Radio Frequency Energy Harvesting (RF-EH) networks are key enablers of massive Internet-ofthings 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-constraint 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 searchbased 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.

We consider a wireless powered, harvest-thentransmit communication network, which consists of a single antenna, energy and information access point (AP) and multiple, single antenna, batteryless users with energy harvesting capabilities. At the beginning of a time frame, the AP broadcasts energy in the downlink to the users. Then, users transmit their data to the AP in the uplink, using their harvested energy. We formulate the optimization problem with the objective of minimizing the total schedule length, subject to the constraints on the minimum amount of data to be sent to the AP, and unlike previous studies, the maximum transmit power for the information transmission. This problem is nonlinear and non-convex. The solution is based on bi-level optimization, consisting of optimizing the transmit power allocation of the nodes for a given energy harvesting time and searching over harvesting time allocation. We also propose a heuristic algorithm in which we incorporate the optimal solution of a single user network. Simulation results demonstrate that under appropriate network conditions, our proposed algorithms provide close-to-optimal results with a reasonable run time compared to another time minimization algorithm which does not integrate the uplink power constraint.

We consider a wireless powered, harvestthen-transmit communication network, which consists of multiple, single antenna, energy and information access points (APs) and multiple, single antenna users with energy harvesting capabilities and rechargeable batteries, and allows simultaneous information transmission. We formulate the joint power control and scheduling problem with the objective of minimizing the total schedule length, subject to the constraints on the minimum amount of data to be sent by the users to the APs, and the maximum transmit power for the information transmission. This problem is a nonlinear and non-convex, mixed integer programming problem for which there is no known polynomial time algorithm. The proposed heuristic algorithm is based on, first, finding the solution for a fixed energy harvesting time and then searching for the optimal energy harvesting time that minimizes the total schedule length. For the former, a scheduling problem is formulated as an integer programming problem, which we solve with Branch and Price based methods upon solving the power control problem separately. Simulation results demonstrate that the proposed algorithm outperforms previously proposed time minimization algorithms that do not consider simultaneous transmission scenarios up to 3.5% for larger AP power, 25.4% for tighter maximum transmit power limit, and 6.5% for greater number of users per AP.

In this paper, we consider a wireless powered communication network where multiple users with RF energy harvesting capabilities communicate to a hybrid energy and information access point (HAP) in full-duplex mode. Each user has to transmit a certain amount of data with a transmission rate from a finite set of discrete rate levels, using the energy initially available in its battery and the energy it can harvest until the end of its transmission. Considering this model, we propose a novel discrete rate based minimum length scheduling problem to determine the optimal power control, rate adaptation and transmission schedule subject to data, energy causality and maximum transmit power constraints. The proposed optimization problem is proven to be NP-hard which requires exponential-time algorithms to solve for the global optimum. As a solution strategy, first, we demonstrate that the power control and rate adaptation, and scheduling problems can be solved separately in the optimal solution. For the power control and rate adaptation problem, we derive the optimal solution based on the proposed minimum length scheduling slot definition. For the scheduling, we classify the problem based on the distribution of minimum length scheduling slots of the users over time. For the non-overlapping slots scenario, we present the optimal scheduling algorithm. For the overlapping scenario, we propose a polynomial-time heuristic scheduling algorithm.

Relay nodes are used to improve the throughput, delay and reliability performance of energy harvesting networks by assisting both energy and information transfer between information nodes and access point. Previous studies on radio frequency energy harvesting networks are limited to single source single/multiple relay networks. In this paper, a novel joint relay selection, scheduling and power control problem for multiple source multiple relay network is formulated with the objective of minimizing the total duration of wireless power and information transfer. The formulated problem is non-convex mixed-integer non-linear programming problem, and proven to be NP-hard. We first formulate a subproblem on scheduling and power control for a given relay selection. We propose an efficient optimal algorithm based on a bi-level optimization over power transfer time allocation. Then, for optimal relay selection, we present optimal exponential-time Branch-and-Bound (BB) based algorithm where the nodes are pruned with problem specific lower and upper bounds. We also provide two BB-based heuristic approaches limiting the number of branches generated from a BB-node, and a relay criterion based lower complexity heuristic algorithm. The performance of the proposed algorithms are demonstrated to outperform conventional harvest-then-cooperate approaches with up to 88% lower schedule length for various network settings.