An Interpretable Neural Network for Configuring Programmable Wireless Environments
This work addresses the challenge of optimizing wireless communication in controlled environments, representing an incremental improvement through a novel application of neural networks to SDM configuration.
The paper tackled the problem of configuring programmable wireless environments (PWEs) using software-defined metasurfaces (SDMs) by modeling wireless propagation as an interpretable neural network, resulting in a system that learns propagation basics and configures SDMs to facilitate user communication after training.
Software-defined metasurfaces (SDMs) comprise a dense topology of basic elements called meta-atoms, exerting the highest degree of control over surface currents among intelligent panel technologies. As such, they can transform impinging electromagnetic (EM) waves in complex ways, modifying their direction, power, frequency spectrum, polarity and phase. A well-defined software interface allows for applying such functionalities to waves and inter-networking SDMs, while abstracting the underlying physics. A network of SDMs deployed over objects within an area, such as a floorplan walls, creates programmable wireless environments (PWEs) with fully customizable propagation of waves within them. This work studies the use of machine learning for configuring such environments to the benefit of users within. The methodology consists of modeling wireless propagation as a custom, interpretable, back-propagating neural network, with SDM elements as nodes and their cross-interactions as links. Following a training period the network learns the propagation basics of SDMs and configures them to facilitate the communication of users within their vicinity.