Urabe Ikumi, Li Aohan, Fujisawa Minoru, Kim Song-Ju, Hasegawa Mikio
Department of Electrical Engineering, Tokyo University of Science, Tokyo 125-8585, Japan.
Graduate School of Informatics and Engineering, The University of Electro-Communications, Tokyo 182-8585, Japan.
Sensors (Basel). 2023 Jul 26;23(15):6687. doi: 10.3390/s23156687.
Long-Range (LoRa) devices have been deployed in many Internet of Things (IoT) applications due to their ability to communicate over long distances with low power consumption. The scalability and communication performance of the LoRa systems are highly dependent on the spreading factor (SF) and channel allocations. In particular, it is important to set the SF appropriately according to the distance between the LoRa device and the gateway since the signal reception sensitivity and bit rate depend on the used SF, which are in a trade-off relationship. In addition, considering the surge in the number of LoRa devices recently, the scalability of LoRa systems is also greatly affected by the channels that the LoRa devices use for communications. It was demonstrated that the lightweight decentralized learning-based joint channel and SF-selection methods can make appropriate decisions with low computational complexity and power consumption in our previous study. However, the effect of the location situation of the LoRa devices on the communication performance in a practical larger-scale LoRa system has not been studied. Hence, to clarify the effect of the location situation of the LoRa devices on the communication performance in LoRa systems, in this paper, we implemented and evaluated the learning-based joint channel and SF-selection methods in a practical LoRa system. In the learning-based methods, the channel and SF are decided only based on the ACKnowledge information. The learning methods evaluated in this paper were the Tug of War dynamics, Upper Confidence Bound 1, and ϵ-greedy algorithms. Moreover, to consider the relevance of the channel and SF, we propose a combinational multi-armed bandit-based joint channel and SF-selection method. Compared with the independent methods, the combinations of the channel and SF are set as arms. Conversely, the SF and channel are set as independent arms in the independent methods that are evaluated in our previous work. From the experimental results, we can see the following points. First, the combinatorial methods can achieve a higher frame success rate and fairness than the independent methods. In addition, the FSR can be improved by joint channel and SF selection compared to SF selection only. Moreover, the channel and SF selection dependents on the location situation to a great extent.
由于能够以低功耗进行长距离通信，长距离（LoRa）设备已被部署在许多物联网（IoT）应用中。LoRa系统的可扩展性和通信性能高度依赖于扩频因子（SF）和信道分配。特别是，根据LoRa设备与网关之间的距离适当地设置SF很重要，因为信号接收灵敏度和比特率取决于所使用的SF，它们处于一种权衡关系。此外，考虑到最近LoRa设备数量的激增，LoRa系统的可扩展性也受到LoRa设备用于通信的信道的极大影响。在我们之前的研究中已经证明，基于轻量级分散学习的联合信道和SF选择方法可以以低计算复杂度和功耗做出适当的决策。然而，在实际的大规模LoRa系统中，LoRa设备的位置情况对通信性能的影响尚未得到研究。因此，为了阐明LoRa设备的位置情况对LoRa系统中通信性能的影响，在本文中，我们在实际的LoRa系统中实现并评估了基于学习的联合信道和SF选择方法。在基于学习的方法中，信道和SF仅根据确认信息来决定。本文评估的学习方法是拔河动力学、置信上限1和ε-贪婪算法。此外，为了考虑信道和SF的相关性，我们提出了一种基于组合多臂赌博机的联合信道和SF选择方法。与独立方法相比，信道和SF的组合被设置为臂。相反，在我们之前工作中评估的独立方法中，SF和信道被设置为独立的臂。从实验结果中，我们可以看到以下几点。首先，组合方法比独立方法能够实现更高的帧成功率和公平性。此外，与仅选择SF相比，联合信道和SF选择可以提高帧成功率（FSR）。而且，信道和SF的选择在很大程度上取决于位置情况。
