Denizer, BirkanBirkanDenizerLandsiedel, OlafOlafLandsiedel2026-01-222026-01-222023-12-012nd Workshop on Machine Learning & Netwoking, MaLeNe 2023https://hdl.handle.net/11420/610115G networks provide high throughput and low latency connections, crucial for remote monitoring and control of mission-critical operations. Managing buffer levels and accurate bandwidth estimations are essential for low-latency applications. However, wireless networks are susceptible to fluctuations in quality metrics due to mobility and interference, impacting link utilization. Sudden quality deterioration can lead to lower Quality-of-Experience (QoE). To address this, we propose a neural network-based bandwidth prediction system. Our system utilizes historical data for time-series forecasting using the Informer model. It achieves 10% lower errors on a publicly available LTE dataset and 51% lower errors on a publicly available 5G dataset. Future work includes multivariate predictions and the creation of a new 5G dataset.enhttps://creativecommons.org/licenses/by/4.0/Bandwidth predictionLTE5GInformerMLE@TUHHTechnology::621: Applied Physics::621.3: Electrical Engineering, Electronic EngineeringComputer Science, Information and General Works::004: Computer SciencesConference Paperhttps://doi.org/10.15480/882.1651310.15480/882.16513Conference Paper