Deep Reinforcement Learning-based Energy Efficiency Optimization For Flying LoRa Gateways

TL;DR

This paper introduces a deep reinforcement learning approach for optimizing energy efficiency in flying LoRa gateways, enabling adaptive resource management and extending network lifetime in UAV-based wireless sensor networks.

Contribution

It proposes a novel DRL-based online resource allocation method for flying LoRa gateways, considering air-ground links and dynamic SFs and TPs, with retraining for onboard policy adjustment.

Findings

Achieves higher energy efficiency compared to benchmark schemes

Enables online adaptation of resource allocation policies

Demonstrates improved network lifetime in simulations

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.