English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Reducing the energy consumption of mobile phones is a crucial design goal forcellular modem solutions for LTE and 5G standards. In addition to improving thepower efficiency of components through structural and technological advances,optimizing the energy efficiency through improved dynamic power management isan integral part in contemporary hardware design. Most techniques targetingmobile devices proposed so far, however, are purely reactive in powering downand up system components. Promising approaches extend this, by predicting andusing information from the environment and the communication protocol to takeproactive decisions. In this paper, we propose and compare two proactivealgorithmic approaches for light-weight machine learning to predict the controlinformation needed to allow a mobile device to go to sleep states more often,e.g., in time slots of transmission inactivity in a cell. The first approach isbased on supervised learning, the second one based on reinforcement learning.As the implementation of learning techniques also creates energy and resourcecosts, both approaches are carefully evaluated not only in terms of predictionaccuracy, but also overall energy savings. Using the presented technique, weobserve achievable energy savings of up to 17%.

Adaptive Predictive Power Management for Mobile LTE Devices