WeUp: Wireless User Perception Based on Dimensional Reduction and Semi-Supervised Clustering

Wireless user perception (WeUP) is considered one of the most important factors in designing next-generation wireless communications systems. The recognition of WeUP involves lots of labor cost up to now. In order to solve this problem, this paper proposes an accuracy recognition algorithm for WeUP based on dimensional reduction and semi-supervised clustering. Usually, the WeUP is highly reflected in the key quality indicator (KQI). we build up a database of KQI including more than 1000 cells to train a deep belief autoencoder (DBA) for dimensional reduction (DR). Then we feed the historical unlabeled and manual-labeled negative data set after dimensional reduction into semi-supervised clustering model. After that, we find out a recognition range, which is the most similar to manual-labeled objects with unsatisfied WeUP. Simulation results show that our proposed method achieves an accurate recognition of unsatisfied WeUP over 93%.The study indicates that dimensional reduction and semi-supervised machine learning method is effective in recognizing unsatisfied WeUP in wireless networks.