  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.
