Name
Abstract | ||
|---|---|---|
This study proposes a two-step ML-based multislice radio resource allocation framework for 5G networks, specifically for emergency scenarios and featuring a good tradeoff between complexity and performance. In the first step, call-level resource demands are predicted using supervised ML, which are then aggregated to predict slice-specific resource demands. An innovative method is included in this step to ensure the collection of representative training data for the supervised ML. In the second step, a contextual multi-armed bandit reinforcement learning model is applied to derive the resource allocation among the slices based on the slice-specific resource demand predictions. The simulation results show that the proposed framework outperforms alternative solutions in the defined utility values for priority emergency traffic at the cost of modest performance sacrifice of the background traffic. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/EuCNC/6GSummit51104.2021.9482547 | 2021 Joint European Conference on Networks and Communications & 6G Summit (EuCNC/6G Summit) |
Keywords | DocType | ISSN |
5G,network slicing,slice management,machine learning,emergency scenarios | Conference | 2475-6490 |
ISBN | Citations | PageRank |
978-1-6654-3021-0 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Apoorva Arora | 1 | 0 | 0.34 |
| Toni Dimitrovski | 2 | 0 | 0.34 |
| Remco Litjens | 3 | 226 | 34.64 |
| Haibin Zhang | 4 | 118 | 18.58 |