Paper ID: 2410.19777 • Published Oct 14, 2024
Deep Learning-driven Mobile Traffic Measurement Collection and Analysis
Yini Fang
TL;DR
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Modelling dynamic traffic patterns and especially the continuously changing
dependencies between different base stations, which previous studies overlook,
is challenging. Traditional algorithms struggle to process large volumes of
data and to extract deep insights that help elucidate mobile traffic demands
with fine granularity, as well as how these demands will evolve in the future.
Therefore, in this thesis we harness the powerful hierarchical feature learning
abilities of Deep Learning (DL) techniques in both spatial and temporal domains
and develop solutions for precise city-scale mobile traffic analysis and
forecasting. Firstly, we design Spider, a mobile traffic measurement collection
and reconstruction framework with a view to reducing the cost of measurement
collection and inferring traffic consumption with high accuracy, despite
working with sparse information. In particular, we train a reinforcement
learning agent to selectively sample subsets of target mobile coverage areas
and tackle the large action space problem specific to this setting. We then
introduce a lightweight neural network model to reconstruct the traffic
consumption based on historical sparse measurements. Our proposed framework
outperforms existing solutions on a real-world mobile traffic dataset.
Secondly, we design SDGNet, a handover-aware graph neural network model for
long-term mobile traffic forecasting. We model the cellular network as a graph,
and leverage handover frequency to capture the dependencies between base
stations across time. Handover information reflects user mobility such as daily
commute, which helps in increasing the accuracy of the forecasts made. We
proposed dynamic graph convolution to extract features from both traffic
consumption and handover data, showing that our model outperforms other
benchmark graph models on a mobile traffic dataset collected by a major network
operator.