Deep Reinforcement Learning-Based Energy Efficiency Optimization for Flying LoRa Gateways

Document Type

Conference Proceeding

Source of Publication

ICC 2023 - IEEE International Conference on Communications

Publication Date

6-1-2023

Abstract

A resource-constrained unmanned aerial vehicle (UAV) can be used as a flying LoRa gateway (GW) to move inside the target area for efficient data collection and LoRa resource management. In this work, we propose deep reinforcement learning (DRL) to optimize the energy efficiency (EE) in wireless LoRa networks composed of LoRa end devices (EDs) and a flying GW to extend the network lifetime. The trained DRL agent can efficiently allocate the spreading factors (SFs) and transmission powers (TPs) to EDs while considering the air-to-ground wireless link and the availability of SFs. In addition, we allow the flying GW to adjust its optimal policy onboard and perform online resource allocation. This is accomplished through retraining the DRL agent using reduced action space. Simulation results demonstrate that our proposed DRL-based online resource allocation scheme can achieve higher EE in LoRa networks over three benchmark schemes.

ISBN

978-1-5386-7462-8

Publisher

IEEE

Volume

00

First Page

6157

Last Page

6162

Disciplines

Computer Sciences

Keywords

Wireless communication, Simulation, Interference, Logic gates, Data collection, Autonomous aerial vehicles, Throughput

Indexed in Scopus

no

Open Access

yes

Open Access Type

Green: A manuscript of this publication is openly available in a repository

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