IMDEA Networks Institute Publications Repository

Processing ANN Traffic Predictions for RAN Energy Efficiency

Vallero, Greta and Renga, Daniela and Meo, Michela and Ajmone Marsan, Marco (2020) Processing ANN Traffic Predictions for RAN Energy Efficiency. In: The 23rd International ACM Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems (MSWiM 2020), 16-20 November 2020, Virtual event (previously at Alicante, Spain).

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Abstract

The field of networking, like many others, is experiencing a peak of interest in the use of Machine Learning (ML) algorithms. In this paper, we focus on the application of ML tools to resource management in a portion of a Radio Access Network (RAN) and, in particular, to Base Station (BS) activation and deactivation, aiming at reducing energy consumption while providing enough capacity to satisfy the variable traffic demand generated by end users. In order to properly decide on BS (de)activation, traffic predictions are needed, and Artificial Neural Networks (ANN) are used for this purpose. Since critical BS (de)activation decisions are not taken in proximity of minima and maxima of the traffic patterns, high accuracy in the traffic estimation is not required at those times, but only close to the times when a decision is taken. This calls for careful processing of the ANN traffic predictions to increase the probability of correct decision. Numerical performance results in terms of energy saving and traffic lost due to incorrect BS deactivations are obtained by simulating algorithms for traffic predictions processing, using real traffic as input. Results suggest that good performance trade-offs can be achieved even in presence of non-negligible traffic prediction errors, if these forecasts are properly processed.

Item Type: Conference or Workshop Papers (Paper)
Uncontrolled Keywords: Radio access network; base station; energy efficiency; traffic prediction; neural network.
Subjects: UNSPECIFIED
Divisions: UNSPECIFIED
Depositing User: Rebeca De Miguel
Date Deposited: 11 Nov 2020 07:17
Last Modified: 11 Nov 2020 07:17
URI: http://eprints.networks.imdea.org/id/eprint/2221

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