • 上一篇    下一篇

认知MIMO干扰网络最优干扰对齐算法

朱世磊, 郑娜娥, 巴斌, 胡捍英   

  1. 解放军信息工程大学导航与空天目标工程学院, 河南郑州 450002
  • 收稿日期:2014-12-17 修回日期:2015-03-30 出版日期:2016-06-25 发布日期:2016-06-25
  • 作者简介:朱世磊 男,1987年生于江苏淮安.现为解放军信息工程大学导航与空天目标工程学院博士研究生.主要研究方向为MIMO无线通信、认知无线电频谱共享、预编码和干扰对齐技术.E-mail:zhushilei3620@163.com;郑娜娥 女,1984年生于福建漳州.现为解放军信息工程大学导航与空天目标工程学院博士、讲师.主要研究方向为MIMO信号处理和无线资源分配.E-mail:13837122426@163.com;巴斌 男,1987年生于河南周口.现为解放军信息工程大学导航与空天目标工程学院博士研究生.主要研究方向为通信系统与信号处理.E-mail:xidianbabin@163.com;胡捍英 男,1961年生于河南内乡.现为解放军信息工程大学教授、博士生导师.主要研究方向为无线通信和空间信息技术.E-mail:huhanying@vip.sina.com
  • 基金资助:

    国家科技重大专项(No.2011ZX03003-003-02);国家863计划项目(No.2012AA01A502,No.2012AA01A505)

Optimum Interference Alignment Algorithm for Cognitive MIMO Interference Network

ZHU Shi-lei, ZHENG Na-e, BA Bin, HU Han-ying   

  1. Institute of Navigation and Space Target Engineering, Information Engineering University, Zhengzhou, Henan 450000, China
  • Received:2014-12-17 Revised:2015-03-30 Online:2016-06-25 Published:2016-06-25

摘要:

认知无线电通过与MIMO(Multi-Input Multi-Output)、OFDM(Orthogonal Frequency Division Multiplexing)、超宽带、协作通信等技术融合来改善频谱利用率.而认知MIMO是认知无线电和MIMO技术的融合,虽然具有干扰抑制、抗多径衰落、空间分集和复用等优势,但是由于underlay共享方式中干扰温度约束的存在,导致发送预编码矩阵之间相互耦合,因此该技术在underlay干扰网络中难以获得最优的传输性能.针对该问题,通过交替迭代的方式,结合Rayleigh-Ritz定理和凸优化理论,推导了最优收发矩阵之间的迭代关系,提出一种最优干扰对齐算法.该算法利用Lagrange部分对偶方式来去除干扰温度约束,并采用次梯度投影法更新Lagrange变量,克服了已有半正定松弛算法因忽略矩阵秩约束而导致速率性能下降的缺陷.理论分析和数值仿真验证了算法的有效性,结果表明所提算法可实现网络可达速率和的最大化.

关键词: 认知MIMO, underlay频谱共享, 干扰网络, 干扰对齐, Lagrange部分对偶

Abstract:

Cognitive radio can improve the spectrum efficiency by fusing with technologies such as multi-input multi-output (MIMO), orthogonal frequency division multiplexing (OFDM), ultra wideband (UWB), cooperative communication, etc.Cognitive MIMO is a fusion technology of cognitive radio and MIMO, which has advantages of interference suppression, anti-multipath fading, spatial diversity, and multiplexing.However, there is intercoupling among its precoding matrices because of the interference temperature constraint in underlay sharing mode, which makes it difficult for the cognitive MIMO in the underlay interference network to obtain optimal transmitting performance.Consequently, an optimal interference align algorithm for cognitive MIMO interference network is proposed to obtain the optimized interference network transmitting performance, in which the iteration relationship between the optimal transmitting and receiving matrices is derived by interactively and alternately using transmitting precoding and receiving interference subspace matrix, and the derivation process is based on Rayleigh-Ritz theorem and convex optimization theory.In order to remove the interference temperature constraint, the Lagrange partial of dual-decomposition was exploited, and the sub-gradient projection method was adopted to update the Lagrange variable, which overcame the shortcoming of decreasing transmitting rate caused by ignorance of the matrix rank constraint in the existing semi-definite relaxation algorithms.The validity of this algorithm is verified by theoretical analysis and numeric simulations, and results also indicate that the proposed algorithm is capable of maximizing the cognitive MIMO interference network available transmitting rate.

Key words: cognitive multi-input multi-output (MIMO), underlay spectrum sharing, interference network, interference alignment, Lagrange partial dual

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