Paper Info
Title
Bsenet: A Data-Driven Spatio-Temporal Representation Learning For Base Station Embedding
Abstract
Base station (BS) plays a critical role in the wireless network. There has been some research on exploring the spatio-temporal information of BS in different fields. However, these works lack re-usability, which require the new researcher to re-do the work of representing the spatio-temporal information. To solve this problem, we propose a neural network model based on autoencoder and representation learning called BSENet to learn embedding of BS based on raw data. The embedding contains spatio-temporal information of BS. In this way, other fields that are related to BS can make use of spatio-temporal information with BSENet embeddings. Besides the spatial information, BSENet can maintain the independence of BS. Moreover, we use bi-directional LSTM to get temporal information and propose a time dropout method to improve the generalization ability. We propose a soft threshold to consider all spatial relations. In addition, we introduce weight to enhance compatibility. We treat the missing states as the inputs to deal with the missing values. The results of clustering show that BSENet embedding is better than other embeddings. In experiments of mobile traffic prediction, BSENet embedding helps a temporal model to improve the Mean Squared Error (MSE) from 0.01463 to 0.01334, which is similar to 0.01302 of a spatio-temporal model. At the same time, the training time increases a little but is still 5.0x faster than spatio-temporal model.
Year
DOI
Venue
2020
10.1109/ACCESS.2020.2980597
IEEE ACCESS
Keywords
DocType
Volume
Autoencoder, base station, BSENet, representation learning, spatio-temporal information, wireless networks
Journal
8
ISSN
Citations 
PageRank 
2169-3536
0
0.34
References 
Authors
0
5
Name
Order
Citations
PageRank
Xinyu Wang111728.52
Tan Yang22310.97
Cui Yidong396.35
Yuehui Jin4319.06
Hongbo Wang500.34