An Interpretable Neural Network for Configuring Programmable Wireless Environments

no code implementations • 7 May 2019 • Christos Liaskos, Ageliki Tsioliaridou, Shuai Nie, Andreas Pitsillides, Sotiris Ioannidis, Ian Akyildiz

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.

Graphene Hypersurface for Manipulation of THz Waves

no code implementations • 3 May 2020 • Sasmita Dash, Christos Liaskos, Ian F. Akyildiz, Andreas Pitsillides

The proposed graphene HSF has active control over absorption, reflection, and transmission of THz waves.

Radiation pattern prediction for Metasurfaces: A Neural Network based approach

no code implementations • 15 Jul 2020 • Hamidreza Taghvaee, Akshay Jain, Xavier Timoneda, Christos Liaskos, Sergi Abadal, Eduard Alarcón, Albert Cabellos-Aparicio

Concretely, we show that this method is able to learn and predict the parameters governing the reflected wave radiation pattern with an accuracy of a full wave simulation (98. 8%-99. 8%) and the time and computational complexity of an analytical model.

End-to-end Wireless Path Deployment with Intelligent Surfaces Using Interpretable Neural Networks

no code implementations • 16 Oct 2020 • Christos Liaskos, Shuai Niez, Ageliki Tsioliaridou, Andreas Pitsillides, Sotiris Ioannidis, Ian F. Akyildiz

A feedforward/backpropagate process optimizes these weights to match ideal propagation outcomes (normalized network power outputs) to wireless user emissions (normalized network power inputs).