Journal on Communications ›› 2017, Vol. 38 ›› Issue (9): 95-105.doi: 10.11959/j.issn.1000-436x.2017186

• Papers • Previous Articles     Next Articles

RW-MC:self-adaptive random walk based matrix completion algorithm

Xin-heng WANG,Qian-yun WANG,Jia-jie WANG,Guo-feng ZHAO,Wen-qiang JIN   

  1. School of Communication,Chongqing University of Posts and Telecommunications,Chongqing 400065,China
  • Revised:2017-06-27 Online:2017-09-01 Published:2017-10-18
  • Supported by:
    The National Natural Science Foundation of China(61402065);The National Basic Research Program of China (973 Program)(2012CB315803);The National Basic Research Program of China (973 Program)(2012CB315806)

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.

Key words: SDWN, matrix completion, RW-MC, random walk

CLC Number: 

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