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arXiv · 2504.13193

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

Hong Yang
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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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BibTeXRIS

Hong Yang. 2025-04-13. HEAT:History-Enhanced Dual-phase Actor-Critic Algorithm with A Shared Transformer. https://arxiv.org/abs/2504.13193

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