Quantum Machine Learning for Sustainable Aerial RIS-ISAC Networks: Minimizing Electromagnetic Exposure through Intelligent System Design

This research project explores the use of quantum machine learning (QML) to optimize aerial reconfigurable intelligent surface (RIS)-assisted networks that integrate sensing and communication (ISAC). The goal is to enhance the sustainability and efficiency of these advanced wireless systems by intelligently minimizing electromagnetic field (EMF) exposure. By leveraging quantum-enhanced algorithms, the project aims to design next-generation aerial ISAC networks that are both environmentally conscious and technically robust, contributing to the development of safer and more adaptive wireless infrastructures.

Faculty Supervisor:

Georges Kaddoum

Student:

Partner:

University of Surrey (UK)

Discipline:

Engineering

Sector:

Quantum Science; Information and Communications Technology; Sustainability & the Environment

University:

École de technologie supérieure

Program: