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

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.

@article{yang2025_2504.13193,
  title={ HEAT:History-Enhanced Dual-phase Actor-Critic Algorithm with A Shared Transformer },
  author={ Hong Yang },
  journal={arXiv preprint arXiv:2504.13193},
  year={ 2025 }
}