Detail publikace

Performance Assessment of Reinforcement Learning Policies for Battery Lifetime Extension in Mobile Multi-RAT LPWAN Scenarios

ŠTŮSEK, M. MAŠEK, P. MOLTCHANOV, D. STEPANOV, N. HOŠEK, J. KOUCHERYAVY, Y.

Originální název

Performance Assessment of Reinforcement Learning Policies for Battery Lifetime Extension in Mobile Multi-RAT LPWAN Scenarios

Typ

článek v časopise ve Web of Science, Jimp

Jazyk

angličtina

Originální abstrakt

Considering the dynamically changing nature of the radio propagation environment, the envisioned battery lifetime of the end device (ED) for massive machine-type communication (mMTC) stands for a critical challenge. As the selected radio technology bounds the battery lifetime, the possibility of choosing among several low-power wide-area (LPWAN) technologies integrated at a single ED may dramatically improve its lifetime. In this paper, we propose a novel approach of battery lifetime extension utilizing reinforcement learning (RL) policies. Notably, the system assesses the radio environment conditions and assigns the appropriate rewards to minimize the overall power consumption and increase reliability. To this aim, we carry out extensive propagation and power measurements campaigns at the city-scale level and then utilize these results for composing real-life use-cases for static and mobile deployments. Our numerical results show that RL-based techniques allow for a noticeable increase in EDs' battery lifetime when operating in multi-RAT mode. Furthermore, out of all considered schemes, the performance of the weighted average policy shows the most consistent results for both considered deployments. Specifically, all RL policies can achieve 90% of their maximum gain during the initialization phase for the stationary EDs while utilizing less than 50 messages. Considering the mobile deployment, the improvements in battery lifetime could reach 200%.

Klíčová slova

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

Autoři

ŠTŮSEK, M.; MAŠEK, P.; MOLTCHANOV, D.; STEPANOV, N.; HOŠEK, J.; KOUCHERYAVY, Y.

Vydáno

12. 8. 2022

Nakladatel

Institute of Electrical and Electronics Engineers Inc.

ISSN

2327-4662

Periodikum

IEEE Internet of Things Journal

Ročník

9

Číslo

24

Stát

Spojené státy americké

Strany od

25581

Strany do

25595

Strany počet

15

URL

https://ieeexplore.ieee.org/document/9854077

BibTex

@article{BUT178836,
  author="Martin {Štůsek} and Pavel {Mašek} and Dmitri {Moltchanov} and Nikita {Stepanov} and Jiří {Hošek} and Yevgeni {Koucheryavy}",
  title="Performance Assessment of Reinforcement Learning Policies for Battery Lifetime Extension in Mobile Multi-RAT LPWAN Scenarios",
  journal="IEEE Internet of Things Journal",
  year="2022",
  volume="9",
  number="24",
  pages="25581--25595",
  doi="10.1109/JIOT.2022.3197834",
  issn="2327-4662",
  url="https://ieeexplore.ieee.org/document/9854077"
}