Name
Abstract | ||
|---|---|---|
The assumption of a fixed computational capacity at the Baseband Unit (BBU) pools in a Cloud Radio Access Network (C-RAN) deployment results in underutilized resources or unsatisfied users depending on traffic requirements. In this paper a new strategy to predict the required resources based on Machine Learning techniques is proposed and analysed. Support Vector Machine (SVM), Time-Delay Neural Network (TDNN), and Long Short-Term Memory (LSTM) have been tested and compared to select the best predicting approach. Instead of using a regular synthetic scenario a realistic dense cell deployment over Vienna city is used to validate the results. Authors show that the proposed solution reduces the unused resources average by 96 %. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/EuCNC48522.2020.9200958 | 2020 European Conference on Networks and Communications (EuCNC) |
Keywords | DocType | ISSN |
Support vector machines,Computer architecture,Computational modeling,Resource management,Microprocessors,Prediction algorithms,Machine learning | Conference | 2475-6490 |
ISBN | Citations | PageRank |
978-1-7281-4355-2 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Rolando Guerra-Gómez | 1 | 1 | 0.75 |
| Silvia Ruiz-Boque | 2 | 63 | 12.78 |
| Mario García-lozano | 3 | 116 | 25.39 |
| Joan Olmos Bonafé | 4 | 1 | 0.75 |