Beamforming for heterogeneous cloud radio access network

Jiakuo ZUO; Longxiang YANG; Nan BAO

doi:10.11959/j.issn.1000-436x.2019170

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Beamforming for heterogeneous cloud radio access network

Papers | 更新时间：2024-06-05

- Beamforming for heterogeneous cloud radio access network
- Journal on Communications Vol. 40, Issue 8, Pages: 45-53(2019)
- 作者机构：
  
  1. 南京邮电大学物联网学院，江苏南京 210023
  2. 南京邮电大学通信与信息工程学院，江苏南京 210023
- 作者简介：
- 基金信息：
  
  The National Natural Science Fundation of China(61801237);The National Natural Science Foundation of China(61701255);Postdoctoral Fund of Jiangsu Province(SBH17024);The Natural Science Foundation of the Jiangsu Higher Education Institutions(15KJB510026);The Natural Science Foundation of Jiangsu Province(BK20150866);NUPTSF(NY215046);NUPTSF(NY217056)
- DOI：10.11959/j.issn.1000-436x.2019170
  CLC： TN92
- Online First：2019-08，
  
  Published：25 August 2019
- 稿件说明：
移动端阅览
Jiakuo ZUO, Longxiang YANG, Nan BAO. Beamforming for heterogeneous cloud radio access network[J]. Journal on Communications, 2019, 40(8): 45-53.
DOI：

Jiakuo ZUO, Longxiang YANG, Nan BAO. Beamforming for heterogeneous cloud radio access network[J]. Journal on Communications, 2019, 40(8): 45-53. DOI： 10.11959/j.issn.1000-436x.2019170.

摘要

针对现有异构云无线接入网络的研究主要集中在单个蜂窝网络场景，仅考虑了蜂窝网络内部干扰，忽略了蜂窝网络间干扰的问题，研究了多个蜂窝网络共存场景下的H-CRAN，通过最大化系统总传输速率，对宏基站和无线远端射频单元的波束成形向量进行联合优化。基于交替优化算法和拉格朗日对偶方法，提出了一种 MBS和RRH波束成形向量联合优化算法。首先将原优化问题分解为2个子优化问题，然后通过迭代的求解这2个子优化问题得到原优化问题的最终解。另外，根据拉格朗日对偶方法推导出了MBS和RRH波束成形向量的闭式表达式。在仿真实验中，将所提算法与其他波束成形算法进行了比较，实验结果证明，所提算法能够较好地改善系统传输速率。

Abstract

To overcome the problem that previous researches for heterogeneous cloud radio access network (H-CRAN) mainly focus on single macro cell

and only considered the intracell interference in the one macro cell

while the inter cell interferences among different macro cells are neglected

H-CRAN with multiple macro-cells was studied

and the objective was to maximize system sum-rate through jointly optimizing the beamforming vectors of macro base stations (MBS) and remote radio heads (RRH).Based on alternating optimization and Lagrangian dual method

a joint MBS and RRH beamforming algorithm was proposed.The original problem was first divided into two subproblems.Then

the two sub-problem were solved alternately to obtain the final solutions of the original problem.In addition

the closed expression solutions of the two sub-problem were derived based on Lagrangian dual method.The proposed algorithm was compared with some beamforming algorithms in the simulation.The experimental results demonstrate the proposed algorithm has a better performance in improving the sum-rate of H-CRAN.

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references

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