The use of artificial intelligence is foreseen to be pervasive in future mobile radio networks, enabling dynamic and proactive radio resource provisioning and allocation as well as end-to-end optimization of the network architecture. Current approaches in mobile radio networks commonly assume having a complete batch of data on the specific network element when optimizing and adapting the network working configuration. Such a pipeline is at odds with the increasing complexity and extreme flexibility of 5G and next generation systems where reconfiguration decisions might be taken rather frequently, and with only few data available. In this paper, we focus on the problem of predicting channel quality and average number of active user equipment when a limited amount of data is available from the cell to predict and a high number of predictions need to be carried out simultaneously. We propose a transfer learning framework based on one dimensional convolutional neural networks and explore several models with different complexity overhead for the prediction task across 100 cells. The performance of the proposed framework is validated against classical machine learning approaches in terms of accuracy and computation time when varying the amount of data available for training. Achieved results indicate that transfer learning outperforms the 'non-transfer' approaches, specifically when the amount of data available from the cell to predict is scarce.

Anticipating Mobile Radio Networks Key Performance Indicators with Transfer Learning

Redondi A. E. C.;Cesana M.;Malanchini I.
2021

Abstract

The use of artificial intelligence is foreseen to be pervasive in future mobile radio networks, enabling dynamic and proactive radio resource provisioning and allocation as well as end-to-end optimization of the network architecture. Current approaches in mobile radio networks commonly assume having a complete batch of data on the specific network element when optimizing and adapting the network working configuration. Such a pipeline is at odds with the increasing complexity and extreme flexibility of 5G and next generation systems where reconfiguration decisions might be taken rather frequently, and with only few data available. In this paper, we focus on the problem of predicting channel quality and average number of active user equipment when a limited amount of data is available from the cell to predict and a high number of predictions need to be carried out simultaneously. We propose a transfer learning framework based on one dimensional convolutional neural networks and explore several models with different complexity overhead for the prediction task across 100 cells. The performance of the proposed framework is validated against classical machine learning approaches in terms of accuracy and computation time when varying the amount of data available for training. Achieved results indicate that transfer learning outperforms the 'non-transfer' approaches, specifically when the amount of data available from the cell to predict is scarce.
16th Conference on Wireless On-Demand Network Systems and Services, WONS 2021
978-3-903176-35-5
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11311/1185330
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