Optimization Theory and Deep Learning Based Resource Allocation in Net-Zero-Energy Networks With Short Packets

Abstract

Net-zero-energy networks enable IoT applications by balancing harvested and consumed energy when the connection to the electric grid or changing batteries is not feasible for the information sources. This letter studies the optimization problem for minimizing the schedule length for net-zero-energy networks with short packets where the schedule length is defined as the total time duration required for the RF energy harvesting (EH) in the downlink and information transmission by exhausting the harvested energy in the uplink. The problem is nonlinear and non-convex, so hard to solve. To obtain a near-optimal solution, a bi-level optimization-based framework is proposed with the master problem searching for optimal EH duration iteratively and subproblems of calculating the schedule length for a given EH time in each iteration. Then, we propose a low complexity optimization theory based deep learning framework based on the simplification of the deep learning architecture by using optimality conditions. The proposed approaches outperform state-of-art algorithms in terms of schedule length. The optimization theory based deep learning approach further decreases the complexity.