Deep Reinforcement Learning-Based Energy Efficiency Optimization for Flying LoRa Gateways
Source of Publication
ICC 2023 - IEEE International Conference on Communications
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.
Wireless communication, Simulation, Interference, Logic gates, Data collection, Autonomous aerial vehicles, Throughput
Jouhari, Mohammed; Ibrahimi, Khalil; Othman, Jalel Ben; and Amhoud, El Mehdi, "Deep Reinforcement Learning-Based Energy Efficiency Optimization for Flying LoRa Gateways" (2023). All Works. 6148.
Indexed in Scopus
Open Access Type
Green: A manuscript of this publication is openly available in a repository