| Autor: |
Xiaoyan Wang, Masahiro Umehira, Hiroyuki Otsu, Takyuya Kawatani, Shigeki Takeda |
| Jazyk: |
angličtina |
| Rok vydání: |
2018 |
| Předmět: |
|
| Zdroj: |
IEEE Access, Vol 6, Pp 34324-34333 (2018) |
| Druh dokumentu: |
article |
| ISSN: |
2169-3536 |
| DOI: |
10.1109/ACCESS.2018.2849094 |
| Popis: |
Broadband Internet access in the building has fundamentally changed almost every aspect of our lives. As the growing use of indoor portable systems and devices, saving their energy consumption becomes an interesting and important issue for smart buildings. Recently, multi-band WLAN where 2.4/5-GHz and 60-GHz bands coexist is a promising solution to offer both ultra-high speed and robust wireless connections. For a multi-band WLAN end device, detecting the available service areas in an energy efficient way is of great importance. In the existing systems, the RF units of device need to be turned on and kept listening all the time, which leads to substantial energy consumption overhead. To solve this problem, this paper proposes an energy efficient learning-based indoor multi-band WLAN system, in which the end device predicts the distinct service areas by learning the influences of reflected waves in buildings. We have performed extensive experiments in different indoor environments, and the evaluation results demonstrate that the proposed mechanism could substantially improve the performance compared with the existing approaches. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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