Deep Learning Based User Mobility Prediction
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Date
2023-09
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Addis Ababa University
Abstract
Telecommunication service providers mainly emphasize on providing uninterrupted network
access with the maximum attainable quality of service. With this in mind, service
providers often monitor and utilize information acquired from user mobility patterns to
performing effective resource management of network resources and to predict the user’s
future location. For instance, information associated with user mobility is used to reduces
the cost of paging, managing the bandwidth resources and efficient planning. Overall, with
the current trend of increase in the number of devices connected to mobile networks, telecom
service providers are expected to carefully monitor and utilize user mobility patterns
in order to improve the quality of service provided to their customers.
With this understanding, in this thesis, we propose to utilize neural networks to predict user
mobility, which helps to increase the performance of mobility analysis in cellular networks.
This in turn is expected to improve the studying and under. In general, we intend to provide
useful insights into how users migrate across various geographic areas and how they interact
with the network infrastructure supplied by Ethiotelcom by constructing neural network
based user predicting models. To meet this objective, we used mobility data obtained from
Call Details Records (CDR) to forecast the future mobility of users (devices) as a sequential
time series. Our experimental outcome suggests that a neural network based on one
dimensional convolutional effective tool for user mobility analysis using datasets extracted
from CDR. In reality, the Conv-LSTM networks take advantage of both an LSTM’s ability
to capture long-short dependency for time series data and the strength of the convolutional
layers to extract localized features from complicated and non-linear dataset.
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Keywords
Telecom carriers, Mobility prediction, Resource Management, Deep Learning, cellular network.