.png){kind=icon}
.png){kind=icon}
Return
Cell Scene Division and Visualization Based on Autoencoder and K-Means Algorithm
.png){kind=icon}
OA
AI
DOI：10.1109/ACCESS.2019.2953184.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
For the network service construction and optimization of wireless cell, the effective scene division is an important basis for formulating more accurate network construction schemes and optimization strategies. The traditional cell scene division method is manually divided according to the single-dimensional business indicators, but there are some problems such as the inaccuracy of division and the inability to visualize. In this paper, we propose a cell scene division and visualization method based on autoencoder and K-means algorithm. We train an autoencoder network to conduct the dimension reduction of the wireless perception key quality indicator (KQI) data of cells, and then use elbow method and K-means algorithm to cluster the dimension-reduced data precisely. Through statistical analysis and comparison of indicators of cells in different classes obtained by clustering, we finally achieve accurate cell scene division and visualization.
Keywords:
Scene division
autoencoder
K-means
elbow method
machine learning
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
3.6
Papers:
9.8W
Citations:
29.4W