Harnessing collaboration to improve the accuracy of throughput prediction in cellular networks
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Date
2026-02-19
Authors
Raca, Darijo
Zahran, Ahmed
Sreenan, Cormac J.
Tiwari, Abhishek
Gupta, Riten
Journal Title
Journal ISSN
Volume Title
Publisher
Institute of Electrical and Electronics Engineers Inc.
Published Version
Abstract
Throughput prediction in cellular networks has garnered considerable interest in recent years due to its demonstrated positive impact on quality of experience. Existing proposals operate by having each user device make its own predictions, in a standalone manner, on the basis of its local measurements. Our hypothesis is that pooling of device measurements in a collaborative way can yield more accurate predictions, by allowing a broader set of observations from within a cell to be combined. To this end, we identify shortcomings in existing datasets, and then present our collaborative approach, along with an extensive evaluation. When compared to operating standalone, the results show a reduction in prediction error of up to 66% for users that have been inactive, and up to 17% for active users.
Description
Keywords
Cellular networks , Collaborative , Machine learning , Measurement , Throughput prediction , [ComputerScience]
Citation
Raca, D, Zahran, A, Sreenan, C J, Tiwari, A & Gupta, R 2026, Harnessing collaboration to improve the accuracy of throughput prediction in cellular networks. in K Erenli, C Guetl, Y Jararweh & J Jansen (eds), 2025 3rd International Conference on Foundation and Large Language Models, FLLM 2025. 2025 3rd International Conference on Foundation and Large Language Models, FLLM 2025, Institute of Electrical and Electronics Engineers Inc., pp. 1284-1289, 2025 3rd International Conference on Foundation and Large Language Models, FLLM 2025, Vienna, Austria, 25/11/25. https://doi.org/10.1109/FLLM67465.2025.11391126
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