Estimating End-User Throughput Using Service Provider Cell Traces Via Gradient Boosting

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  • The adoption of 5G networks has enabled supporting applications that require high bandwidths and low latencies. Service providers need to manage their resources efficiently to avoid service interruptions. Therefore, being aware of customer's experienced bandwidth is of paramount importance. Utilizing the traces collected on the service provider's side to estimate the experienced bandwidth on the user's side is a problem that was not studied in the literature. Moreover, the traces collected by the service provider are sparse and missing a large number of values to be reliable in predicting the user's experienced bandwidth. In this thesis, we focus on accurately imputing missing values in the collected traces, and consequently, we build a Regularized Gradient Boosting model to predict the user's throughput using traces that are exclusively collected from the service provider's resources. Our approach shows that using our imputing and prediction approaches, we can accurately estimate the user equipment's throughput.

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  • Copyright © 2022 the author(s). Theses may be used for non-commercial research, educational, or related academic purposes only. Such uses include personal study, research, scholarship, and teaching. Theses may only be shared by linking to Carleton University Institutional Repository and no part may be used without proper attribution to the author. No part may be used for commercial purposes directly or indirectly via a for-profit platform; no adaptation or derivative works are permitted without consent from the copyright owner.
Date Created
  • 2022


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