A Lightweight Transmission Parameter Selection Scheme Using Reinforcement Learning for LoRaWAN
A Lightweight Transmission Parameter Selection Scheme Using Reinforcement Learning for LoRaWAN
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DOI：
10.48550/arxiv.2208.01824
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发表时间：
2022-08
期刊：
影响因子：
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通讯作者：
Aohan Li;Ikumi Urabe;Minoru Fujisawa;So Hasegawa;Hiroyuki Yasuda;Song-Ju Kim;M. Hasegawa
中科院分区：
文献类型：
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作者：
Aohan Li;Ikumi Urabe;Minoru Fujisawa;So Hasegawa;Hiroyuki Yasuda;Song-Ju Kim;M. Hasegawa
到2023年，物联网设备的数量预计将达到1250亿。物联网设备的增长将加剧设备之间的冲突，降低通信性能。选择合适的传输参数，如信道和扩频因子（SF），可以有效地减少远程（LoRa）设备之间的冲突。然而，目前文献中提出的大多数方案都不容易在计算复杂性和内存有限的物联网设备上实现。为了解决这个问题，我们提出了一种轻量级的传输参数选择方案，即使用低功耗广域网（LoRaWAN）强化学习的联合信道和SF选择方案。在该方案中，仅使用确认（ACK）信息就可以通过简单的四种算术运算选择合适的传输参数。此外，我们从理论上分析了所提方案的计算复杂度和内存需求，验证了所提方案能够以极低的计算复杂度和内存需求选择传输参数。此外，在实际的LoRa设备上进行了大量的实验，以评估我们提出的方案的有效性。实验结果显示了以下主要现象。(1)与其他轻量级传输参数选择方案相比，本文提出的方案可以有效地避免LoRa设备之间的冲突，而不影响可用信道的变化。(2)与只选择接入信道相比，可以通过选择接入信道并使用顺位信号来提高帧成功率。(3)由于相邻信道之间存在干扰，增大相邻可用信道的间隔可以提高FSR和公平性。
The number of IoT devices is predicted to reach 125 billion by 2023. The growth of IoT devices will intensify the collisions between devices, degrading communication performance. Selecting appropriate transmission parameters, such as channel and spreading factor (SF), can effectively reduce the collisions between long-range (LoRa) devices. However, most of the schemes proposed in the current literature are not easy to implement on an IoT device with limited computational complexity and memory. To solve this issue, we propose a lightweight transmission-parameter selection scheme, i.e., a joint channel and SF selection scheme using reinforcement learning for low-power wide area networking (LoRaWAN). In the proposed scheme, appropriate transmission parameters can be selected by simple four arithmetic operations using only Acknowledge (ACK) information. Additionally, we theoretically analyze the computational complexity and memory requirement of our proposed scheme, which verified that our proposed scheme could select transmission parameters with extremely low computational complexity and memory requirement. Moreover, a large number of experiments were implemented on the LoRa devices in the real world to evaluate the effectiveness of our proposed scheme. The experimental results demonstrate the following main phenomena. (1) Compared to other lightweight transmission-parameter selection schemes, collisions between LoRa devices can be efficiently avoided by our proposed scheme in LoRaWAN irrespective of changes in the available channels. (2) The frame success rate (FSR) can be improved by selecting access channels and using SFs as opposed to only selecting access channels. (3) Since interference exists between adjacent channels, FSR and fairness can be improved by increasing the interval of adjacent available channels.