In this paper, we present a novel learning-aided energy management scheme ($\mathtt {LEM}$LEM) for multihop energy harvesting networks. Different from prior works on this problem, our algorithm explicitly incorporates information learning into system control via a step called perturbed dual learning. $\mathtt {LEM}$LEM does not require any statistical information of the system dynamics for implementation, and efficiently resolves the challenging energy outage problem. We show that $\mathtt {LEM}$LEM achieves the near-optimal $[O(\epsilon), O(\log (1/\epsilon)^2)]$[O(ε),O(log(1/ε)2)] utility-delay tradeoff with an $O(1/\epsilon ^{1-c/2})$O(1/ε1-c/2) energy buffers ($c\in (0,1)$c∈(0,1)). More interestingly, $\mathtt {LEM}$LEM possesses a convergence time of $O(1/\epsilon ^{1-c/2} +1/\epsilon ^c)$O(1/ε1-c/2+1/εc), which is much faster than the $\Theta (1/\epsilon)$Θ(1/ε) time of pure queue-based techniques or the $\Theta (1/\epsilon ^2)$Θ(1/ε2) time of approaches that rely purely on learning the system statistics. This fast convergence property makes $\mathtt {LEM}$LEM more adaptive and efficient in resource allocation in dynamic environments. The design and analysis of $\mathtt {LEM}$LEM demonstrate how system control algorithms can be augmented by learning and what the benefits are. The methodology and algorithm can also be applied to similar problems, e.g., processing networks, where nodes require nonzero contents to support their actions.

中文翻译：

---

能量收集网络中的快速收敛学习辅助控制

在本文中，我们提出了一种新颖的学习辅助能源管理方案（$\mathtt {LEM}$莱姆) 用于多跳能量收集网络。与之前在这个问题上的工作不同，我们的算法通过称为扰动对偶学习. $\mathtt {LEM}$莱姆不需要任何系统动力学的统计信息来执行，并有效地解决了具有挑战性的能源中断问题。我们证明$\mathtt {LEM}$莱姆 达到接近最优 $[O(\epsilon), O(\log (1/\epsilon)^2)]$[哦(ε),哦(日志(1/ε)2)] 效用延迟权衡 $O(1/\epsilon ^{1-c/2})$哦(1/ε1——C/2) 能量缓冲（$c\in (0,1)$C∈(0,1)）。更有趣的是，$\mathtt {LEM}$莱姆 拥有一个 收敛时间 的 $O(1/\epsilon ^{1-c/2} +1/\epsilon ^c)$哦(1/ε1——C/2+1/εC)，这比 $\Theta (1/\epsilon)$Θ(1/ε) 纯基于队列的技术的时间或 $\Theta (1/\epsilon ^2)$Θ(1/ε2)完全依赖于学习系统统计数据的方法的时间。这种快速收敛的特性使得$\mathtt {LEM}$莱姆动态环境中资源分配的适应性和效率更高。设计与分析$\mathtt {LEM}$莱姆演示如何通过学习来增强系统控制算法以及好处是什么。该方法和算法也可以应用于类似的问题，例如处理网络，其中节点需要非零内容来支持它们的动作。