Baseband-Function Placement with Multi-Task Traffic Prediction for 5G Radio Access Networks

Baseband-Function Placement with Multi-Task Traffic Prediction for 5G Radio Access Networks

Zorello, Ligia Maria Moreira (Politecnico di Milano)
Bliek, L. (Eindhoven University of Technology)
Troia, Sebastian (Politecnico di Milano)
Guns, Tias (Vrije Universiteit Brussel)
Verwer, S.E. (TU Delft Cyber Security)
Maier, Guido (Politecnico di Milano)

2022

The 5G Radio Access Network (RAN) virtualization aims to improve network quality and lower the operator's costs. One of its main features is the functional split, i.e., dividing the instantiation of RAN baseband functions into different units over metro-network nodes. However, its optimal placement is non-trivial: it depends on the application requirements and on the expected traffic volume, whose daily variation highly impacts the total power consumption. Current optimization solutions fail to provide a placement solution capable of handling traffic fluctuations. In fact, the standard machine learning algorithms used in the literature for planning the network resources in advance result in an allocation that is inadequate to carry the actual traffic at all the time-slots. Hence, we must reserve an artificial buffer capacity in the nodes to ensure feasibility. Instead, our proposed method exploits a fine-grained two-step multi-task algorithm that predicts the mean and quantile traffic, making the artificial capacity no longer necessary. The subsequent placement uses mixed-integer linear programming and a heuristic. The former considers the expected traffic in the objective function (to estimate costs) and the quantile in the constraints (to enforce capacity limits). The heuristic combines the mean and quantile results to minimize the power and comply with the requirements. While using sufficiently large artificial buffers guarantees robustness with a mild power increase compared to the oracle, the fine-grained multi-task model improves the results, reducing the power consumption compared to the mean and meets all constraints. The heuristic enables significant computational time reduction.

5G Radio Access Network
functional split
network function virtualization
traffic prediction
mathematical optimization
machine learning
energy saving

http://resolver.tudelft.nl/uuid:a48bcbe5-907c-4f4b-b994-8d24345ebc7a

https://doi.org/10.1109/TNSM.2022.3190059

2023-07-01

1932-4537

IEEE Transactions on Network and Service Management, 19 (4), 5104 - 5119

Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Institutional Repository

journal article

© 2022 Ligia Maria Moreira Zorello, L. Bliek, Sebastian Troia, Tias Guns, S.E. Verwer, Guido Maier