Learning Aided Optimization for Energy Harvesting Devices with Outdated State Information
Learning Aided Optimization for Energy Harvesting Devices with Outdated State Information
复制标题
DOI：
10.1109/infocom.2018.8485833
复制
发表时间：
2018-01
期刊：
影响因子：
--
通讯作者：
Hao Yu;M. Neely
中科院分区：
文献类型：
--
作者：
Hao Yu;M. Neely
本文研究了有限容量电池的无线能量采集设备的效用最优功率控制问题。底层无线环境和可收获能量的分布信息是未知的，并且在设备控制器处仅知道过时的系统状态信息。这个场景与李亚普诺夫机会主义优化和在线学习有相似之处，但不同于两者。通过Zinkevich的在线梯度学习技术和李亚普诺夫机会优化中的漂移加惩罚技术的新组合，提出了一种学习辅助算法，该算法使用容量为0(1/∊)的电池，对于任何期望的∊>；0，其效用都在最优值的O(∊)以内。该算法复杂度低，不需要知道系统状态或其概率分布，根据系统历史进行电力投资决策。
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 The distribution information of the underlying wireless environment and harvestable energy is unknown and only outdated system state information is known at the device controller.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 (∊) of the optimal, for any desired ∊ > 0, by using a battery with an 0 (1/∊) 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.