利用学习的优化方法用于具有过时状态信息的能量收集设备
Learning Aided Optimization for Energy Harvesting Devices with Outdated State Information
AI总结：
本文研究了在有限容量电池下的无线能量收集设备的效用最优功率控制问题，通过结合Zinkevich的在线梯度学习技术与Lyapunov机会优化中的漂移加惩罚技术，提出了一种学习辅助算法，在使用容量为O(1/ε)的电池的情况下，能够实现效用在O(ε)范围内接近最优，且具有低复杂度，无需了解系统状态或其概率分布。
摘要：
本文考虑了在有限容量电池下的无线能量收集设备的效用最优功率控制问题。底层无线环境的能量分布信息和可收集能量是未知的，设备控制器仅知道过时的系统状态信息。这种情形与Lyapunov机会优化和在线学习有相似之处，但又与两者不同。通过将Zinkevich的在线梯度学习技术与Lyapunov机会优化中的漂移加惩罚技术相结合，本文提出了一种学习辅助算法，通过使用容量为O(1/ε)的电池，可以实现效用在O(ε)范围内接近最优，对于任意的ε>0。所提出的算法具有低复杂度，并基于系统历史做出功率投资决策，而无需了解系统状态或其概率分布。
英文摘要：
This paper considers utility optimal power control for energy harvesting wireless devices with a finite capacity battery. The distribution information of the underlying wireless environment and harvestable energy is unknown and only outdated system state information is known at the device controller. This scenario shares similarity with Lyapunov opportunistic optimization and online learning but is different from both. By a novel combination of Zinkevich's online gradient learning technique and the drift-plus-penalty technique from Lyapunov opportunistic optimization, this paper proposes a learning-aided algorithm that achieves utility within $O(ε)$ of the optimal, for any desired $ε>0$, by using a battery with an $O(1/ε)$ capacity. The proposed algorithm has low complexity and makes power investment decisions based on system history, without requiring knowledge of the system state or its probability distribution.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
