HEAT:History-Enhanced Dual-phase Actor-Critic Algorithm with A Shared Transformer

Hong Yang
4/13/2025
cs.NIcs.AIcs.LG

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Abstract

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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Cite this paper

@article{yang2025heathistoryenhanced,
  title  = {HEAT:History-Enhanced Dual-phase Actor-Critic Algorithm with A Shared Transformer},
  author = {Hong Yang},
  year   = {2025},
  eprint = {2504.13193},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2504.13193v1}
}

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