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

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.