Deep Reinforcement Learning for Multi-User RF Charging with Non-linear Energy Harvesters

TL;DR

This paper develops a deep reinforcement learning approach for multi-user RF wireless power transfer, optimizing precoding to efficiently charge multiple non-linear energy harvesters in dynamic IoT environments.

Contribution

It introduces a novel DDPG-based method for joint beamforming and scheduling in multi-antenna RF-WPT systems with non-linear EH nodes, achieving near-optimal performance with low complexity.

Findings

Proposed beamforming achieves near-optimal energy transfer.

DDPG-based scheduling reduces power consumption over episodes.

System outage probability increases with more devices.

Abstract

Radio frequency (RF) wireless power transfer (WPT) is a promising technology for sustainable support of massive Internet of Things (IoT). However, RF-WPT systems are characterized by low efficiency due to channel attenuation, which can be mitigated by precoders that adjust the transmission directivity. This work considers a multi-antenna RF-WPT system with multiple non-linear energy harvesting (EH) nodes with energy demands changing over discrete time slots. This leads to the charging scheduling problem, which involves choosing the precoders at each slot to minimize the total energy consumption and meet the EH requirements. We model the problem as a Markov decision process and propose a solution relying on a low-complexity beamforming and deep deterministic policy gradient (DDPG). The results show that the proposed beamforming achieves near-optimal performance with low computational complexity, and the DDPG-based approach converges with the number of episodes and reduces the system's power consumption, while the outage probability and the power consumption increase with the number of devices.