Optimisation of massive MIMO data classification algorithm based on fuzzy C-means and differential evolution method

Abstract

In the environment of massive data multiple input multiple output (MIMO), it leads to poor data recognisability, clustering ability and overall data processing ability. Therefore, a massive MIMO data classification algorithm based on fuzzy C-means and differential evolution method is proposed. The information flow model of time series of massive data in data flow is established, and the time series features of massive data are extracted by using autocorrelation feature analysis method. Through fuzzy C-means, the features of massive data are cross fused and clustered. The global convergence and stability of data classification are adjusted and controlled by differential evaluation method, and then the delay of data classification is modified to optimise the data classification algorithm. The simulation results show that the data classification recall rate of this method is higher than 95%, and it has the advantages of strong clustering ability and low misclassification rate.