HEAT提升物联网网络性能，兼顾上下行参数与历史数据。
HEAT:History-Enhanced Dual-phase Actor-Critic Algorithm with A Shared Transformer
- 用共享Transformer融合历史与实时数据，双阶段优化
- 包成功率达95%提升，能效提高95%
- 适合物联网通信优化与强化学习研究者
针对单网关LoRaWAN网络，本文提出一种基于共享Transformer的历史增强双阶段演员-评论家算法（HEAT），以提升网络性能。该算法同时考虑上行与常被忽略的下行参数，有效结合离线与在线强化学习，利用历史数据和实时交互提升模型表现。此外，本文开发了开源的LoRaWAN网络仿真器LoRaWANSim，支持多通道、多解调器及双向通信，并考虑解调器锁相效应。仿真实验表明，相较于所有对比算法的最佳结果，HEAT分别将包成功率和能效提升了15%和95%。
原文摘要 · Abstract (English)
For a single-gateway LoRaWAN network, this study proposed a history-enhanced two-phase actor-critic algorithm with a shared transformer algorithm (HEAT) to improve network performance. HEAT considers uplink parameters and often neglected downlink parameters, and effectively integrates offline and online reinforcement learning, using historical data and real-time interaction to improve model performance. In addition, this study developed an open source LoRaWAN network simulator LoRaWANSim. The simulator considers the demodulator lock effect and supports multi-channel, multi-demodulator and bidirectional communication. Simulation experiments show that compared with the best results of all compared algorithms, HEAT improves the packet success rate and energy efficiency by 15% and 95%, respectively.
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