Unpublished / Preprint
(Conference contribution)

A Predictive Dynamic Power Management for LTE-Advanced Mobile Devices

Publication Details
Publication status: Accepted
Author(s): Ah Sue J, Brand P, Brendel J, Hasholzner R, Falk J, Teich J
Publication year: 2018

Event details
Event: IEEE Wireless Communications and Networking Conference
Event location: Barcelona, Catalonia, Spain
Start date of the event: 15/04/2018
End date of the event: 18/04/2018


Power consumption is a key challenge for LTE-Advanced or future 5G mobile devices and current power management systems successfully achieve significant power savings. However, these systems are driven by static rules and provide a posteriori responses to traffic and context changes. In this paper, we propose a smart dynamic power management system for cellular modems, extending existing power saving mechanisms by using machine learning-based traffic prediction. With the a priori knowledge of specific scheduling messages, internal device
parameters can be finely tuned to improve the modem power consumption. In order to accurately estimate the power saving potential of several LTE use cases, we build a relevant data set of live network modem traces, as well as a power model of the baseband physical layer and radio frequency components. Subsequently, we propose an evaluation methodology and apply
it to analyze the predictive power management performance in terms of error rate and global power consumption outcome. Our analysis results in maximal power savings of 12% for meaningful traffic scenarios as well as the identification of variables of interest to improve the proposed power manager.

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Last updated on 2018-03-17 at 04:53
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