Multiagent Reinforcement Learning based Energy Beamforming Control

TL;DR

This paper introduces a multiagent reinforcement learning approach for energy beamforming control in wireless power transfer, enabling decentralized decision-making and reducing feedback overhead.

Contribution

It proposes a novel MARL formulation for codebook-based beamforming, facilitating fully local control in wireless power transfer networks.

Findings

Reduces feedback and computation overhead in energy beamforming.

Enables decentralized, local control of beamforming.

Lays groundwork for fully autonomous beam control algorithms.

Abstract

Ultra low power devices make far-field wireless power transfer a viable option for energy delivery despite the exponential attenuation. Electromagnetic beams are constructed from the stations such that wireless energy is directionally concentrated around the ultra low power devices. Energy beamforming faces different challenges compare to information beamforming due to the lack of feedback on channel state. Various methods have been proposed such as one-bit channel feedback to enhance energy beamforming capacity, yet it still has considerable computation overhead and need to be computed centrally. Valuable resources and time is wasted on transfering control information back and forth. In this paper, we propose a novel multiagent reinforcement learning(MARL) formulation for codebook based beamforming control. It takes advantage of the inherienntly distributed structure in a wirelessly powered network and lay the ground work for fully locally computed beam control algorithms. Source code can be found at https://github.com/BaiLiping/WirelessPowerTransfer.