Identification of Wireless User Perception Based on Unsupervised Machine Learning

Abstract

Wireless user perception (WiUP) plays an important role in designing next-generation wireless communications systems. Users are very sensitive with the quality of WiUP. However, the bad quality of WiUP cannot be identified with traditional methods. In this paper, we propose an intelligent identification method using unsupervised machine learning. More precisely, we create an algorithm model based on historical data to realize feature extraction and clustering. The most similar cluster to those cells with bad WiUP is identified according to Euclidean distance. The experiment is conducted on the basis of a large amount of historical data. With several contrast experiments, Simulation results show that the method proposed achieves the accuracy of identification of bad WiUP over 93%. The study manifests that unsupervised machine learning is effective in identifying bad WiUP in wireless networks.