Learning-based Distributed Detection with Energy Harvesting
Learning-based Distributed Detection with Energy Harvesting
复制标题
基于学习的分布式能量收集检测
DOI：
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发表时间：
2021
期刊：
影响因子：
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通讯作者：
A. Vosoughi
中科院分区：
文献类型：
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作者：
Ghazaleh Ardeshiri;A. Vosoughi
我们考虑一个无线网络，由几个传感器和一个融合中心（FC），这是解决二进制分布式检测问题的任务。每个传感器都能够收集随机到达的能量并将其存储在有限大小的电池中。将信道衰落过程建模为时间齐次有限状态马尔可夫链，并假设每个传感器都知道其当前电池状态和通过有限反馈从FC获得的量化信道状态信息（CSI），我们的目标是找到最优的发射功率控制策略，使得感兴趣的检测性能指标最大化。我们将手头的问题表示为有限时间段马尔可夫决策过程（MDP）问题，并通过有限时间段动态规划获得最优策略。我们的模拟表明，所提出的政策优于贪婪的政策，其中每个传感器使用其所有可用的能量进行传输。
We consider a wireless network, consisting of several sensors and a fusion center (FC), that is tasked with solving a binary distributed detection problem. Each sensor is capable of harvesting randomly arrived energy and storing it in a finite-size battery. Modeling the channel fading process as a time-homogeneous finite-state Markov chain and assuming that each sensor knows its current battery state and its quantized channel state information (CSI) obtained by a limited feedback from the FC, our goal is to find the optimal transmit power control policy such that the detection performance metric of interest is maximized. We formulate the problem at hand as a finite-horizon Markov decision process (MDP) problem and obtain the optimal policy via finite-horizon dynamic programming. Our simulations demonstrate that the proposed policy outperforms Greedy-based policy, in which each sensor uses all its available energy for transmission.