This paper proposes a lightweight distributed learning method for transmission parameter selection in Long Range (LoRa) networks that can adapt to dynamic communication environments. In the proposed method, each LoRa End Device (ED) employs the Upper Confidence Bound (UCB)1-tuned algorithm to select transmission parameters including channel, transmission power, and bandwidth. The transmission parameters are selected based on the ACKnowledgment (ACK) feedback returned from the gateway after each transmission and the corresponding transmission energy consumption. Hence, it enables devices to simultaneously optimize transmission success rate and energy efficiency in a fully distributed manner. However, although UCB1-tuned based method is effective under stationary conditions, it suffers from slow adaptation in dynamic environments due to its strong reliance on historical observations. To address this limitation, we integrate the Schwarz Information Criterion (SIC) to our proposed method. SIC is adopted because it enables low-cost detection of changes in the communication environment, making it suitable for implementation on resource-constrained LoRa EDs. When a change is detected by SIC, the learning history of UCB1-tuned is reset, allowing rapid re-learning under the new conditions. Experimental results using real LoRa devices demonstrate that the proposed method achieves superior transmission success rate, energy efficiency, and adaptability compared with the conventional UCB1-tuned algorithm without SIC.

翻译：本文提出一种适用于动态通信环境的轻量级分布式学习方法，用于长距离（LoRa）网络中的传输参数选择。在所提方法中，每个LoRa终端设备（ED）采用上置信界（UCB）1-tuned算法选择传输参数，包括信道、发射功率和带宽。传输参数的选择基于每次传输后网关返回的确认（ACK）反馈及相应的传输能耗。因此，该方法使设备能够以完全分布式的方式同时优化传输成功率和能量效率。然而，尽管基于UCB1-tuned的方法在稳态条件下表现良好，但由于其高度依赖历史观测值，在动态环境中适应性较慢。为解决此局限性，我们将施瓦茨信息准则（SIC）集成到所提方法中。采用SIC是因为它能够以较低成本检测通信环境的变化，适合在资源受限的LoRa ED上实现。当SIC检测到环境变化时，UCB1-tuned的学习历史将被重置，从而在新条件下实现快速重新学习。使用真实LoRa设备进行的实验结果表明，与未集成SIC的传统UCB1-tuned算法相比，所提方法在传输成功率、能量效率和适应性方面均表现出更优性能。