基于SADQN的剩余能量意识光束成型用于LoRa启用的RF能量采集,用于耐灾地下采矿网络
Hilary Kelechi Anabi1, Samuel Frimpong1, Sanjay Madria2
1Department of Mining and Explosives Engineering, Missouri University of Science and Technology, Rolla, MO 65409, USA.
Sensors (Basel, Switzerland)
|January 28, 2026
概括
本研究介绍了一种安全的自适应深度强化学习框架,用于地下灾害网络中的能量束形成. 新方法提高了能源采集和网络稳定性,这对于灾后可靠的通信至关重要.
科学领域:
- 无线通信网络 无线通信网络
- 强化学习是一种强化学习.
- 灾害管理 灾害管理
背景情况:
- 无线通信网络 (WPCN) 面临着灾后地下矿山的挑战,包括能源限制和有效的电力传输需求.
- 识别能源受限制的节点,避免复杂的通道状态信息 (CSI) 获取,对于保持网络功能至关重要.
研究的目的:
- 开发一个具有适应性和安全意识的深度强化学习 (DRL) 框架,用于在LoRa支持的地下灾难网络中进行能量束形成.
- 为应对能源受限节点,CSI采购和远程射频无线电力传输 (LoRa-RFWPT) 的挑战.
主要方法:
- 引入了一个安全的自适应深度Q网络 (SADQN),结合了剩余能源意识和双变量更新.
- 制定了这个问题作为一个受约束的马尔科夫决策过程 (CMDP),并使用拉格朗的放松来进行NP-hard受约束的强化学习 (CRL).
- 开发了灾后地下矿山环境的数学模型.
主要成果:
- 在SADQN中,累计收获的能源增加了11% (相对于DQN),15% (相对于Safe-DQN) 和40% (相对于PSO).
- 保持公平性指数在0.90以上,并显示出更快的趋同 (27%与Safe-DQN相比,43%与DQN相比).
- 与基线算法相比,表现出卓越的稳定性,性能差异明显较低.
结论:
- 在具有挑战性的地下灾害场景中,SADQN框架为能量束形成提供了强大的解决方案.
- 该方法增强了能源采集,网络公平性和运行稳定性,这对于安全关键应用至关重要.
- SADQN为复杂的CRL问题提供了近乎最佳的解决方案,提高了WPCN的效率和可靠性.
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