.png){kind=icon}
.png){kind=icon}
Return
Deep Reinforcement Learning Based Resource Allocation for LoRaWAN
.png){kind=icon}
PRE
AI
DOI：10.1109/VTC2022-Fall57202.2022.10012698.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
It is predicted that the number of Internet of Things (IoT) devices will be more than 75 billion by 2025, where a large portion of IoT devices will be long-range (LoRa) powered by batteries. The battery lifetime limitations and the spectrum shortage have been the main problems in realizing LoRa wide area network (LoRAWAN) for the devices in hard-to-reach areas. The dynamic spectrum access technique has gained tremendous research interest as a promising paradigm due to its outstanding performance in improving spectrum efficiency. How to realize intelligent resource allocation (RA) to avoid collisions among IoT devices with low energy consumption is an important problem in LoRaWAN. However, either synchronization and prior information estimation, such as channel state information (CSI), are required, or the energy consumption of LoRa devices is not considered in related work, which may decrease the energy efficiency of the LoRa devices. In addition, the necessary prior information may be challenging to obtain in future networks. To address these issues, we propose a deep Q learning-based RA (DQLRA) method for LoRaWAN. In our proposed method, the gateway (GW) trains the deep neural network (DNN) only based on the transmission state, i.e., transmission failure or success, and the corresponding device number of each LoRa device. Then, each LoRa device can make decisions based on its device number and ACK or NACK information using the trained DNN. Synchronization and prior information estimation are not required in our proposed method, which may improve the energy efficiency of IoT devices. Simulation results show that the proposed method can achieve the optimal frame success rate (FSR) in most scenarios.
Keywords:
LoRaWAN
Resource Management
Deep Reinforcement Learning
Energy Efficiency
Journal
I
IF:
0
Papers:
123
Citations:
0