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<p>LoRaWAN is a popular IoT wireless access technology that is characterized by low power and long range. This paper proposes a new decentralized access scheme that utilizes Reinforcement Learning to enhance the capacity of the multichannel ALOHA used by LoRaWAN. The performance of the proposed scheme is evaluated via extensive simulations and compared to the standard multichannel ALOHA in LoRaWAN. The simulation results demonstrate that the new scheme can achieve a throughput of <InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(82\%\)</EquationSource> </InlineEquation> compared to <InlineEquation ID="IEq2"> <EquationSource Format="TEX">\(60\%\)</EquationSource> </InlineEquation> by the conventional multi-channel ALOHA scheme with. Moreover, collision rate and power consumption are both reduced substantially.</p>

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Q-Learning-Based Multi-channel ALOHA MAC for LoRaWAN

  • 期刊论文

  • 发表日期：

    2025年11月6日

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  • Mohamed Osman Omar,
  • Louai Al-Awami,
  • Uthman Baroudi,
  • Akram Fadhl Ahmed

摘要

LoRaWAN is a popular IoT wireless access technology that is characterized by low power and long range. This paper proposes a new decentralized access scheme that utilizes Reinforcement Learning to enhance the capacity of the multichannel ALOHA used by LoRaWAN. The performance of the proposed scheme is evaluated via extensive simulations and compared to the standard multichannel ALOHA in LoRaWAN. The simulation results demonstrate that the new scheme can achieve a throughput of \(82\%\) compared to \(60\%\) by the conventional multi-channel ALOHA scheme with. Moreover, collision rate and power consumption are both reduced substantially.