Recently,one of the main challenges facing the smart grid is insufficient computing resources and intermittent energy supply for various distributed components(such as monitoring systems for renewable energy power sta...Recently,one of the main challenges facing the smart grid is insufficient computing resources and intermittent energy supply for various distributed components(such as monitoring systems for renewable energy power stations).To solve the problem,we propose an energy harvesting based task scheduling and resource management framework to provide robust and low-cost edge computing services for smart grid.First,we formulate an energy consumption minimization problem with regard to task offloading,time switching,and resource allocation for mobile devices,which can be decoupled and transformed into a typical knapsack problem.Then,solutions are derived by two different algorithms.Furthermore,we deploy renewable energy and energy storage units at edge servers to tackle intermittency and instability problems.Finally,we design an energy management algorithm based on sampling average approximation for edge computing servers to derive the optimal charging/discharging strategies,number of energy storage units,and renewable energy utilization.The simulation results show the efficiency and superiority of our proposed framework.展开更多
空间信号源数检测是阵列信号处理的关键问题之一,该文针对低信噪比下传统检测方法的性能差的问题,提出了一种基于近似特征向量的检测新方法DTAE(Detection Technique based on Approximate Eigenvectors)来改善低信噪比下传感器阵列的...空间信号源数检测是阵列信号处理的关键问题之一,该文针对低信噪比下传统检测方法的性能差的问题,提出了一种基于近似特征向量的检测新方法DTAE(Detection Technique based on Approximate Eigenvectors)来改善低信噪比下传感器阵列的信源数检测性能。该方法首先利用波束形成器在空间做预扫描来估计信号群中心的位置,以这些位置作为参考方向计算接收数据协方差矩阵的特征向量的近似值,然后使用特征向量的近似值对阵列输出数据加权,最后计算加权输出数据的频域峰值-平均功率比值从而估计信号源的个数。仿真结果表明,提出的新方法在低信噪比下的检测性能显著优于AIC(Akaike Information Criterion)等方法,有一定的工程应用价值。展开更多
基金supported in part by the National Natural Science Foundation of China under Grant No.61473066in part by the Natural Science Foundation of Hebei Province under Grant No.F2021501020+2 种基金in part by the S&T Program of Qinhuangdao under Grant No.202401A195in part by the Science Research Project of Hebei Education Department under Grant No.QN2025008in part by the Innovation Capability Improvement Plan Project of Hebei Province under Grant No.22567637H
文摘Recently,one of the main challenges facing the smart grid is insufficient computing resources and intermittent energy supply for various distributed components(such as monitoring systems for renewable energy power stations).To solve the problem,we propose an energy harvesting based task scheduling and resource management framework to provide robust and low-cost edge computing services for smart grid.First,we formulate an energy consumption minimization problem with regard to task offloading,time switching,and resource allocation for mobile devices,which can be decoupled and transformed into a typical knapsack problem.Then,solutions are derived by two different algorithms.Furthermore,we deploy renewable energy and energy storage units at edge servers to tackle intermittency and instability problems.Finally,we design an energy management algorithm based on sampling average approximation for edge computing servers to derive the optimal charging/discharging strategies,number of energy storage units,and renewable energy utilization.The simulation results show the efficiency and superiority of our proposed framework.
文摘空间信号源数检测是阵列信号处理的关键问题之一,该文针对低信噪比下传统检测方法的性能差的问题,提出了一种基于近似特征向量的检测新方法DTAE(Detection Technique based on Approximate Eigenvectors)来改善低信噪比下传感器阵列的信源数检测性能。该方法首先利用波束形成器在空间做预扫描来估计信号群中心的位置,以这些位置作为参考方向计算接收数据协方差矩阵的特征向量的近似值,然后使用特征向量的近似值对阵列输出数据加权,最后计算加权输出数据的频域峰值-平均功率比值从而估计信号源的个数。仿真结果表明,提出的新方法在低信噪比下的检测性能显著优于AIC(Akaike Information Criterion)等方法,有一定的工程应用价值。
