AI 中文总结
该研究针对动态LoRa网络，提出结合汤普森采样与Schwarz信息准则的轻量分布式学习方法，实现分散资源分配，提升了传输成功率与能效。
AI 中文摘要
本文提出一种轻量级分布式学习方法，用于在远程（Long-Range，LoRa）网络中选择传输参数，以适应动态变化的通信环境。该方法采用汤普森采样（Thompson Sampling，TS）进行传输参数选择，同时使用Schwarz信息准则（Schwarz Information Criterion，SIC）检测环境变化。TS是一种强化学习方法，通过基于概率分布更新参数，有效平衡探索与利用，即使在少量试验下也能表现出稳定性能，因此非常适合内存容量和计算资源有限的LoRa终端设备（End Devices，EDs）。此外，针对基于TS的方法严重依赖过去学习历史，难以快速适应通信环境突变的问题，本文在方法中集成了基于SIC的统计变化检测机制；选用SIC是因为其检测环境变化的计算成本低，适合在资源受限的LoRa EDs上实现。当SIC检测到通信环境变化时，会重置TS的学习历史，从而在新环境条件下实现快速重新学习。此外，为实现完全分布式的通信参数选择，同时提升传输可靠性和能效，该方法仅依赖确认（Acknowledgment，ACK）反馈和已选传输参数。实验结果表明，在高密度动态LoRa网络场景下，与传统的上置信界（Upper Confidence Bound，UCB）1调优方案相比，本文提出的方法将传输成功率从64.0%提升至71.1%，能效从293.9 bit/J提高到328.3 bit/J。
英文摘要
This paper proposes a lightweight distributed learning method for selecting transmission parameters in Long-Range (LoRa) networks that adapts to dynamically changing communication environments. In the proposed method, the Thompson Sampling (TS) is adopted for transmission parameter selection, whereas the Schwarz Information Criterion (SIC) is employed for environmental change detection. TS is a reinforcement learning approach that effectively balances exploration and exploitation by updating parameters based on probability distributions. Additionally, it demonstrates stable performance even with a small number of trials, thereby making it well-suited for LoRa end devices (EDs) with limited memory capacity and computational resources. Furthermore, to address the issue that TS-based methods strongly depend on past learning histories and therefore adapt slowly to abrupt changes in communication environments, a statistical change detection mechanism based on the SIC is integrated into our proposed method. SIC is adopted because it can detect environmental changes with low computational cost and is suitable for implementation on resource-constrained LoRa EDs. When a change in the communication environment is detected by SIC, the learning history of TS is reset, thereby enabling rapid re-learning under new environmental conditions. Moreover, to achieve fully distributed communication parameter selection while enhancing transmission reliability and energy efficiency, the proposed method relies solely on Acknowledgment (ACK) feedback and the selected transmission parameters. Experimental results demonstrate that the proposed method improves the transmission success rate from 64.0% to 71.1% and increases energy efficiency from 293.9 bit/J to 328.3 bit/J compared with the conventional Upper Confidence Bound (UCB)1-tuned scheme under high-density dynamic LoRa networks.
Comments13 pages, 7 figures
