Reinforcement Learning for Hybrid Energy LoRa Wireless Networks

Rami Hamdi, Emna Baccour, Aiman Erbad, Marwa Qaraqe, Mounir Hamdi

Research output: Contribution to journalConference articlepeer-review

1 Citation (Scopus)

Abstract

LoRa supports the exponential growth of connected devices. In this paper, we investigate green LoRa wireless networks powered by both the grid power and a renewable energy source. The grid power compensates for the randomness and intermittency of the harvested energy. We propose an efficient and smart resource management scheme of the limited number of channels and spreading factors (SFs) with the objective of improving the LoRa gateway (LG) energy efficiency. We formulate the problem of grid power consumption minimization while satisfying the quality of service demands. The optimal resource management problem is solved by decoupling the formulated problem into two sub-problems: channel and SF assignment problem and energy management problem. Next, we develop an adaptable resource management schemes based on Reinforcement Learning (RL) taking into account the channel and energy correlation. Simulations results show that the proposed resource management schemes offer efficient use of renewable energy in LoRa wireless networks.

Original languageEnglish
JournalProceedings - IEEE Global Communications Conference, GLOBECOM
DOIs
Publication statusPublished - 2021
Event2021 IEEE Global Communications Conference, GLOBECOM 2021 - Madrid, Spain
Duration: 7 Dec 202111 Dec 2021

Keywords

  • LoRa
  • energy harvesting
  • reinforcement learning
  • resource management

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