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1. Hybrid Deep Learning Model for Cellular Network Traffic Prediction : Case Study using Telecom Time Series Data, Satellite Imagery, and Weather Data
Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)Sammanfattning : Cellular network traffic prediction is a critical challenge for communication providers, which is important for use cases such as traffic steering and base station resources management. Traditional prediction methods mostly rely on historical time-series data to predict traffic load, which often fail to model the real world and capture surrounding environment conditions. LÄS MER
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