Proactive Wake-up Scheduler based on Recurrent Neural Networks
Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review
Details
| Original language | English |
|---|---|
| Title of host publication | 2020 IEEE International Conference on Communications, ICC 2020 - Proceedings |
| Publisher | IEEE |
| Number of pages | 6 |
| ISBN (Electronic) | 9781728150895 |
| DOIs | |
| Publication status | Published - Jun 2020 |
| Publication type | A4 Article in a conference publication |
| Event | IEEE International Conference on Communications - Duration: 7 Jun 2020 → 11 Jun 2020 |
Publication series
| Name | IEEE International Conference on Communications |
|---|---|
| Volume | 2020-June |
| ISSN (Print) | 1550-3607 |
Conference
| Conference | IEEE International Conference on Communications |
|---|---|
| Period | 7/06/20 → 11/06/20 |
Abstract
Recently, wake-up scheme has been proposed to enhance the energy-efficiency of 5G mobile devices and prolong its battery lifetime while reducing the buffering delay. The existing wake-up optimization mechanisms use off-line methods and are tied to specific traffic models. In this paper, a novel concept of wake-up scheduling is introduced to further improve the energy-efficiency of mobile devices and to deal with realistic traffic. The main idea is to use a fixed configuration of the wake-up scheme and adjust the scheduling of the wake-up signals dynamically. For this, a proactive wake-up scheduler is proposed to take online decisions based on traffic prediction. Towards this end, a framework to predict packet arrivals based on recurrent neural networks is developed. Numerical results show that for given delay requirements of video, audio streaming, and mixed traffic flow, the proactive wake-up scheduler reduces the power consumption of the baseline wake-up scheme without scheduler by up to 36%, 28% and 9%, respectively.
ASJC Scopus subject areas
Keywords
- 5G, energy efficiency, LSTM, machine learning, wake-up scheme