Machine learning for traffic prediction and communication efficient data analytic in wireless networks
Author(s)
Alamoudi, Abdulrahman Mohammed A Mohammed
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Abstract
The exponential growth of available data has catalyzed the prominence of deep learning as a foundational tool for interpreting data abstractions and constructing computational models. This paradigm shift has profoundly impacted our understanding of information processing, enabling exploration across diverse domains such as text and signal analysis, image and audio recognition, social network analysis, and bioinformatics. The overarching aim of this research is to mitigate wireless network traffic and operational costs for both mobile users and network operators. This integrated framework endeavors to develop predictive models by analyzing the behavior of mobile users within wireless networks and designing efficient, task-oriented models in wireless communication. Specifically, our research harnesses machine learning techniques to learn and forecast mobile user behaviors, design semantic communication systems resilient to noisy channels, and implement unsupervised distributed functional compression over wireless channels.
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Date
2024-04-27
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Text
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Dissertation