# An Interpretable Neural Network for Configuring Programmable Wireless Environments

## TL;DR

This paper introduces an interpretable neural network model that uses machine learning to configure programmable wireless environments created by software-defined metasurfaces, optimizing wireless communication.

## Contribution

It presents a novel neural network approach that models SDM-based wireless environments for effective configuration and improved communication performance.

## Key findings

- The neural network successfully learns propagation characteristics of SDMs.
- Configuration of SDMs enhances wireless communication within the environment.
- The approach provides an interpretable model for SDM configuration.

## Abstract

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.