Publication detail

Learning-Aided Multi-RAT Operation for Battery Lifetime Extension in LPWAN Systems

ŠTŮSEK, M. MOLTCHANOV, D. MAŠEK, P. HOŠEK, J. ANDREEV, S. KOUCHERYAVY, Y.

Original Title

Learning-Aided Multi-RAT Operation for Battery Lifetime Extension in LPWAN Systems

English Title

Learning-Aided Multi-RAT Operation for Battery Lifetime Extension in LPWAN Systems

Type

conference paper

Language

en

Original Abstract

End-device (ED) lifetime is considered to be a crucial design factor in radio systems for massive machine-type communications. This parameter is heavily impacted by the continuously changing propagation conditions between the ED and the base station. In this paper, to extend the ED lifetime, we consider equipping a single ED with multiple low-power wide-area network (LPWAN) technologies to dynamically select the one with lower energy consumption. To facilitate this process, we propose to employ reinforcement learning (RL) algorithms. Assessing the resultant performance, we conduct two large-scale measurement campaigns that characterize the ED power consumption and the time-dependent propagation conditions for NB-IoT, Sigfox, and LoRaWAN technologies. Our numerical results demonstrate that the designed schemes effectively reduce ED power consumption by timely reacting to the varying radio conditions. Consequently, the ED lifetime expectancy is prolonged by around 10%. For instance, the Thompson sampling technique delivers the most consistent results by outperforming its counterparts and allowing to exploit up to 99% of the theoretical gains while converging over only 25-50 samples.

English abstract

End-device (ED) lifetime is considered to be a crucial design factor in radio systems for massive machine-type communications. This parameter is heavily impacted by the continuously changing propagation conditions between the ED and the base station. In this paper, to extend the ED lifetime, we consider equipping a single ED with multiple low-power wide-area network (LPWAN) technologies to dynamically select the one with lower energy consumption. To facilitate this process, we propose to employ reinforcement learning (RL) algorithms. Assessing the resultant performance, we conduct two large-scale measurement campaigns that characterize the ED power consumption and the time-dependent propagation conditions for NB-IoT, Sigfox, and LoRaWAN technologies. Our numerical results demonstrate that the designed schemes effectively reduce ED power consumption by timely reacting to the varying radio conditions. Consequently, the ED lifetime expectancy is prolonged by around 10%. For instance, the Thompson sampling technique delivers the most consistent results by outperforming its counterparts and allowing to exploit up to 99% of the theoretical gains while converging over only 25-50 samples.

Keywords

End-device lifetime; Energy consumption optimization; LPWAN; Multi-armed bandit; Multi-RAT; Reinforcement learning

Released

07.10.2020

ISBN

978-1-7281-9281-9

Book

2020 12th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops (ICUMT).

Pages from

1

Pages to

7

Pages count

7

URL

Documents

BibTex


@inproceedings{BUT165816,
  author="Martin {Štůsek} and Pavel {Mašek} and Jiří {Hošek}",
  title="Learning-Aided Multi-RAT Operation for Battery Lifetime Extension in LPWAN Systems",
  annote="End-device (ED) lifetime is considered to be a crucial design factor in radio systems for massive machine-type communications. This parameter is heavily impacted by the continuously changing propagation conditions between the ED and the base station. In this paper, to extend the ED lifetime, we consider equipping a single ED with multiple low-power wide-area network (LPWAN) technologies to dynamically select the one with lower energy consumption. To facilitate this process, we propose to employ reinforcement learning (RL) algorithms. Assessing the resultant performance, we conduct two large-scale measurement campaigns that characterize the ED power consumption and the time-dependent propagation conditions for NB-IoT, Sigfox, and LoRaWAN technologies. Our numerical results demonstrate that the designed schemes effectively reduce ED power consumption by timely reacting to the varying radio conditions. Consequently, the ED lifetime expectancy is prolonged by around 10%. For instance, the Thompson sampling technique delivers the most consistent results by outperforming its counterparts and allowing to exploit up to 99% of the theoretical gains while converging over only 25-50 samples.",
  booktitle="2020 12th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops (ICUMT).",
  chapter="165816",
  doi="10.1109/ICUMT51630.2020.9222440",
  howpublished="online",
  year="2020",
  month="october",
  pages="1--7",
  type="conference paper"
}