Radio Frequency Energy Harvesting (RF-EH) networks are key enablers of massive Internet-of-things by providing controllable and long-distance energy transfer to energy-limited devices. Relays, helping either energy or information transfer, have been demonstrated to significantly improve the performance of these networks. This paper studies the joint relay selection, scheduling, and power control problem in multiple-source-multiple-relay RF-EH networks under nonlinear EH conditions. We first obtain the optimal solution to the scheduling and power control problem for the given relay selection. Then, the relay selection problem is formulated as a classification problem, for which two convolutional neural network (CNN) based architectures are proposed. While the first architecture employs conventional 2D convolution blocks and benefits from skip connections between layers; the second architecture replaces them with inception blocks, to decrease trainable parameter size without sacrificing accuracy for memory-constrained applications. To decrease the runtime complexity further, teacher-student learning is employed such that the teacher network is larger, and the student is a smaller size CNN-based architecture distilling the teacher's knowledge. A novel dichotomous search-based algorithm is employed to determine the best architecture for the student network. Our simulation results demonstrate that the proposed solutions provide lower complexity than the state-of-art iterative approaches without compromising optimality.

翻译：射频能量采集网络通过为能量受限设备提供可控且远距离的能量传输，成为海量物联网的关键使能技术。中继通过辅助能量或信息传输，已被证明能显著提升此类网络性能。本文研究多源多中继射频能量采集网络中，在非线性能量采集条件下的联合中继选择、调度与功率控制问题。针对给定中继选择方案，我们首先获得调度与功率控制问题的最优解。随后将中继选择问题转化为分类问题，并提出了两种基于卷积神经网络（CNN）的架构：第一种架构采用传统二维卷积块并利用层间跳跃连接；第二种架构以Inception模块替代前述组件，在内存受限应用中通过减少可训练参数规模而不牺牲精度。为进一步降低运行时复杂度，采用师生学习机制，其中教师网络规模较大，学生网络为更小型的CNN架构以蒸馏教师知识。通过基于二分搜索的新型算法确定学生网络的最优架构。仿真结果表明，所提方案在不损失最优性的前提下，比现有迭代方法的复杂度更低。