Premsankar, Gopika and Piao, Guangyuan and Nicholson, Patrick K. and Di Francesco, Mario and Lugones, Diego
(2021)
Data-driven Energy Conservation in Cellular Networks: A Systems Approach.
IEEE Transactions on Network and Service Management, 18 (3).
pp. 3567-3582.
ISSN 1932-4537
Abstract
The energy consumption of mobile networks is
already substantial nowadays, and only expected to further
increase with the roll-out of 5G. Base stations are the key elements in this context: reducing their energy consumption is
of paramount importance for network operators, not only to
lower operating costs, but also to meet sustainable development
goals. Today’s base stations are typically over-provisioned, i.e.,
they comprise multiple cells to meet the peak load in a region.
Therefore, substantial energy savings are possible by switching
off cells that are under-utilized. This article proposes a datadriven approach to determine the time periods when a cell can
be switched off. Forecasting is used to accurately predict network
utilization and automatically find the time intervals to reliably
switch off a cell. We carefully analyze the requirements of the
system as a whole, from data collection to forecasting methods, to enable effective energy savings in practice. Considering
several real-world traces from LTE networks, we show that an
average of 10.24% energy savings is possible. We explore the
trade-offs between energy savings and overhead in switching off
cells, and provide insights into the choice of methods accordingly.
In particular, we show that the accuracy of forecasting is not the
most important factor in achieving energy savings; instead, the
prediction (uncertainty) interval plays a key role in being able
to achieve energy savings with less impact on end-users. Finally,
we propose a model to generate utilization traces that match the
distribution of real-world traces obtained from cellular networks.
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