Name
Title | ||
|---|---|---|
Down-clocking Scheme using Deep Learning for Minimizing Energy Consumption in Wireless Networks |
Abstract | ||
|---|---|---|
Wi-Fi interface is known to consume a lot of energy in mobile devices, and Idle Listening (IL) dominates clients' energy consumption in Wi-Fi. In this paper, we propose IL down-clocking schemes using deep learning model to reduce the energy consumption in IL time. We exploit the orthogonal frequency-division multiplexing (OFDM) subcarrier addressing for the preamble design. To minimize preamble length for energy efficiency, we use a deep learning model with the recurrent neural network (RNN). Our experimental evaluation using OPNET network simulator and USRP/GNU Radio implementation shows that our scheme outperforms the state-of-the-art down-clocking scheme in both energy consumption and network throughput. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/ICAIIC48513.2020.9065016 | 2020 International Conference on Artificial Intelligence in Information and Communication (ICAIIC) |
Keywords | DocType | ISBN |
Wireless Communications,Energy Efficiency,Idle Listening Adapting Clock Rate,OFDM Subcarrier,Deep Learning | Conference | 978-1-7281-4986-8 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jae-Hyeon Park | 1 | 0 | 0.34 |
| Seung Hyun Jeong | 2 | 0 | 0.34 |
| Young-joo Suh | 3 | 478 | 58.07 |