4G LTE Network Data Collection and Analysis Along Public Transportation Routes
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With the advancements in wireless network technologies over the past few decades and the deployment of 4G LTE networks, the capabilities and services provided to end-users have become seemingly endless. Users of smartphones utilize high-speed network services while commuting on public buses and hope to have a consistent, high-quality connection for the duration of their trip. Due to the massive load demand on cellular networks and frequent changes in the underlying radio channel, users often experience sudden unexpected variations in the connection quality. To overcome such a variation and maintain a consistent connection, we need to predict these variations before they occur. This can be accomplished by analyzing different network quality parameters at various times and locations and investigating the main factors that affect the network’s performance and network QoS. To this end, we conducted a network survey via Kingston Transit, in Kingston, Ontario, using the Android network monitoring application G-NetTrack Pro from which we constructed a dataset of various client-side wireless network quality parameters. The dataset consists of 30 repeated public transit bus trips, each lasting no more than one hour. We studied two techniques for throughput analysis: regression predictive modelling and time series forecasting. For regression predictive modelling, we deployed various machine learning models on the collected data for throughput prediction and achieved the highest prediction performance with the random forest model. For time series forecasting, we used statistical methods as well as deep learning architectures. Our evaluation shows that the machine learning models had a higher throughput prediction performance than the time series forecasting techniques. In this thesis, we present an analysis of the collected data, where we investigate the effects of time and location on the network’s measured throughput and signal strength. Also, we discuss and compare the results of applying different throughput prediction techniques on the collected data.