An Interpretable Neural Network for Configuring Programmable Wireless Environments

By C. Liaskos, A. Tsioliaridou, Shuai Nie, Andreas Pitsillides, S. Ioannidis, Ian F. Akyildiz. 2019

63 citations recorded by the source corpus.

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.

Subfields: Electrical and Electronic Engineering

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Electrical and Electronic Engineering

Citations in the corpus

A Survey on Explainable Artificial Intelligence (XAI): Toward Medical XAI (2019, methodology): “Neural network has been used for programmable wireless environments (PWE) [109].”
Smart Radio Environments Empowered by Reconfigurable Intelligent Surfaces: How it Works, State of Research, and Road Ahead (2020, background): “In [266], the authors introduce a neural-network-based approach for configuring the behavior of tiles in RIS-based environments.”
Toward Smart Wireless Communications via Intelligent Reflecting Surfaces: A Contemporary Survey (2019, background): “The authors in [179] construct an inter-pretable neural network for learning the optimal conﬁguration of reconﬁgurable reﬂecting elements, which are modeled as the neural nodes in different layers of a back-propagating neural network.”
Smart Radio Environments Empowered by Reconfigurable Intelligent Surfaces: How It Works, State of Research, and The Road Ahead (2020, background): “In [268], the authors introduce a neural-network-based approach for configuring the behavior of tiles in RIS-based”
Weighted Sum-Rate Maximization for Reconfigurable Intelligent Surface Aided Wireless Networks (2019, methodology): “The algorithms proposed for this setup may serve as a benchmark to study the ultimate performance of the system, as well as providing training labels for the machine learning based designs [41], [42].”