HEAT:History-Enhanced Dual-phase Actor-Critic Algorithm with A Shared Transformer
Fuente:
arXiv
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866915249764958208 |
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| author | Yang, Hong |
| author_facet | Yang, Hong |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_13193 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | HEAT:History-Enhanced Dual-phase Actor-Critic Algorithm with A Shared Transformer Yang, Hong Networking and Internet Architecture Artificial Intelligence Machine Learning 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. |
| title | HEAT:History-Enhanced Dual-phase Actor-Critic Algorithm with A Shared Transformer |
| topic | Networking and Internet Architecture Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2504.13193 |