Deep learning based minimum length scheduling for half duplex wireless powered communication networks

Abstract

Minimum length scheduling is used to ensure the strict delay requirements of time-critical applications in wireless powered communications networks (WPCNs). The previous optimal and sub-optimal solutions of the problem suffer from the run-time complexity of the iterative algorithms, which makes real-time applications unpractical. This paper proposes a deep learning based framework for a low-complexity solution to the minimum length scheduling problem in half-duplex WPCNs. The objective of the problem is to minimize the duration of the schedule for energy harvesting (EH) and information transmission (IT), subject to the data demand, energy causality, and maximum transmit power constraints. Multi-input multi-output feed-forward deep neural network (DNN) architecture is considered, where the inputs are channel state information and two parameters derived from the optimality conditions of the problem; and outputs are the transmit powers, EH and IT lengths. To ensure the feasibility of the DNN outputs, we design a final layer which maps the estimated transmit powers to the feasible EH and IT lengths. The DNN is trained offline with both supervised and unsupervised techniques. Simulation results indicate that the proposed DNN-based approaches are up to 8.5 times faster than the benchmark iterative algorithms. These approaches also outperform benchmark sub-optimal algorithms in terms of accuracy with only 0.12% optimality gap and robustness against varying network conditions.