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.

射频能量收集（RF-EH）网络是大规模物联网的关键推动因素，通过为能量受限设备提供可控和长距离的能量传输来实现。本研究主要探讨多源多中继射频能量收集网络中非线性能量收集情况下的联合中继选择、调度和功率控制问题。首先，在给定中继选择情况下求解调度和功率控制问题的最优解。然后，将中继选择问题表述为一个分类问题，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的 2D 卷积块，并受益于层之间的跳连连接；第二种架构将其替换为启示块，以减小具有内存限制应用的可训练参数的大小，而不损失准确性。为进一步降低运行时复杂性，采用师生学习，其中师网络更大，学生网络是基于较小尺寸 CNN 的架构，提取师网络的知识。采用一种新的二分搜索算法来确定学生网络的最佳架构。我们的模拟结果表明，所提出的解决方案提供了比最先进的迭代方法更低的复杂性，而不会损害最优性。