RW-MC：基于随机游走的自适应矩阵填充算法

王新恒; 王倩云; 王佳杰; 赵国锋; 靳文强

doi:10.11959/j.issn.1000-436x.2017186

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RW-MC：基于随机游走的自适应矩阵填充算法

学术论文 | 更新时间：2024-06-05

- RW-MC：基于随机游走的自适应矩阵填充算法
- RW-MC:self-adaptive random walk based matrix completion algorithm
- 通信学报 2017年38卷第9期页码：95-105
- 作者机构：
  
  重庆邮电大学通信学院，重庆 400065
- 作者简介：
  
  [ "王新恒（1988-），男，山东枣庄人，重庆邮电大学博士生，主要研究方向为网络测量、SDN、NFV以及无线通信。" ]
  [ "王倩云（1992-），女，四川渠县人，重庆邮电大学硕士生，主要研究方向为无线局域网节能、网络测量。" ]
  [ "王佳杰（1992-），男，河北唐山人，重庆邮电大学硕士生，主要研究方向为网络测量、无线通信。" ]
  [ "赵国锋（1972-），男，陕西西安人，博士，重庆邮电大学教授，主要研究方向为未来网络、SDN、NFV、网络安全等。" ]
  [ "靳文强（1993-），男，内蒙古乌兰察布人，重庆邮电大学硕士生，主要研究方向为未来网络、SDN、NFV以及无线通信。" ]
- 基金信息：
  
  国家自然科学基金资助项目(61402065);国家重点基础研究发展计划（“973”计划）基金资助项目(2012CB315803);国家重点基础研究发展计划(“973”计划)基金资助项目(2012CB315806)
- DOI：10.11959/j.issn.1000-436x.2017186
  中图分类号： TP319
- 网络首发：2017-09，
  
  纸质出版：2017-09-25
- 稿件说明：
移动端阅览
王新恒, 王倩云, 王佳杰, 等. RW-MC：基于随机游走的自适应矩阵填充算法[J]. 通信学报, 2017,38(9):95-105.

Xin-heng WANG, Qian-yun WANG, Jia-jie WANG, et al. RW-MC:self-adaptive random walk based matrix completion algorithm[J]. Journal on Communications, 2017, 38(9): 95-105.
王新恒, 王倩云, 王佳杰, 等. RW-MC：基于随机游走的自适应矩阵填充算法[J]. 通信学报, 2017,38(9):95-105. DOI： 10.11959/j.issn.1000-436x.2017186.

Xin-heng WANG, Qian-yun WANG, Jia-jie WANG, et al. RW-MC:self-adaptive random walk based matrix completion algorithm[J]. Journal on Communications, 2017, 38(9): 95-105. DOI： 10.11959/j.issn.1000-436x.2017186.

摘要

为了对软件定义无线网络系统中虚拟接入点（VAP）状态信息进行实时测量，根据实际网络中虚拟接入点性能的数据特征，提出一种基于随机游走的自适应矩阵填充算法（RW-MC）。首先，基于离散度和覆盖度的采样模型确定初始样本点；然后，利用随机游走模型对之前时隙的采样点序列建模分析，确定新时隙的测量点；最后，比较相邻窗口的恢复矩阵中重叠部分的误差率与标准误差，实现测量点的动态自适应。实验表明，该测量方法能够在低采样率、低误差的情况下实现对全网VAP实时感知。

Abstract

Concerning the continually perceiving performance of virtual access points (VAP) was urgent in software-defined wireless network (SDWN)

with the features of VAPs’ measurement data (VMD)

a self-adaptive matrix completion algorithm based on random walk was proposed

named RW-MC.Firstly

the discrete ratio and covering ratio of VMD account for a sample determination model was used to claim initial samples.Secondly

random walk model was implemented for generating sampling data points in the next iteration.Finally

a self-adaptive sampling redress model concerning the differences between the current error rates and normalize error rates of neighboring completion matrices.The experiments show that the approach can collect the real-time sensory data

meanwhile

maintain a relatively low error rate for a small sampling rate.

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Keywords

references

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