Multiagent Reinforcement Learning with an Attention Mechanism for
  Improving Energy Efficiency in LoRa Networks
Xu Zhang Ziqi Lin Shimin Gong Bo Gu Dusit Niyato
Long Range (LoRa) wireless technology, characterized by low power consumption
and a long communication range, is regarded as one of the enabling technologies
for the Industrial Internet of Things (IIoT). However, as the network scale
increases, the energy efficiency (EE) of LoRa networks decreases sharply due to
severe packet collisions. To address this issue, it is essential to
appropriately assign transmission parameters such as the spreading factor and
transmission power for each end device (ED). However, due to the sporadic
traffic and low duty cycle of LoRa networks, evaluating the system EE
performance under different parameter settings is time-consuming. Therefore, we
first formulate an analytical model to calculate the system EE. On this basis,
we propose a transmission parameter allocation algorithm based on multiagent
reinforcement learning (MALoRa) with the aim of maximizing the system EE of
LoRa networks. Notably, MALoRa employs an attention mechanism to guide each ED
to better learn how much ''attention'' should be given to the parameter
assignments for relevant EDs when seeking to improve the system EE. Simulation
results demonstrate that MALoRa significantly improves the system EE compared
with baseline algorithms with an acceptable degradation in packet delivery rate
(PDR).