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Gijón, Carolina Toril-Genovés, Matías Luna-Ramírez, Salvador 2025-02-03T07:19:28Z 2025-02-03T07:19:28Z 2022-09-21 C. Gijón, M. Toril and S. Luna-Ramírez, "Data-Driven Estimation of Throughput Performance in Sliced Radio Access Networks via Supervised Learning," in IEEE Transactions on Network and Service Management, vol. 20, no. 2, pp. 1008-1023, June 2023, doi: 10.1109/TNSM.2022.3208336 https://hdl.handle.net/10630/37550 10.1109/TNSM.2022.3208336 In 5G systems, Network Slicing (NS) feature allows to deploy several logical networks customized for specific verticals over a common physical infrastructure. To make the most of this feature, cellular operators need models reflecting cell and slice performance for re-dimensioning the Radio Access Network (RAN). For enhanced Mobility BroadBand (eMBB) services, throughput is regarded as a key performance metric since it strongly influences user experience. This work presents the first comprehensive analysis tackling cell and slice throughput estimation in the downlink of RAN-sliced networks through Supervised Learning (SL), based on information collected in the operations support system. Different well-known SL algorithms are tested in two NS scenarios with single-service or multi-service slices serving eMBB users. To this end, several synthetic datasets are generated with a system-level simulator emulating the activity of a sliced RAN. Results show that NS alters the correlation between network performance indicators and cell throughput compared to legacy RANs, thus being required a separate analysis for NS scenarios. Moreover, the best model to estimate throughput at cell/slice level may depend on the scenario (single-service vs multi-service slices). In all cases, the best models have shown an estimation error below 10 %. eng open access Antenas (Electrónica) Data-Driven Estimation of Throughput Performance in Sliced Radio Access Networks via Supervised Learning journal article